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The New Rules of Retail Resilience: Balancing Tech, Trust, and Turbulence

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Retail has always been sensitive to economic shifts, but today’s challenges are amplified by forces far beyond the control of retailers. Geo-political tensions are reshaping trade routes, sanctions and conflicts are disrupting access to raw materials, and extreme weather events are destabilizing global supply hubs. On top of this, inflation continues to squeeze household budgets, labor costs remain high, and consumers expect seamless, personalized shopping experiences across channels.

This mix of geo-political volatility, economic pressure, and rising customer expectations means resilience can’t just be about trimming costs. True resilience is about agility.  It is about the ability to anticipate change, adjust quickly, and protect both margins and customer trust.

Technology, especially artificial intelligence (AI) combined with enablers like IoT and blockchain, is giving retailers the tools to achieve this. Below are six areas where retailers are applying tech-enabled agility to build resilience.


1. Predictive Analytics and Demand Forecasting

Predictive Analytics and Demand Forecasting

Customer demand in the current times is more unpredictable than ever before. In today’s world dominated by social media, trends are quick to spiral into a craze in no time.  Products can go viral overnight and fade just as fast. Traditional forecasting can’t keep up.

AI-driven predictive analytics continuously analyzes data, from sales and promotions to weather and local events, allowing forecasts to adjust in near real time.

For example,

  • Rue La La, built ML models to forecast demand for new product styles, boosting revenue by nearly 10% without reducing sell-through.
  • Target used predictive demand models to cut out-of-stock rates by about 21% and reduce excess inventory costs by about 15%.

The payoff: Better-matched inventory, fewer lost sales, and meaningful margin protection.

2. AI-Driven Price Optimization in Retail

AI-Driven Price Optimization in Retail

Shoppers under financial pressure scrutinize every purchase. Blanket discounts may drive volume but destroy profitability.

AI-powered tools analyze elasticity, competitor pricing, and historical sales to optimize pricing at the product, region, or store level.  Amazon is the poster child here.  Its AI reprices millions of items daily, balancing competitiveness and margin. Others, like grocery chains, maintain steady pricing on essentials while using targeted offers to attract footfall.

The result: Sharper pricing strategies, more predictable promotional outcomes, and healthier margins.

3. Smarter Supply Chains with IoT, Blockchain, and Diversification

Smarter Supply Chains with IoT, Blockchain, and Diversification

Global events continue to expose supply chain fragility. Transparency and responsiveness are critical.

  • IoT sensors track shipments and conditions in real time.
  • Blockchain creates tamper-proof records of sourcing and movement.
  • Multi-shoring strategies reduce reliance on single geographies.

Zara built a reputation for speed, turning new designs into store-ready stock in weeks. IKEA, rerouted goods via rail when European ports were congested, keeping supply chains moving despite geo-political shifts.

The lesson:  Resilience comes from both visibility and flexibility.

4. Omnichannel Inventory Visibility

Omnichannel Inventory Visibility

Customers expect inventory accuracy across channels: browse online, buy in-store, or ship from store.  Accuracy across channels therefore is non-negotiable.

AI and RFID give retailers item-level visibility, keeping systems synchronized. Sephora links its app to real-time store inventory, letting customers reserve products online with confidence.

The bigger story here is customer trust. Few things damage loyalty faster than a shopper arriving for an item that was shown “in stock” online but isn’t on the shelf. Retailers that invest in omnichannel inventory accuracy are not just improving operations; they’re delivering on a promise of reliability that builds long-term relationships.

The insight:  When customers believe availability promises, loyalty grows.

5. Automated Workforce Management

Automated Workforce Management

Labor is one of retail’s biggest costs and pain point. Seasonal peaks, unpredictable traffic, and rising wages complicate scheduling.

AI-based workforce tools analyze sales history, promotions, and even weather to create smarter rosters. Starbucks aligns barista shifts with local events and weather patterns, balancing service quality and cost efficiency.

The upside: Happier customers, lower labor waste, and higher staff retention thanks to more predictable schedules.

6. Real-Time Performance Dashboards

Real-Time Performance Dashboards

Retail moves too quickly for weekly or monthly reporting. Leaders need to act in the moment.

AI-powered dashboards bring together live data on sales, supply chain, and staffing. Target uses real-time dashboards to adjust promotions and inventory the same day, not weeks later.

The real power:  Lies in linking outcomes to root causes: a dip in sales tied to a competitor promotion or staffing gap can be corrected immediately.

Resilience, a Retail Advantage

Together, these six areas show that retail resilience doesn’t require sweeping reinvention. It comes from focused, tech-enabled improvements across demand, pricing, supply chain, inventory, labor, and decision-making.

Technology provides the eyes and intelligence, while strategies like supplier diversification or contingency planning provide the muscle to pivot.

Most importantly, resilience is no longer just a survival tactic. It’s a competitive differentiator. Retailers that can adapt quickly to geo-political shifts, economic shocks, or sudden demand spikes not only protect margins but also strengthen their position with consumers and suppliers. In a world where uncertainty is the only constant, resilience separates those who merely cope from those who lead.

Experiential Retail Trends: How Immersive Store Experiences Are Redefining In-Store Shopping

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The role of the physical store has changed. With e-commerce delivering unmatched convenience, retailers can no longer compete on speed or price alone. Brick-and-mortar retailers are now focusing on creating engaging, differentiated experiences that can’t be replicated online. This shift is at the core of experiential retail, a growing strategy designed to attract, retain, and meaningfully engage customers.

In this blog, let’s look at the drivers behind experiential retail, examine how global and regional brands are implementing immersive strategies, break down the key elements of successful experiences, and explore the measurable business impact.

What is Experiential Retail?

In 2025, immersive in-store experiences are no longer “add-ons” for premium brands. They’re becoming table stakes for retailers who want to drive loyalty, foot traffic, and brand differentiation. The store is evolving from a point of sale into a destination where consumers can explore, connect, and interact with both products and the brand. These experiences rely on technology, personalization, community engagement, and purposeful design to foster long-term relationships with shoppers.

Why Experiential Retail Matters

  • Consumer preferences are changing: Recent research shows that Gen Z and millennial shoppers increasingly prioritize interactive, educational in-store experiences over conventional retail environments.
  • Digital and physical are blending: Consumers now move seamlessly between physical and digital touchpoints, often blending online research with in-store visits to complete their shopping journey.
  • In-store differentiation is critical: Immersive formats such as events, try-ons, and personalized services help physical stores stay competitive against e-commerce alternatives.

What’s Driving the Shift?

Changing consumer expectations

Today’s shoppers are more informed and selective than ever. They routinely rely on online research, peer reviews, and price comparisons before making in-store purchases. But beyond convenience and product variety, what they truly seek are meaningful, personalized experiences. For younger generations in particular, the shopping journey is not just about buying, it’s about enjoying a seamless, engaging, and memorable interaction with the brand.

Omnichannel integration

The lines between digital and physical retail continue to blur. Shoppers now expect a consistent and connected experience, whether they begin their journey online or in-store. To meet these expectations, retailers are using technology like mobile apps, virtual product previews, and intelligent support systems to create a seamless flow across all touchpoints.

Technology-led transformation

Technology is reshaping the in-store experience in dynamic ways. Innovations such as smart fitting rooms, augmented reality, and generative AI are making shopping more interactive and tailored. At the same time, connected devices are enabling retailers to gather real-time insights, allowing them to fine-tune everything from product displays to inventory management.

Key Elements of Successful Experiential Retail

Product interaction

  • In-store demos and AR tools allow customers to try before they buy.
  • Brands like Nike have introduced 3D sneaker customization and AR tools that allow visitors to digitally try on and personalize footwear.

Events and community

  • Experiences like makeup tutorials, product launches, and pop-ups build a sense of belonging.
  • Foot Locker’s “Sneaker Hub” in select US locations merges cultural events with shopping, encouraging community visits and brand loyalty.

Personalization

  • According to Salesforce, 73% of consumers expect companies to understand their needs and preferences.
  • Personalized recommendations, birthday offers, and behavior-based discounts are now becoming standard features in successful retail formats.

Convenience through technology

Modern shoppers value speed and simplicity. Retailers are embracing tools like contactless checkout, self-service kiosks, and mobile app support to make the in-store experience faster and more efficient. These technologies not only reduce friction but also allow customers to shop on their own terms, with minimal wait times and greater control.

Sustainability and ethics

Shoppers today are increasingly mindful of the impact their purchases have on the planet. As a result, many retailers are prioritizing sustainability by using eco-friendly materials, offering recycling programs, and designing energy-efficient store environments. Ethical practices and transparency are becoming key factors in building trust and long-term brand loyalty.

Regional and Global Examples

Brand/Region Experiential tactic Impact
Nike (global) AR customization, digital try-ons Boost in loyalty and user-generated content
Foot Locker (US) Try-on hubs, exclusive events Higher store traffic and repeat visits
Sephora (global) In-store beauty AR, educational workshops Improved conversion and longer dwell times
Dubai malls Pop-ups, immersive tech installations Increased tourist footfall and social sharing

Challenges in Implementation

  • Balancing automation and human interaction: While technology enhances convenience, too much automation can lead to impersonal experiences. Successful retailers strike a balance by ensuring knowledgeable staff are available to add a human touch where it matters most.
  • High upfront costs: Building immersive, experience-driven store formats often involves considerable investment in technology, space design, and training. Retailers must carefully plan and prioritize these efforts to ensure long-term value.
  • Data privacy concerns: Personalized shopping relies heavily on customer data, making data protection a critical responsibility. Retailers need to maintain strong privacy practices and cybersecurity measures to protect consumer trust.

The Value of Experiential Retail

  • Increased foot traffic: Memorable in-store moments bring customers back and encourage word-of-mouth promotion.
  • Deeper engagement: Shoppers engage longer and more meaningfully with products, increasing average basket sizes.
  • Brand differentiation: Experiential tactics help retailers stand out in saturated markets, especially when aligned with local tastes and culture.

What’s Next?

  • Modular store formats: Smaller, agile stores that serve multiple purposes from retail to community events will continue to rise.
  • Hyper-personalization through AI: AI will fine-tune everything from product recommendations to store layouts, based on individual shopper behavior.
  • Sustainable innovation: From zero-waste packaging to renewable energy in-store, sustainability will remain a competitive differentiator.

Experiential retail is fast becoming an expectation. As retailers focus on delivering immersive, tech-enabled, and values-driven experiences, they are reshaping the role of the physical store. Brands that invest in purposeful innovation and stay aligned with customer needs will lead the future of in-store shopping.

Want to explore how InfoVision can help reimagine your in-store experience? Connect with us at digital@infovision.com. You can also download our whitepaper to dive deeper into experiential retail strategies and global case studies.

Agentic AI: The Future of Business Is Autonomous

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As an AI-first company, contributing to the conversation on Agentic AI, a topic gaining strong momentum among technology and business leaders, was a natural step.  Abhilash Vantaram, our VP and Head of Emerging Technologies, has captured this pivotal shift in his white paper titled “The Agentic Shift: Redefining Business with Autonomous AI.”

In this blog, I share a preview of our ‘in demand’ white paper that explores how enterprises can move from assistance to autonomy and turn intelligent systems into proactive business partners.

As a marketing leader working closely with our innovation and technology teams, I often get a front-row view of transformative trends before they hit the mainstream. One such shift that’s gathering momentum across industries is ‘Agentic AI.’

Imagine an AI system that doesn’t just respond to commands — but plans, decides, and acts on your behalf. From real-time diagnostics in healthcare to personalized recommendations in retail, intelligent agents are already transforming industries.

This is the era of Agentic AI, where automation is steadily moving towards autonomy.

From Assistants to Agents: A Paradigm Shift in AI

Artificial Intelligence has gone through several evolutionary waves. First came rule-based systems – basic if-then logic that could execute predefined tasks. Then, predictive machine learning models enabled pattern recognition and forecasting. More recently, generative AI (GenAI) introduced natural language interfaces, allowing AI to create content, summarize documents, and converse like humans.

But, if you come to think of it, even GenAI  for all its sophistication remains reactive. It needs a prompt. It waits for instructions. Enter Agentic AI: systems that operate independently, understand context, and take initiative.

These are not just smart tools. They are goal-driven digital collaborators, capable of engaging with environments, orchestrating tasks, and continuously improving themselves with minimal human input.

Why Now? The Business Case for Agentic AI

Enterprises today face mounting pressure to do more with less. They’re expected to deliver faster services, personalized experiences, and smarter operations while reducing costs and improving agility. Agentic AI offers a powerful solution to

  • Enhance productivity by automating multi-step, cross-functional workflows
  • Improve decision-making through contextual intelligence and feedback loops
  • Boost customer satisfaction by anticipating needs and acting proactively
  • Create new digital revenue streams through intelligent personalization and automation

According to recent industry reports, 82% of organizations plan to adopt Agentic AI frameworks in the next 1–3 years. Those who act early will define the next competitive curve.

What Makes Agentic AI Different?

While GenAI focuses on content generation, Agentic AI is about action. It brings together multiple layers of intelligence:

  • LLMs (Large Language Models)

    Understand and reason using human-like language, enabling rich natural interactions.

  • Memory

    Retain long-term and session-based context, allowing agents to learn from previous interactions and personalize future ones.

  • Planning Modules

    Break down user goals into executable steps, prioritizing tasks and sequencing actions based on logic and intent.

  • Tool Integration

    Connect with external applications and APIs to actually perform tasks, such as triggering workflows, fetching data, or updating records.

  • Multi-modal Input

    Interpret voice, text, images, and video, allowing seamless omnichannel experiences.

  • Feedback Loops

    Learn through feedback loops and correction to improve performance, reduce errors, and increase reliability over time.

GenAI vs. Agentic AI: A Quick Comparison

Regional and Global Examples

Capability GenAI Agentic AI
Requires prompts Yes No
Takes action No Yes
Understands context Partially Deep context awareness
Personalization Basic Dynamic and learning-based
Goal-driven No Yes

Across Industries: Real-World Impact

Agentic AI isn’t just in books. It’s already making an impact across industries:

Retail

A global beauty retailer uses intelligent agents to recommend personalized skincare routines. By combining historical data, live preferences, and real-time stock levels, the agent builds curated carts — increasing sales and reducing returns. The result: improved conversion and customer satisfaction.

Telecom

A leading North American telecom provider leverages Agentic AI to autonomously manage network bandwidth, flag anomalies, and resolve outages — all without human intervention. This has helped reduce downtime and operational costs significantly.

Healthcare

A prominent nonprofit medical system integrates multimodal AI agents to assist with diagnostics, document summarization, and care plan recommendations. This reduces administrative workload and allows healthcare professionals to focus more on patient care.

BFSI

A global investment bank has deployed Agentic AI to automate legal document review and compliance workflows. By analyzing contracts and flagging inconsistencies, the system has saved hundreds of thousands of hours annually.

Manufacturing & Cybersecurity

In factories, AI agents monitor machinery, predict breakdowns, and schedule maintenance proactively. In cybersecurity, agents triage alerts, investigate incidents, and trigger mitigation protocols, reducing analyst fatigue and improving response times.

Designing Agentic AI in Your Enterprise

Deploying Agentic AI is not just about adopting new tools. It’s about strategically designing autonomous systems that align with your business goals. At InfoVision, we follow a structured 5-stage model:

  1. Define

    Identify high-impact use cases where autonomy can add measurable value.

  2. Design

    Create an agent architecture — mapping workflows, choosing tools, and planning integrations.

  3. Deploy

    Run controlled pilots to validate functionality, track KPIs, and gather feedback.

  4. Develop

    Implement continuous learning loops, retraining models based on real-world performance.

  5. Govern

    Establish guardrails around ethics, compliance, and human oversight to ensure responsible AI behavior.

Challenges and Considerations

Despite its promise, Agentic AI comes with important challenges that organizations must address thoughtfully:

  • Ethical risk: Agents trained on biased or incomplete data can perpetuate discrimination. Transparency in design and explainable AI tools are essential for trust.
  • Oversight and control: While autonomy is the goal, it should never come at the cost of control. Organizations must define when human intervention is required and design effective escalation paths.
  • Security and compliance: Agents often interact with sensitive data and systems. Proper safeguards, audit trails, and access controls must be built into their design.
  • Workforce readiness: Employees may resist or misunderstand AI systems. Successful adoption depends on upskilling teams and fostering a culture of co-innovation.

Understanding and navigating these considerations early can ensure a safer and more scalable deployment of Agentic AI.

Leading the Agentic Shift

Agentic AI is more than a technology trend — it’s a foundational shift in how enterprises function. It redefines roles, processes, and the relationship between humans and machines.

The most successful leaders in this shift will be those who embrace orchestration over micromanagement, who use AI not as a substitute for people but as a force multiplier for innovation.

As intelligent systems take on more responsibility, human leaders must evolve – from managing tasks to architecting ecosystems of collaboration between people and AI agents.

The time to lead is now.

Partner with InfoVision

At InfoVision, we help enterprises design, build, and scale Agentic AI frameworks tailored to their strategic goals. Our phased approach ensures minimal disruption, ethical integration, and tangible business outcomes.

Read the full white paper

Let’s build intelligent systems that don’t just assist — they act.

Git and Jujutsu: The next evolution in version control systems

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Version control systems (VCS) are the silent enablers of every successful software delivery pipeline. For over 15 years, Git has reigned supreme as the de facto standard for tracking changes in code. However, as development workflows become increasingly complex, some limitations of Git have become apparent thus sparking interest in newer, more ergonomic alternatives.

Enter Jujutsu (often abbreviated as “jj”), a Git-compatible version control system designed to simplify common tasks, improve collaboration, and reduce the risk of costly errors.

This blog explores how Jujutsu compares to Git, why it’s gaining interest among forward-looking teams, and what makes it a strong contender in modern DevOps environments.

But first, let’s take a step back.

To understand Jujutsu’s value, it helps to revisit what makes Git so powerful and which are the areas where it falls short.

About Git

Git is a distributed version control system created by Linus Torvalds in 2005 for Linux kernel development. Today, it powers nearly every modern software project, with platforms like GitHub, GitLab, and Bitbucket built around it.

Key Git concepts

  • Distributed architecture: Each developer has a complete copy of the entire repository history.
  • Branching and merging: Lightweight branches enable parallel development and integration.
  • Staging area: Acts as an intermediate zone between working directory and commit history.
  • Commit hashes: Unique identifiers for each commit using SHA-1 hashing.
  • Remote repositories: Central repositories that facilitate collaboration.

While Git is powerful, its design has some notable drawbacks, especially as workflows scale in complexity. A steep learning curve, uncommitted changes, rebase complexity, history rewriting and merge conflicts are among the prominent challenges that developers encounter.

What is Jujutsu?

Jujutsu is a Git-compatible VCS designed to overcome Git’s shortcomings, particularly around user experience, mutability, and conflict resolution. It aims to make daily workflows simpler and less error-prone, while remaining compatible with existing Git tooling.

Most importantly, Jujutsu is not a replacement for Git but rather an alternative interface to Git repositories, allowing teams to adopt it incrementally without disrupting their existing workflows.

What Jujutsu primarily brings to the table is:

  • Git compatibility
  • Simplified mental model
  • Improved workflows
  • Flexible history editing
  • Better collaboration

Why Jujutsu?

While Git’s design is brilliant, it’s not always developer friendly. Its steep learning curve, verbose commands, and history manipulation quirks often lead to frustration, especially in fast-paced environments.

  • Git’s power often comes at the cost of usability. Developers frequently run into issues like: Cryptic command-line interface (CLI)
  • Confusing concepts like detached HEAD, index/staging area
  • Complex rebasing and merging mechanisms
  • Fragile conflict resolution process

Jujutsu was created by a Googler as an attempt to modernize version control. The philosophy behind Jujutsu is:

  • Make the user interface more intuitive
  • Allow safer, less error-prone versioning
  • Provide better conflict handling
  • Support mutable history for easier correction and clean-up

In short, Jujutsu is what Git could be if designed with today’s developer experience in mind – smarter, safer, and more human-centric.

Key features that set Jujutsu apart

  • Immutable history model:  Every operation creates a new commit, reducing risk and making history easier to understand.
  • Revsets: a powerful query language for selecting commits. This feature allows developers to perform complex operations on specific sets of commits easily.
    # Find all commits authored by Alice that touch Python files
    jj log -r “author(alice) & file(*.py)”
  • Working copy as a commit:  In Jujutsu, the working copy is treated as a special commit, allowing all operations that work on commits to also work on the working copy. This unifies the conceptual model and simplifies many workflows.
  • Multi-layered branches: Separates local, remote, and collaborative branches for clarity.
  • Operation log: Easy ‘undo’ actions and review workflow history.
  • Simplified Conflict Resolution:  Improved tools to manage and resolve complex conflicts including better visual indications and automatic resolutions.
  • Git-Compatible: Works seamlessly with existing Git repositories.

Git and Jujutsu: At a glance

FeatureGitJujutsu
Staging areaExplicit git add neededNo staging area; working copy is tracked directly
History editingMutableImmutable
Mutability of commitsImmutable; requires rebasingMutable; use jj amend, jj split, jj rebase
Rebase safetyRisk of data lossSafer due to snapshotting and mutable state
Working copySeparate from commit historyTreated as a commit
Branch ModelSingle-layer branchesMulti-layered branches
Conflict resolutionManual with limited tools. Must resolve immediately during mergeEnhanced automatic resolution. Conflicts live inside commits and can be resolved anytime
Operation historyLimited to reflogComprehensive operation log
Command structureInconsistent syntaxConsistent command patterns
Learning curveSteepModerate
CompatibilityNative GitGit-compatbale
Ecosystem integrationExtensiveGrowing through Git compatibility

Workflow comparisons

Let us compare some common Git workflows with their Jujutsu equivalents to highlight the differences and benefits.

Feature branch workflow

Git:

# Create and switch to a new feature branch
git checkout -b feature/new-feature

# Make changes and commit
git add .
git commit -m “Implement new feature”

# Push to remote
git push -u origin feature/new-feature
# Update with main branch changes

git checkout main
git pull
git checkout feature/new-feature
git rebase main

# Resolve conflicts if any
git add .
git rebase –continue

# Force push after rebase
git push –force-with-lease

Jujutsu:

[bash]
# Create a new change with description
jj new -m “Implement new feature”

# Create a branch pointing to this change
jj branch create feature/new-feature

# Make changes (they are automatically tracked)
# Update the current change description if needed
jj describe -m “Implement new feature – updated”

# Push to remote
jj git push –branch feature/new-feature

# Update with main branch changes
jj rebase -d main

# Resolve conflicts if any and continue
jj resolve

# Push updated branch
jj git push –branch feature/new-feature

Advantage Jujutsu:

  • No need to explicitly stage changes before committing
  • Rebasing is simpler and safer
  • No need for force pushes
  • Conflict resolution is more streamlined

Stashing workflow

Git:

[bash]
# Save uncommitted changes
git stash push -m “Work in progress”

# Switch branches
git checkout another-branch

# Do some work and commit
git add .
git commit -m “Work on another branch”

# Return to original branch
git checkout original-branch

# Apply stashed changes
git stash pop

Jujutsu:

[bash]
# Working copy is already a commit – create a new change
jj new -m “Work in progress”

# Switch to another branch
jj new main -m “Work on another branch”

# Do some work (changes are automatically tracked)

# Return to previous work
jj edit @–  # Edit the previous change

# Or checkout a specific change by ID
jj checkout <change-id>

Advantage Jujutsu:

  • No need for a separate stash mechanism
  • Working copy changes are always preserved
  • Easier to manage multiple sets of changes

History editing workflow

Git:

[bash]
# Interactive rebase to edit history
git rebase -i HEAD~3

# In the editor, mark commits to edit with ‘edit’

# For each commit marked as ‘edit’:
git commit –amend
git rebase –continue

# To reorder commits, change the order in the interactive editor

# Then continue the rebase
git rebase –continue

Jujutsu:

[bash]
# Edit a specific commit (3 commits back)
jj edit @—

# Make changes to files
# Update the commit
jj amend

# Return to the latest change
jj edit @

# Reorder commits using rebase
jj rebase -r @– -d @—  # Move second-to-last commit after third-to-last

Benefits of Jujutsu:

  • More intuitive history editing
  • Less manual intervention required
  • Safer operations with lower risk of mistakes

Code review workflow

Git:

[bash]
# Create a branch for review feedback
git checkout feature-branch
git checkout -b address-feedback

# Make changes based on feedback
git add .
git commit -m “Address feedback”

# Squash commits for clean history
git rebase -i HEAD~2
# In editor, change ‘pick’ to ‘squash’ for commits to combine

# Push updated branch
git push –force-with-lease origin feature-branch

Jujutsu:

[bash]
# Make changes based on review feedback
# (assuming you’re on the feature branch)
jj describe -m “Address review feedback”

# Make the changes to files
# Amend the current change
jj amend

# To squash with previous commit
jj squash

# Push updated branch
jj git push –branch feature-branch

Advantage Jujutsu:

  • Simpler commands for common operations
  • Better visualization of history changes
  • More intuitive squashing of commits

Cherry-picking workflow

Git:

[bash]
# Identify commit to cherry-pick
git log –oneline# Switch to target branch
git checkout target-branch

# Cherry-pick the commit
git cherry-pick abc123def

# If conflicts occur
git add .
git cherry-pick –continue

# Push changes
git push origin target-branch

Jujutsu:

[bash]
# Identify commit to cherry-pick
jj log# Switch to target branch
jj checkout target-branch

# Duplicate the commit (cherry-pick equivalent)
jj duplicate abc123def

# If conflicts occur, resolve them
jj resolve

# Push changes
jj git push –branch target-branch

Advantage Jujutsu:

  • More intuitive representation of cherry-picked commits
  • Better conflict resolution for cherry-picks
  • Explicit indication that a commit is cherry-picked

Bisecting Workflow

Git:

[bash]
# Start bisect process
git bisect start

# Mark current commit as bad
git bisect bad

# Mark a known good commit
git bisect good abc123def

# Git automatically checks out commits for testing
# After testing each commit:
git bisect good  

# if the commit is good
# or
git bisect bad   

# if the commit is bad

# After finding the faulty commit
git bisect reset

Jujutsu:

[bash]
# List commits in the range to bisect
jj log -r “abc123def..@”

# Create a new workspace for testing
jj workspace add ../bisect-workspace

# Switch to the workspace and test different commits
cd ../bisect-workspace
jj edit <commit-to-test>

# Test the commit, then try another
jj edit <another-commit-to-test>

# After finding the faulty commit, clean up
cd ../original-workspace
jj workspace forget ../bisect-workspace

Benefits of Jujutsu:

  • Multiple workspaces allow parallel testing
  • No need to constantly switch working copies
  • Better visualization of the bisect process

Collaborative Workflow with Multiple Contributors

Git:

[bash]
# Get latest changes from remote
git fetch origin

# See what others have done
git log –oneline origin/main..HEAD

# See incoming changes
git log –oneline HEAD..origin/main

# Integrate others’ changes
git rebase origin/main

# If conflicts occur during rebase
git add .
git rebase –continue

# Incorporate specific changes from a colleague
git cherry-pick <colleague-commit-hash>

# Push your changes
git push origin main

Jujutsu:

[bash]
# Get latest changes
jj git fetch# See what’s new on remote
jj log -r “remote_branches()”

# See your local changes vs remote
jj log -r “main..@”

# Integrate remote changes
jj rebase -d main@origin

# If conflicts occur
jj resolve

# Duplicate specific changes from a colleague
jj duplicate <colleague-commit-id>

# Push your changes
jj git push –branch main

  • Advantage Jujutsu:

  • Better visibility of remote changes
  • Simplified integration of others’ work
  • More intuitive handling of distributed workflows

Getting started with Jujutsu

Installation is simple (via Homebrew, winget, or source), and setup involves minimal configuration. Its command set is intuitive for Git users – just without the baggage of staging, complex rebasing, or stash juggling.
Here is some practical guidance on installation, configuration, and command equivalents for those ready to explore.

Installation

macOS

[bash]
brew install jj

Windows

[cmd]
winget install jujutsu –id jj-vcs.jj

From Source

[bash]
git clone https://github.com/martinvonz/jj.git
cd jj
cargo install –path .

Refer official install and setup guide

Configuration

Create a .jjconfig.toml file in your home directory:

toml:

[user]
name = “Your Name”
email = “your.email@example.com”[ui]
diff-editor = “vscode”
merge-tool = “vscode”

[aliases]
st = “status”
co = “checkout”

Basic Commands

[bash]
# Initialize a new repository
jj init# Or clone an existing Git repository
jj git clone https://github.com/example/repo.git

# Create a new change
jj new -m “Add new feature”

# Modify the current change
jj amend

# View status
jj status

# View history
jj log

# Compare changes
jj diff

# Push to Git remote
jj git push

Migration Tips

For Git users transitioning to Jujutsu, here are some helpful equivalents:

Git CommandJujutsu Command
git initjj init
git clonejj git clone
git statusjj status
git addNot needed (changes auto-tracked)
git commitjj new or jj amend
git checkoutjj checkout
git branchjj branch
git mergejj merge
git rebasejj rebase
git stashNot needed (working copy is a commit)
git pushjj git push
git pulljj git fetch + jj rebase

Use Cases

Large-scale Monorepos

Companies with large monorepos face unique challenges with version control. Jujutsu offers several advantages:

  • Better performance with efficient handling of large repositories
  • Improved visibility with clearer history visualization
  • Enhanced collaboration with better merge conflict resolution
  • Simplified workflows with reduction in complex Git commands

Cross-team collaboration

Enterprises with multiple teams working on shared codebases benefit from:

  • Flexible branching: Easier management of feature branches
  • Improved merge processes: Better resolution of conflicts
  • Enhanced history editing: Cleaner commit history
  • Compatibility with existing workflows: Incremental adoption possible

Known Challenges with Jujutsu

Despite its promise, Jujutsu is still evolving. Limitations include:

  • A smaller ecosystem compared to GitHub/GitLab.
  • Differences in terminology and workflows require mental re-mapping.
  • Limited IDE integrations.
  • A learning curve for experienced Git users due to different mental models.
  • Less community maturity compared to Git.

That said, its strong Git interoperability allows teams to experiment and gradually adopt features at their own pace.

Final thoughts

Jujutsu represents a significant evolution in version control systems, addressing many of Git’s limitations while maintaining compatibility with Git repositories. While Git remains the dominant VCS and will continue to be widely used, Jujutsu offers a compelling alternative that simplifies many common workflows and reduces friction in the development process. As more developers experience these benefits, we may see a gradual shift toward Jujutsu or similar systems that build upon Git’s foundation while addressing its pain points.

For teams considering Jujutsu, the good news is that adoption can be incremental. Developers can start using Jujutsu alongside Git on the same repositories, allowing for a gradual transition that minimizes disruption while maximizing benefits.

Whether you’re a seasoned Git user looking for a more intuitive experience or a team leader seeking to improve developer productivity, Jujutsu is worth exploring as the next evolution in version control systems.

At InfoVision, we’re constantly exploring such next-gen developer tools to improve engineering efficiency and software quality. If you’re rethinking your DevOps strategy or looking to modernize your toolchain – let’s talk.

We’d be happy to share insights, adoption strategies, or even help you pilot emerging technologies like Jujutsu in your environment.

Connect with us at digital@infovision.com

Java vs. Node.js: Making the right choice for today’s enterprise needs

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As enterprises transform digitally, their tech choices must align with larger strategic outcomes: performance, scalability, developer agility, and future-readiness. The long-standing Java vs. Node.js debate has matured. It’s no longer a question of which is better overall, but which fits best — for your business context.

In this blog, we explore whether to:

  • Modernize legacy Java systems for long-term reliability
  • Adopt Node.js for lightweight, real-time experiences
  • Or craft a hybrid approach for maximum flexibility

Let’s explore how to make the right call.

Understanding Java and Node.js

Before choosing a migration or development path, it’s important to understand what makes Java and Node.js distinct.

Node.js

Node.js is a JavaScript runtime environment that allows developers to build server-side applications using the JavaScript programming language. Known for its event-driven, non-blocking I/O model, Node.js is particularly well-suited for building scalable network applications and real-time, data-intensive web services. Here are some key features that make Node.js a popular choice for modern web and server-side development:

  • Asynchronous, event-driven architecture
  • Extensive ecosystem of open-source libraries and tools
  • Efficient resource utilization and high concurrency
  • Rapid development and deployment with JavaScript

Java

Java is a widely adopted, enterprise-grade programming language and platform that offers robust features, extensive tooling, and a mature ecosystem. Java-based applications are known for their reliability, security, and scalability, making it a popular choice for mission-critical enterprise systems. Java stands out because of these proven capabilities:

  • Strongly-typed, object-oriented language
  • Extensive enterprise-grade libraries and frameworks
  • Proven track record of reliability and security
  • Mature development tools and ecosystem

Key considerations for migration, modernization, and new development

1. Performance

Performance is a critical factor when developing new applications or migrating legacy applications. Below is a benchmarking analysis that compares the performance of Node.js and Java-based applications across various workloads and scenarios.

2. Horizontal scaling (distributed architecture)

Modern enterprises are shifting towards cloud-native architectures with containers, serverless computing, and microservices. Java’s enterprise-ready features, robust ecosystem, and scalability make it a natural choice for building cloud-native applications, especially in large-scale, mission-critical deployments whereas, the asynchronous event-driven model and flexibility of Node.js align well with the demands of cloud-native application development, enabling rapid prototyping and deployment of scalable, distributed services.

Java for cloud-native applications

  • Strong support for Kubernetes, Docker, and Spring Boot microservices
  • Works well with serverless platforms (AWS Lambda, Azure Functions) but has a heavier runtime
  • Best for enterprises needing hybrid cloud and on-premises stability

Node.js for cloud-native applications

  • Lightweight and event-driven – ideal for serverless functions and microservices
  • Scales horizontally across distributed environments, making it a good fit for cloud-first startups
  • Works seamlessly with API-driven architectures and edge computing

3. Cost

The total cost of ownership (TCO) is a critical factor in migration or new development decision, encompassing infrastructure, licensing, and ongoing maintenance expenses.

Infrastructure

Node.js, with its lightweight, event-driven model, typically requires fewer server resources and lower infrastructure costs compared to Java-based applications, which often have higher memory and CPU requirements.

Licensing and tools

Node.js, being an open-source platform, avoids the licensing fees associated with commercial Java development tools and application servers.

Maintenance and support

While Java benefits from a mature, enterprise-grade ecosystem with extensive documentation and a large community of experienced developers, Node.js maintenance and support costs can be lower due to its simpler architecture and the prevalence of open-source libraries and community-driven solutions.

4. Security and reliability

As organizations migrate legacy applications or build new ones, ensuring robust security, reliability, and compliance is paramount, especially for mission-critical systems.

Security

Java’s strong typing, mature security libraries, and well-established best practices make it a preferred choice for building secure, enterprise-grade applications. Node.js, while offering a robust security ecosystem, requires more proactive management and vigilance to address potential vulnerabilities in its open-source dependencies.

Reliability

Java’s proven track record of reliability, scalability, and fault tolerance, combined with its enterprise-grade tooling and application containers, make it a compelling choice for mission-critical systems that demand high availability and resilience. Node.js, with its asynchronous, event-driven architecture, can also deliver reliable performance, particularly in WebSockets workloads, when properly configured and managed.

Compliance and governance

Organizations in highly regulated industries often require strict compliance and governance frameworks. Java’s maturity and enterprise-grade security features align well with such requirements, while Node.js may require additional attention to ensure the integrity and traceability of mission-critical applications.

Real-world success stories

This section outlines a series of real-world case studies that highlight the experiences and outcomes of organizations that have successfully transitioned from legacy platforms to Node.js or Java-based architectures or built new applications using any of these technologies.

E-commerce platform

A leading retail e-commerce company migrated its legacy .NET-based platform to a Node.js-powered architecture, resulting in a 40% improvement in response times, a 25% increase in developer productivity, and significant cost savings in infrastructure and hosting.

Healthcare data analytics

A healthcare technology provider transitioned its legacy Java-based data analytics platform to a modern, microservices-based architecture using Node.js. This migration enabled a 50% reduction in time-to-market for new features and a 30% improvement in system scalability.

Financial services integration

A global financial services firm migrated its complex integration layer from a monolithic Java application to a distributed, event-driven architecture powered by Node.js. This transformation resulted in a 35% increase in system throughput and a 20% decrease in maintenance overhead.

Open Access Fiber Network Platform

A leading digital infrastructure company built a new platform to manage an open-access fiber optic network. Using a hybrid architecture with Java microservices for network provisioning and Node.js services for real-time dashboards and portals, the platform enabled multi-tenant ISP management, customer onboarding, and network provisioning. This approach improved scalability, optimized performance by workload type, and supported domain-driven design.

Making the right choice

Java remains a robust and reliable choice for enterprise-grade applications, particularly in industries that demand high security, compliance, and scalability. Its mature ecosystem, extensive libraries, and strong typing make it suitable for large-scale, mission-critical systems. Java’s performance in CPU-intensive tasks and its proven track record in enterprise environments continue to make it a preferred choice for many organizations.

Node.js, on the other hand, excels in real-time, I/O-bound applications due to its non-blocking, event-driven architecture. It is particularly favored by startups and agile development teams for its rapid development cycle, lightweight runtime, and efficient resource utilization. Node.js is also highly suitable for microservices and serverless architectures, making it a popular choice for modern, cloud-native applications.

Both platforms have their strengths and are evolving to meet the demands of contemporary software development. Java’s advancements in cloud-native compatibility and Node.js’s growing ecosystem for enterprise applications highlight their adaptability and relevance in today’s technology landscape.

To help CTOs and IT leaders make an informed choice, here’s a decision matrix that can help:

Our perspective

For enterprise-scale applications, Java is often the preferred choice due to its maturity, extensive libraries, and strong concurrency support. However, for modern web applications that require real-time interactions and fast development cycles, Node.js is an excellent choice due to its lightweight, scalable architecture and the ability to use JavaScript throughout the full stack.

Best of both – A hybrid approach

Many enterprises are moving toward a hybrid tech stack where Java powers mission-critical backend systems, while Node.js handles APIs, microservices, and real-time interactions. For instance, a large financial institution may use Java for its core banking system while integrating Node.js for a customer-facing chatbot that responds in real-time. You could consider a hybrid approach for the best balance of stability and flexibility, if it aligns with your business needs.

Still unsure which approach suits your enterprise? Our experts at InfoVision specialize in Java and Node.js migrations, modernization, and cloud transformations. Connect with us at digital@infovision.com to explore the best-fit solution for your business.

How GenAI Is reshaping retail – And why we are on the cusp of a remarkable transformation

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A customer browses your online store late at night, checking out the maroon and dark green jacket, adding it to the cart but leaving without buying. By morning, your AI-powered system has analyzed their behavior, identified them as a high-intent shopper, and triggered a personalized offer the moment they step into your store. No manual intervention, no guesswork – just seamless AI-driven engagement that converts interest into sales.

This isn’t a futuristic fantasy. It’s happening right now as retailers embrace Generative AI (GenAI) to optimize every aspect of the shopping experience – from hyper-personalized marketing and dynamic pricing to automated supply chains and cashier-less stores.

In our recent webinar on How GenAI is transforming retail innovation, industry leaders explored how GenAI is already making waves in retail, the challenges of AI adoption, and where the future is headed. Hosted by Nitin Naveen (VP – Innovation Strategy, AICorespot) and moderated by Jaydev Doshi (Director, Retail, InfoVision), the webinar panel featured:

From C-suite perspectives on AI implementation to how retailers are using AI to reduce costs, increase sales, and improve customer retention, the discussion made one thing clear: AI isn’t coming. It’s already here.

Let’s break down what they had to say – without the technical jargon, but with genuine insights and some additional real-world examples to boost understanding.

Retail’s two big goals: Cut costs & boost sales

Retails two big goals Cut costs & boost sales

If there’s one thing that never changes in retail, it’s the relentless push to lower costs while driving more revenue.

Aravind Kashyap explained how retailers work with razor-thin margins. Anything that helps them reduce costs or increase sales is a game-changer.
Retailers are leveraging AI to optimize supply chains, reduce waste, and predict demand more accurately – helping them keep shelves stocked without overloading warehouses. AI is also helping them streamline operations, reduce IT overhead, and automate routine processes, which adds up to significant cost savings over time.

On the sales side, AI is making customer engagement more intelligent and efficient. AI-powered personalization ensures that customers receive precisely what they are looking for, without having to search endlessly.

A good example of this is Walmart’s use of AI-driven inventory forecasting. Walmart’s AI analyzes historical sales data, weather patterns, and regional trends to ensure that the right products are in stock at the right time. During peak shopping seasons, AI helps prevent stockouts and overstock issues, ensuring customers always find what they need while reducing operational waste.

AI is also transforming retail marketing. GenAI can now create highly targeted campaigns by analyzing past purchases, browsing behaviors, and even sentiment from social media interactions. Instead of blasting generic promotions, AI helps retailers send the right offer to the right customer, at the right time.

The shift from personalization to hyper-personalization

For years, retailers have worked on personalizing experiences – things like “People who bought this also bought…” or sending birthday offers. But GenAI is taking this further, creating experiences that feel uniquely crafted for each customer.

Take chatbots, for example. A few years ago, they were just task-oriented virtual assistants, handling basic queries like “What’s my order status?” or “Where’s the nearest store?” Now, they understand context, sentiment, and intent. Instead of offering 20 similar products, they interpret what the customer really wants and deliver the most relevant choice.

Retailers are also using AI to analyze customer sentiment and behavior in real time. If someone searches for a white shirt with a blue stripe, AI can recognize not just the color preference but the style, fit, and even brand tendencies. This level of precision makes shopping frictionless and more engaging.

Aravind spoke about Domino’s being a great example of this shift. They let customers order pizza using just an emoji, because their AI already knows their usual order. No need for extra steps or manual selections. That’s hyper-personalization in action.

The beauty retailer, Sephora, utilizes AI-powered chatbots to deliver personalized product recommendations and makeup tips. By analyzing customer preferences, past purchases, and even skin tone data from Sephora’s Color IQ technology, these chatbots offer tailored advice that mimics the expertise of in-store beauty consultants. This level of hyper-personalization can help increase customer engagement and boost conversion rates.

Hyper-personalization also helps solve a critical challenge in retail – customer churn. As Hemanth Kumar pointed out, AI is now being used to predict when a customer is about to leave and take proactive steps to retain them. AI-powered churn prediction models analyze browsing habits, purchase frequency, and engagement levels to flag at-risk customers. Retailers can then offer personalized offers, exclusive early access, or targeted re-engagement campaigns before customers abandon the brand.

AI’s growing role in reducing product returns

AI’s growing role in reducing product returns

Returns are one of the biggest cost drivers in retail, and AI is now tackling this issue from both proactive and reactive angles.

On the proactive side, AI is improving product recommendations and sizing tools, reducing the chances of a customer buying the wrong item in the first place. Virtual try-ons and AI-generated fit assistants help customers make more informed choices, leading to fewer returns.

On the reactive side, AI is analyzing return patterns to identify which products are frequently sent back and why. Retailers can then decide whether to improve product descriptions, fix manufacturing defects, or even discontinue problematic items altogether.

Zalando, a European online fashion retailer, implemented an AI-powered size advice feature that combines machine learning and computer vision technologies. This system analyzes various data sets, including brand-provided item information, to inform customers if an item runs small or large, recommending size adjustments accordingly. As a result, Zalando has achieved a 10% reduction in size-related returns compared to items without size advice.

Another case in point is Amazon using AI to identify damaged items before they are shipped to customers, to reduce inevitable returns down the line.

Bridging the online-offline gap

Bridging the online-offline gap

One of retail’s big pain points has been the disconnect between online and in-store experiences. Customers want a seamless transition between both, retailers struggle to make that happen. AI is starting to bridge that gap.

Imagine browsing for shoes on a retailer’s website in the morning, then walking into the store in the afternoon. AI can recognize your online activity and offer personalized recommendations based on what you already viewed.

Some retailers are even using AI-powered cart handovers, where your mobile shopping cart syncs with in-store inventory in real time. If a product is unavailable in-store, AI can instantly suggest an alternative or offer a home delivery option, without any extra steps.

Luxury brand Burberry’s phygital store experiences through smart mirrors and digital screens allow seamless online-to-offline integration.

This kind of AI-driven unified commerce is quickly becoming the new standard.

AI Is no longer just a tool, it’s a business partner

AI Is no longer just a tool, it’s a business partner

Retailers are now embracing Agentic AI, which moves beyond just assisting with recommendations or transactions. AI is now taking action.

For example, many companies have already deployed AI-powered dynamic pricing models that adjust product prices in real-time based on demand, competitor pricing, and customer behavior. Instead of waiting for a team to manually review pricing trends, AI makes those decisions instantly.

Retailers are also using AI to automate back-end operations, such as supply chain management. AI is now predicting which products will run out of stock before it happens, rerouting shipments in real time, and ensuring inventory levels are optimized, without human intervention.

Ocado, a UK-based online grocery retailer, operates AI-powered fulfillment centers where intelligent robots autonomously manage inventory, retrieve products, and pack orders.

Cashier-less checkouts, another AI-driven innovation, have started appearing in major retail stores. AI doesn’t just scan products; it also monitors shopping behavior, detects fraud, and personalizes offers in real time.

Even finance departments are feeling AI’s impact. Companies are now deploying AI-driven credit collection agents, which manage invoices, payment reminders, and credit limits without human involvement.

As Aravind noted, AI is not just a tool, it’s a teammate making decisions alongside you.

Challenges and considerations

Challenges and considerations

While the promise of generative AI is immense, the journey isn’t without its obstacles.

Cutting through the AI hype

Every executive today is under pressure to jump into AI.

Sruti Patnaik shared a common dilemma: many retailers want to launch AI projects simply because everyone else is doing it. But not every problem requires GenAI.

Before investing in AI, retailers need to ask:

  • Is our data clean, organized, and structured – Is it ready to use?
    AI is only as good as the data it’s trained on. Messy data leads to unreliable results.
  • Is this solving a real business challenge?
    Just because AI can do something doesn’t mean it should.
  • What’s the actual ROI?
    AI projects can be expensive, and not all of them deliver the expected results.

The key takeaway? AI isn’t a magic fix for every problem. It’s a powerful tool, but only when used thoughtfully, and with the right data. Retailers that clean up, centralize, and structure their data will have a huge competitive advantage as AI continues to evolve.

Amazon has mastered this by using AI to clean and structure vast amounts of customer data. Its recommendation engine is powered by AI models that continuously refine product suggestions based on real-time shopping behavior.

Security & ethical concerns – The elephant in the room

Many companies, including big retailers like Target, have implemented AI-driven fraud detection systems that analyze real-time transaction data to identify unusual patterns. The AI models are trained to recognize potential fraud based on behavior anomalies, such as sudden large purchases or frequent returns.

However, as AI becomes more powerful, security risks grow even bigger. It’s a double-edged sword. Deepfake scams, misinformation, and data privacy concerns are already raising red flags across industries. AI-powered systems need vast amounts of customer data, and that opens up serious questions about how much data is too much.

Sruti emphasized that businesses are not spending enough time thinking about security. While companies rush to adopt AI, cybersecurity risks are often treated as an afterthought. And that’s a problem.

The speed at which AI is evolving means that ethical and security discussions need to catch up – very fast. Addressing this challenge necessitates continuous vigilance, innovative strategies, and a commitment to cybersecurity excellence.

What’s next for AI in retail?

What’s next for AI in retail

The future of AI in retail isn’t about whether companies will adopt AI, it’s about how they choose to use it.

Aravind believes that AI will soon be embedded into every business function, from HR and supply chain to marketing and finance. The challenge won’t be finding AI solutions, it will be figuring out how to use them effectively.

Sruti advised that businesses start small, focus on internal use cases first, and prioritize ROI.

Hemanth echoed this sentiment, emphasizing that the next three months matter more than the next three years. AI is evolving so rapidly that businesses must remain adaptable.

The AI revolution is here. Are you ready?

Retail is at an inflection point. The brands that succeed will be the ones that think strategically about AI, rather than just jumping on the bandwagon.

  • Hyper-personalization is no longer optional, it’s expected.
  • Agentic AI is turning AI from a passive tool into an active business partner.
  • Security and ethical concerns need to be tackled head-on.

The question isn’t whether AI will transform retail, it’s how well businesses can leverage it to their advantage. Because AI isn’t coming! It’s already here.

For more insights on the future of in-store shopping in the GenAI era, download our whitepaper.

Want to build your AI strategy for retail? Let’s talk. Connect with our experts at digital@infovision.com.

The future of digital health: Innovations reshaping healthcare

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Picture this: a world where a smartwatch can alert a doctor about a potential health issue before someone even feels sick. That’s not sci-fi. It’s the power of digital health, and it’s already reshaping how we experience healthcare. Our recent webinar, hosted by Sandeep Punjani, Vice President – Healthcare, Life Sciences and Manufacturing at InfoVision on “The Future of Digital Health: Innovations in Healthcare Technology,” brought together some of the brightest minds in healthcare and technology to talk about where the healthcare industry is headed and what it means for healthcare providers and their surrounding landscape. Here’s a breakdown of what they had to say.

Empowering patients with AI and engagement tools

Empowering patients with AI and engagement tools

Engaging patients in their care journey is the cornerstone of modern healthcare. Technology is giving patients tools to take control of their health, providing access to information, and facilitating better communication with providers.

Shauna Zamarripa, Director of Business Analytics at Community First Health Plans, shared how her team used AI and predictive analytics for maternity health monitoring. This project improved outreach and care for high-risk pregnancies by partnering with local organizations, ensuring a holistic support system. She also highlighted how AI-driven insights are now being applied to behavioral health.

“Being able to do predictive analytics has been absolutely game changing and we’re now moving that into the behavioral health space,” Shauna highlighted.

But the applications of AI don’t stop there. Madhur Pande, Senior Vice President of Digital Product at Optum Health and former Executive Director of Digital Products at Kaiser Permanente, described how wearables are making proactive health management possible. She emphasized the importance of remote patient monitoring (RPM). Devices that track chronic conditions are transforming care by enabling continuous observation and early clinical intervention. Madhur described how wearable technologies help manage conditions like diabetes and heart failure, offering significant benefits for both patients and providers.

“Wearables keep people healthy and reduce the cost of care,” she explained.

Patient portals are another vital tool. They allow patients to access medical records, view test results, and receive reminders, fostering greater autonomy in managing their health. Pande noted that high adoption rates at Kaiser Permanente – exceeding 80%, demonstrate the potential for technology to enhance patient-provider relationships.

Telehealth: Increasing access and reducing barriers

“Availability, accessibility, affordability – all these things have really been supercharged with telehealth,” he shared.

Telehealth is also expanding beyond virtual consultations. Diagnostic services, such as radiology readings, can now be handled remotely, often reducing turnaround times from days to hours. To Ram’s points, Madhur cited how behavioral health services have particularly embraced telehealth, with virtual visits rising from 40% of appointments in 2021 to 67% in 2023.

Shauna pointed out how it is essential for providers to be cognizant of patient preferences and leave space to innovate and create new mechanisms instead of just continuing doing things the way they have always been done.

“The ultimate goal is giving the patient the ability to feel like they are in charge of their healthcare journey,” she remarks.

AI-powered clinical workflows: Efficiency without overload

Telehealth_ Increasing access and reducing barriers

AI offers immense potential to streamline clinical workflows, reduce administrative burdens, and combat provider burnout. However, thoughtful implementation is critical to prevent new inefficiencies. Clinicians are already stretched thin, and poorly deployed AI can add complexity instead of reducing it. Both Shauna and Madhur emphasized that AI must complement, not complicate, existing processes.

“You have to be cognizant of anything that is incorporating AI to not replace humans but support them.” Shauna believes.

One promising application is AI-driven triaging, where tools can sort patient messages and prioritize urgent cases. Madhur highlighted the importance of pilot testing and involving clinicians when rolling out new systems to ensure adoption and trust.

“When we are thinking about bringing AI to an environment like health care, it needs to be very thoughtful and purposeful,” she noted.

The concept of “human-in-the-loop” AI emerged as a best practice, ensuring clinicians retain control over AI-generated recommendations. Trust, transparency, and collaborative governance are essential to successfully integrate AI into healthcare systems.

Data privacy, bias, and ethical AI

Data privacy, bias, and ethical AI

With AI’s growing role comes increased responsibility to safeguard data privacy and mitigate bias. Shauna highlighted the importance of consistent data collection practices to ensure accuracy and integrity. She described how improper handling of sensitive information – such as emailing unprotected patient data, poses significant privacy risks.

“You need to make sure that the AI technology that you’re deploying or using within your organization is compliant with the relevant regulatory bodies,” Shauna highlighted.

Consent management is another critical area. Ram pointed out that machine learning algorithms do not inherently respect consent boundaries. Embedding consent rules into AI workflows is vital to prevent unauthorized data usage.

He remarked “We have to find a way to take the consent management restrictions and build them into the ML workflow.”

Bias in AI models remains a pressing issue. Without diverse and representative datasets, AI systems risk perpetuating healthcare disparities. Ethical review boards and rigorous data governance frameworks are necessary to address this challenge.

Shauna pointed out that data quality directly impacts the success of AI.

“It is really important to make sure that we unknowingly are not feeding our biases to the systems,” Madhur highlighted.

AI in drug discovery and personalized medicine

AI in drug discovery and personalized medicine

Drug discovery is a costly, time-consuming process, but AI is accelerating key stages. From predicting protein structures to identifying therapeutic targets, AI-driven simulations reduce reliance on lab testing.

Ram described how AI helps pharmaceutical companies speed up clinical trials by automating participant selection and analyzing trial data in real time. While no drugs have been fully developed using AI yet, platforms like DeepMind’s AlphaFold and IBM Watson are already making significant strides.

Personalized medicine, guided by genomic data, represents another frontier. AI enables rapid analysis of genetic mutations and their implications for individualized treatment plans.

“If you look back ten years, most of the genomic information was static, but the human genome is not static, it’s evolving. So, the AI tools have to be able to absorb real time data and be able to continually update your genome map,” Ram observed.

Cybersecurity for a digital future

Cybersecurity for a digital future

The rapid adoption of digital health technologies raises cybersecurity concerns. Healthcare data is a prime target for cyberattacks, and traditional security measures are insufficient against AI-enabled threats.

Ram stressed the need for dynamic, real-time cybersecurity solutions since static defences can’t handle threats evolving with AI.  He emphasized on adaptive architectures and comprehensive employee training.

“The current suite of cyber security platforms across the Globe were never designed with the proliferation of AI in mind”, he said.

Policy frameworks must also evolve. Regulations governing AI, telehealth, and genomic data require continuous refinement to balance innovation and patient safety.

Webinar highlights

In summary, the webinar underscored several transformative trends shaping the future of healthcare:

AI-driven tools: From wearables to predictive analytics, AI is empowering patients and improving outcomes.

  • AI-driven tools: From wearables to predictive analytics, AI is empowering patients and improving outcomes.
  • Telehealth expansion: Virtual care is making healthcare more accessible, especially for underserved populations.
  • Ethical AI: Clean data, privacy safeguards, and diverse datasets are crucial to making AI reliable and equitable.
  • Pharmaceutical innovation: AI is reducing the time and cost of drug development while paving the way for personalized medicine.
  • Future technologies: Quantum computing and advanced cybersecurity solutions are poised to tackle healthcare’s critical challenges.

The road ahead

The future of digital health is filled with promise. Innovations in AI, telehealth, and personalized medicine are enhancing care delivery and improving outcomes. However, realizing this vision requires a holistic approach – combining technology with thoughtful policy, ethical governance, and human collaboration.

As healthcare embraces these changes, the goals remain clear: empowering patients, reducing disparities, and creating a more connected, efficient healthcare system. By pushing boundaries and staying vigilant about ethical considerations, we can shape a future where technology serves as a true catalyst for health and well-being. To know more about the technologies shaping healthcare, reach out to us at digital@infovision.com.

MSSP: The final piece of the Security puzzle for CISOs

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CISOs (Chief Information Security Officers) are constantly putting out fires as they face increasing complexities daily with additional threats like AI-based attacks, ransomware, and supply chain vulnerabilities dotting the ever-evolving threat landscape. Even if new security tools are acquired, a lack of skilled staff to manage them exacerbates the problem. As if this is not enough, they face growing regulatory pressures, limited budgets, and resource constraints, often leaving companies vulnerable.

According to a Security Leaders Report, on average, enterprises use 76 security tools, many of which require manual intervention, leading to inefficiencies and errors. The shortage of cybersecurity professionals is severe, with over 500,000 positions unfilled in the U.S. alone, creating additional stress on already overstretched teams. Fatigue from manual tasks and alert overload contribute to human errors, driving high turnover rates in the field—33% of security professionals change careers due to burnout, it is said.

This pressure extends to CISOs themselves, with 32% considering leaving due to regulatory demands and 70% contemplating a change due to overall stress. Board conflicts, like the one that led Alex Stamos to leave Facebook, further strain the role, making the average CISO tenure just two years, compared to five for other C-suites.

This blog focuses on how CISOs, who are tasked to fight evil with their hands tied behind their back, can bolster their arsenal with the right Managed Security Services Provider (MSSP) partner. As they grapple with the challenges of limited resources and budgets causing burnout and attrition, can MSSPs be the silver lining? What immediate and strategic advantages do MSSPs bring? How can companies benefit from this partnership and how can CISOs ensure that their KPIs are met? Read on to uncover our perspective.

Going the MSSP way is a wise move by CISOs

Going the MSSP way is a wise move by CISOs

An MSSP offers security tools and services such as security management, monitoring, and response services. It acts like an extended arm, especially for businesses with small in-house security teams and limited expertise. An MSSP can therefore be the exact solution to the CISO’s predicament. Let’s find out ‘why’ and ‘how’.

Reduced costs

Having a full-scale in-house security team is expensive, especially when the security budget is tight. Most CISOs do not have a separate budget, as their budgets are carved out from the IT. On average, only 9% of this IT budget goes to security. In such a challenging scenario, partnering with an MSSP makes ample business sense.

For instance, any organization that’s considering running an in-house Security Operations Center (SOC), would have to spend more than USD 2.8 million a year.10 Running an advanced SOC can be as expensive as USD 5 million. In contrast, using SOC services from an MSSP costs around USD 1.4 million – around 50% cheaper than an in-house SOC. These numbers may further go down depending on what type of services are chosen. CISOs understand that in-house security teams mean full-time resources and tools – this needs up-front capital investment.

Skill availability

Apart from costs, the availability of the right experts in the market is a concern. As millions of security jobs are still open, recruiting the right talent continues to be a hurdle. It takes more than 7 months to recruit and train a security analyst. Attrition in the security department makes this even worse – it can be assumed that about 3 analysts will leave or be fired from the team.12

The lack of resources creates fatigue for the small in-house team, which is unable to cope with the tasks. According to Gartner, by 2025 more than 50% of security incidents will be attributed to a lack of security professionals or human errors.13

CISOs do not have to deal with either skill gaps or the availability of talent with an MSSP.  MSSPs employ experienced cybersecurity experts with specialized knowledge in various domains, such as threat detection, incident response, and compliance. MSSPs provide access to skilled professionals and advanced security tools like SIEMs, threat intelligence platforms, and automated detection systems.

Tools, technology & capabilities

Security Tools technology capabilities

As the threat landscape keeps changing with new threats looming around, newer tools are launched in the market. We already discussed that on average an enterprise maintains more than 76 security tools. Managing and adding so many tools can be unrealistic for many companies.

Additionally, organizations that have piled up security tools to avoid buyers’ regret end up not maintaining or patching previous software. This increases the attack surface and vulnerabilities. It can also create non-compliance issues due to software non-maintenance.

When outsourced to an MSSP, all these challenges are easily handled. Additionally, MSSPs provide 24/7 monitoring, threat detection, and immediate incident response capabilities to reduce the risk of undetected breaches. This constant vigilance reduces the average breach detection time.

Threat detection and incident response are critical parameters. For instance, if a company with limited in-house capabilities takes longer to detect and respond to a breach, then it’s likely to have a longer period of downtime. The longer the downtime, the more revenue losses. For most enterprises, the cost of hourly downtime is about USD 300,000, and these costs have been rising.14 This signifies the importance of faster detection and incident response which an MSSP can offer. Every hour means lower costs incurring out of downtime.

Even further, implementing advanced technologies like AI & ML in-house can be expensive as well as complicated, but these technologies are needed for advanced threat detection and remediation. Partnering with a service provider with the latest capabilities such as AI, ML, & Automation is much simpler.

Regulatory compliance

Regulatory compliance

Companies need to be compliant with many data security and privacy regulations, usually more than one. For example, a multinational financial company might have to deal with GDPR, PCI DSS, CCPA AML, and more.

The cost of non-compliance with each one of these regulations can be pretty steep. For instance, GDPR fines can go up to Euro 20 million or 4% of annual turnover (global), whereas PCI DSS ranges between USD 5000 to 100,000 per month until compliance is met.

In 2023, Meta was fined a staggering amount of USD 1.2 billion (under GDPR) relating to the unlawful transfer of customer data to the USA.15 Non-compliance costs bother CISOs much because of too many hassles, and they usually need external help staying compliant with multiple, ever-changing regulations is complex and resource intensive.

MSSPs provide expert teams with in-depth knowledge of regulatory frameworks. They also leverage advanced tools like automated compliance monitoring and real-time reporting. They ensure continuous compliance by managing audits, maintaining required controls, and swiftly addressing gaps, allowing businesses to avoid costly fines, reputational damage, and operational disruptions while staying focused on growth.

Scalability

With staff and skill shortages, it’s difficult for CISOs to take on additional projects. For instance, if a Zero Trust security architecture is to be implemented on top of existing solutions – more resources are needed. Resources are also needed to manage the operations after the implementation. Such business requirements cannot be met overnight – hence MSSP is a good alternative.

MSSPs offer scalable solutions that grow with the business. Whether a company needs to expand coverage, integrate new systems, or manage peak security demands, MSSPs adapt more easily than in-house teams. The additional advantage is not just scaling up but also scaling down – the number of resources can be reduced if there’s no requirement in the future.

Picking up the right partner

There is no doubt that picking an MSSP makes a compelling case. However, there are the following key considerations that businesses must oversee before zeroing in on anyone. Here is a checklist that we have designed to help you identify the right partner.

  • Do services and solutions offered by the MSSP integrate with current technology investments?
  • What are detection and response capabilities? What are the metrics used to measure the success? What are the SLAs?
  • What different regulatory compliances does the MSSP support?
  • What is the scope of the service offerings? Are the SLAs defined?
  • What is the level of reporting and visibility? Will there be real-time dashboards available? Will security operations be transparent enough? What will the frequency of different reports be?

The above questions help businesses align with the right MSSP that aligns with their security needs, technology requirements, and strategic goals.

How InfoVision can help

As one of the most trusted MSSP providers, we can help to improve your security posture and resilience. Right from endpoint protection solutions to the implementation of modern Zero Trust security solutions, we’ve got you covered.

Our customers trust us for our robust capabilities such as threat detection, incident response, intrusion detection, managed firewall, virtual private network (VPN), and various security assessments. For businesses that want to evaluate their current security posture, we offer different security assessments.

We enable businesses to access the latest technologies and security platforms like AI, ML, Automation, and more for enhanced detection capabilities. We also offer compliance assistance empowering CISOs and other C-suite leaders to focus on their core business objectives.

With security professionals having sound industry experience, businesses can easily onboard resources to scale up operations. Above all, our core strength is in security policy configurations, access management, 24/7 threat detection, and swift response to cyber risks. InfoVision also has experience in managing security operations of large organizations – and if you need advice on where to begin, talk to us today.

Need help in managing security or improving current security posture? Get in touch with us today for a discovery call.

You may also read:  MDR made simple to explore the emerging role of managed detection and response (MDR) in cybersecurity.

Adopting Gen AI? Start with Modernization

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The era of Generative Artificial Intelligence (Gen AI) has arrived.

In 2023, Generative AI made its debut, capturing attention across industries. By 2024, organizations began actively harnessing its capabilities, translating the same into tangible business value. Today, businesses worldwide are keen to jump onto the AI bandwagon to improve efficiency, innovate, and stay competitive. The use cases of this transformative technology seem to be extensive and endless, and in just a short span of two years, AI has become a strategic imperative for businesses across verticals such as healthcare, finance, retail, manufacturing, and telecom.

We’re now way past the initial hype.  Boardrooms and IT departments alike are now endorsing the immense potential AI holds and within a short span it has become a key strategic focus for many businesses. A recent survey underscores this optimism: over 67% of leaders are prioritizing Generative AI, with a third of them naming it their top priority due to its transformative potential. According to recent reports including Goldman Sachs Research, global AI investments could significantly reach $200 USD billion by 2025 and $32.8 USD billion in Asia-Pacific, highlighting the rapidly growing commitment to AI technologies worldwide.

While rolling out AI capabilities is a top priority today and will most likely continue to be so over the next few years, businesses must bear in mind one key challenge in its adoption: legacy applications. Agreed that legacy applications are the bedrock of many businesses, but equally true is the fact that they are aging fast and struggling to keep pace with modern technological advancements. This creates a significant roadblock for businesses planning to adopt AI, as most of their required data resides in such legacy applications. In fact, about 10% of business applications are at “end of life” (~150 applications per business, on average), according to an ISG report.

As the gap between AI adoption and legacy shortcomings widens, the need for Application Development and Modernization (ADM) strengthens. ADM enables organizations to expedite the adoption of AI, enhance operational efficiency, and establish a scalable foundation for future development by modernizing applications and optimizing IT infrastructure. And we have data to substantiate this: despite the ongoing cost optimization efforts, investments in ADM are on the rise. ADM is no longer a cost center but a strategic asset for the age of AI.

Modernizing applications and infrastructure is seen as essential for staying competitive in the digital age. This strategic view helps organizations leverage technology effectively and drive growth.

Why most businesses continue to support legacy applications?

The State of Application Modernization Report 2024 states that 62.5% of CTOs had spoken about their biggest challenge being, “the accumulated technical debt and dependencies within legacy applications.”

Despite the obvious benefits of modernization including adoption of AI, many businesses continue to support legacy applications in their operations for multiple reasons:

Critical role in operation

Legacy applications often perform essential functions such as supporting core business processes, financial transactions, customer data management, and other critical activities. Replacing these systems can be risky and complex considering that business operations cannot be disrupted during transition.

Intricated architecture and integration

Compatibility with modern platforms and technologies could be potentially challenging, time-consuming and error-prone for legacy systems which are built on outdated technology. Legacy applications often have intricated architecture and interdependencies. Untangling these legacy systems and integrating with modern systems can be a daunting task and cause business to delay or avoid modernization.

Data and compliance concerns

Critical business data in legacy applications is essential for operations, decision-making, and analysis. Migrating this data is complex and risky, potentially causing security & compliance concerns, data loss or corruption.

High cost of modernization

Migrating to new applications often gets costly and can be substantial, not only due to direct expenses like new infrastructure, systems and software but also indirect expenses like data migration, system integration, and employee training.

Employee and stakeholder resistance to new technologies

People who are accustomed to working on legacy applications are the main barriers and may resist new technology due to comfort, fear of change, or concerns about the learning curve.

Skill Gap

Many organizations lack the IT staff, expertise, and time to migrate to new applications. Managing legacy applications with limited resources is often more practical, despite the drawbacks. Also finding engineers with expertise in both legacy and modern technologies could be a challenging task.

Fear of disruption

Legacy applications are embedded in a business’s operations, including those of partners and customers who rely on them. Changing these systems could disrupt relationships and operations, so businesses continue to support them to ensure continuity and reliability.

5 reasons why legacy applications hinder AI adoption

While legacy applications have been the backbone of operations, they pose significant challenges to AI ambitions. Such applications and systems are riddled with critical limitations that impact data accessibility and drain organizational resources. Here are five shortcomings of legacy applications that hinder AI adoption:

1. Data silos: Legacy applications often store crucial business data, but accessing and utilizing it becomes difficult due to outdated formats and limited data extraction capabilities. This hampers the use of large, high-quality datasets essential for AI applications.

2. Incompatibility with AI systems: Many legacy systems are incompatible with modern AI technologies, hindering effective implementation and scaling. This incompatibility can hinder seamless adoption of AI and limit its potential benefits.

3. Lack of integration capabilities: Many legacy systems lack modern APIs or integration capabilities needed to connect to AI platforms and other contemporary technologies.

4. Increased maintenance cost: Maintaining outdated applications often proves costly and resource-intensive, diverting funds from investing in new technologies like AI and straining budgets.

5. Limited scalability and performance: As legacy application is built on outdated hardware and software architectures that it struggles with computational demands of Modern AI applications like scalability and performance, failing to handle the large volumes of data and high processing requirements needed for AI, leading to inefficiencies and bottlenecks.

6. Security and compliance Issues: Security is a grave concern, as legacy applications are more vulnerable to threats and lack the advanced features needed to protect sensitive data and comply with modern regulations.

Modernizing legacy applications has its own challenges

Forward-looking businesses that plan to modernize their legacy systems still face many challenges along the way. While both boardroom members and IT leaders share the common goal of enhancing the customer experience and harnessing AI, their unique challenges can complicate modernization efforts and undermine AI’s effectiveness.

Boardroom Members IT Leaders
Accessing and utilizing data effectively:Boardroom members need timely, accurate data for decision-making, but legacy applications complicate data access and usage, limiting strategic insights. Integrating value streams:IT leaders often struggle to align different IT projects and systems seamlessly to support overall business objectives efficiently.
Integrating value streams:Boardroom members struggle to ensure business processes and operations align with organizational goals efficiently. Managing technical debt:Technical debt involves maintaining and updating older systems and code. IT leaders must address this to avoid hindering innovation and efficiency.
Managing organizational change:Implementing new technologies requires significant organizational changes, and boardroom members must manage resistance and ensure a smooth transition. Handling high software license costs:Legacy systems often have costly licensing fees. IT leaders must manage these while balancing investment in new technologies.
Phasing out outdated applications:Decommissioning legacy applications is complex, requiring boardroom members to manage the transition carefully to avoid disrupting operations and ensure full integration of new systems. Addressing skills and talent gaps:IT leaders struggle to find and retain skilled professionals to manage legacy systems and new technologies, slowing modernization efforts.

Overcoming these challenges requires a strategic approach and expertise. Partnering with an expert service provider can provide valuable insights and solutions tailored to specific business goals, needs, vulnerabilities, industries, and budgets. In fact, 96% of large enterprises are using external providers for some form of application service, according to a recent ISG ADM study. These external providers provide the resources needed for these legacy transformation programs, as well as their ability to combine cost optimization with modernization.

InfoVision has been a strategic partner to various businesses in developing and modernizing their applications for years. Our enterprise ADM services prepare businesses for seamless evolution. With our expertise in cloud, serverless operational models, agile and SAFe implementations, and emerging technology practices, we help businesses transition from complex legacy structures to dynamic and resilient application portfolios. Our comprehensive suite of services, including API modernization, microservices architecture, cloud-native and serverless operations, custom application development, and updating existing applications, empower businesses to excel in their digital transformation.

The critical role of application optimization in ADM

Modernizing and optimizing applications will become increasingly important as technology stacks grow more complex and demanding. With the integration of advanced technologies like AI and cloud computing, existing applications will need to be fine tuned to meet new requirements efficiently. By focusing on ADM, businesses can stay competitive, enhance user experiences, and maximize their technological investments.

Research by top IT industry experts suggests that replacing legacy systems can potentially reduce operational costs by 13 percent annually and boost revenue by over 14 percent.

InfoVision can help you forge a future of digital modernization and expand your limits with enterprise ADM services. Connect with us today to learn more!

Advancing telemedicine: Addressing gaps, creating possibilities

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The first two decades of the twenty-first century saw increasing adoption of Telemedicine and Remote Patient Monitoring. The pandemic irrevocably changed one aspect of healthcare delivery – it allowed Telemedicine to move from consumer paid one-off services to mainstream healthcare delivery integrated with Provider and Payer Systems. While after the pandemic, patient interest in telemedicine has tapered or reduced, providers now see telemedicine as a key option to avoid burn-out of clinicians rushing physically from one location to another. Telemedicine can often provide faster and better experiences to rural patients, reduce doctor and nurse burn-out, can bring novel therapies and clinical trials to say, cancer patients, among many other benefits.

Telehealth and RPM have enabled the industry to close the gap between patients, caregivers, and providers pivoting around advanced technologies. From virtual consultations to monitoring patients remotely, these innovations offer unprecedented access to quality care. While Telehealth and RPM are not entirely new concepts, many healthcare stakeholders have realized their potential in the recent years, enthused with the prospect of making healthcare accessible to everyone.

Moving to the mainstream

Most Telehealth services were “consumer-paid” in the pre-pandemic era; but the need to minimize in-person visits drove rapid integration of virtual consultations and RPM into payer and provider systems. This shift potentially solidified telehealth and RPM as mainstream healthcare solutions and are no doubt here to stay. In fact, a recent survey clearly indicates that the “online doctor consultations segment” of the digital health market is projected to add 13.7 million users between 2024 and 2028, approximating an 11.74% increase. Such radical shifts are happening across the globe. Many countries are jumping onto the Telehealth bandwagon and enacting supportive legislation in recent years.

For instance,

These laws and requirements supplement the existing regulations and authorizations needed to provide general health services.

The new healthcare model is here to stay

Telehealth and RPM bring a wealth of benefits to care delivery, benefiting everyone in the healthcare ecosystem.

Continuous monitoring of patients’ health conditions with the help of technologies such as data analytics, leading to timely interventions and improved management of chronic illnesses are one of the biggest attractions of this modern approach. The additional advantage of scalability ensures that healthcare services remain adaptable to the needs of a growing population, maintaining accessibility and effectiveness even during periods of heightened demand or resource constraints. In fact, according to a Rock Health report, 80% of people have used a telemedicine service at least once.

Furthermore, telehealth enhances the efficiency of healthcare delivery and democratizes access to medical expertise, particularly in underserved areas. Individuals with unique medical needs, such as those with disabilities, physical limitations, or age-related issues, greatly benefit from telehealth. It provides them with access to specialized care without the need for travel, ensuring they receive timely and convenient healthcare from the comfort of their homes. This is especially impactful for people residing in remote regions, such as rural Washington, where traveling long distances for basic healthcare services is often burdensome and time-consuming.

Why are people adopting Telehealth and RPM?

People are adopting Telehealth and RPM for several compelling reasons. These tech-driven solutions have proven highly effective in various therapies and indications. By closely monitoring a patient’s condition, these technologies ensure smooth transitions between different pharmaceutical services, preventing confusion and discontinuation. Moreover, telehealth facilitates collaboration with clinics, providing patients with a comprehensive view of their healthcare journey. This ensures continuity and a better understanding of their treatment progress, which is crucial for effective healthcare delivery.

Remote health monitoring of patients in their home is another big reason. After hospital discharge, patients often lose healthcare oversight, leading to avoidable readmissions and distress. Telehealth offers continuous supervision, preventing relapses into acute conditions and reducing hospitalizations, thereby reducing both financial strain and emotional stress for patients and caregivers. For example,  Lee Health, Florida, RPM has successfully reduced 30-day readmission rates by 50%, highlighting the tangible benefits of integrating telehealth into post-discharge care pathways.

The last few years have marked a profound transformation in healthcare delivery, with a surge in telehealth adoption. Compared to the pre-pandemic era, where telehealth visits accounted for merely 1%, current statistics show a remarkable increase, with approximately 14–17% of visits now conducted through telehealth channels. Furthermore, telehealth has played a pivotal role in bridging the gap between physicians and family members, who are crucial in supporting patient recovery. In response to this shift, healthcare facilities have significantly expanded their telehealth capabilities, such as telehealth training for professionals, mobile health applications, and more, reflecting a positive evolution in healthcare delivery.

The central role of data and technology

Data and technology form the backbone of Telehealth and RPM, driving innovation in modern healthcare delivery. Advanced monitoring devices and sensors enable the remote collection of patient data, facilitating continuous tracking of health status. Real-time communication platforms allow for seamless virtual consultations, supported by electronic health records that ensure easy access to patient information. Dedicated telehealth platforms provide the infrastructure for virtual care services, enabling appointment scheduling and e-prescriptions. One cannot overemphasize the role of analytics in elevating the overall telehealth experience for patients as well as providers. Digital health solutions monitor side effects, manage current medications and therapies, and track Patient-Reported Outcomes (PROs). Patients at home tend to be more comfortable, which can lead to more accurate reporting of symptoms and side effects. PROs are particularly valuable as they provide direct insights into the patient’s condition and experience. By collecting and analyzing this data, healthcare providers can gain a more accurate understanding of the patient’s health status and tailor treatments accordingly. This continuous feedback loop ensures that interventions are timely and effective, ultimately leading to better health outcomes.

Addressing challenges for improved effectiveness

While Telehealth has made good strides in healthcare delivery, there are a good number of challenges that still remain to be navigated. Providers, for instance, will have to negotiate complex issues to ensure effective and efficient remote care. From integrating disparate data systems to maintaining patient privacy, and from educating users to dealing with regulatory complexities, each challenge requires careful consideration and robust solutions. Here are some of the key obstacles healthcare providers face in the realm of telehealth:

  • Integrating data from various platforms and standalone applications is complex.  This requires sophisticated interoperability solutions to ensure seamless communication and a comprehensive view of patient health.
  • Educating both patients and healthcare providers on how to use telehealth technologies effectively is also a crucial and challenging task.
  • Data privacy remains a significant concern, necessitating robust security measures to protect sensitive patient information, thus ensuring trust.
  • Navigating the complex landscape of compliance and regulations, which vary by region, adds another layer of difficulty.
  • Reliable technological infrastructure is essential for telehealth success but costly to maintain, particularly in areas with poor internet connectivity.
  • Variability in reimbursement policies affects the adoption and sustainability of telehealth practices.  Ensuring patient engagement and providing ongoing technical support are critical for the smooth operation of telehealth services.

Providers must use solutions that can help navigate such tides. In a recent webinar, I exchanged my views with industry experts on the essential features required in today’s Telehealth solutions to ensure a superior patient experience. Watch the webinar on-demand to learn more.

Partnering with domain and technology experts is the key to building such robust solutions and minimizing roadblocks. At InfoVision, we are excited to partner with diverse healthcare businesses in building innovative, technology-driven solutions and contributing to the future of modern healthcare.

With over 25 years of delivery through 11 centers, InfoVision offers electronic data capture, lab informatics, and remote monitoring. Our digital and innovation services support customer, patient, and enterprise technology stacks, and are driven by 130+ technologists and cross-functional teams. We specialize in robust technology solutions for addressing key challenges in telehealth. We help providers reduce clinical trial time and costs with data warehousing, data analytics, collaboration, automation, and Artificial Intelligence (AI). Our end-to-end solutions adhere to HIPAA, GXP, CSV, 21 CFR Part 11, and other regulatory guidelines.

The future of digital healthcare

Telehealth and RPM are poised to become increasingly integral parts of the healthcare landscape, transforming the industry as we know it. With the rapid pace of advancement, these technologies are expected to seamlessly integrate into clinical workflows, promising a smoother experience for patients and providers alike. It’s crucial for healthcare stakeholders to seize this opportunity and harness the power of data to develop robust telehealth solutions. By doing so, we can make healthcare more affordable, accessible, and compliant than ever before.

InfoVision uses diverse technologies to transform healthcare delivery and improve patient outcomes. Connect with us today to learn more about how we can help you in building robust healthcare delivery systems.