Digitalization is accelerating, and more data is being generated than ever. In this new era ruled by data, enterprises can now use AI as a critical enabler to identify trends and glean insights from this vast pool of data to achieve customization, personalization, operational efficiency and gain a competitive advantage.

On the organizational side, technology executives are now racing to adopt data-driven decision making and agile mindsets and fuel their AI-led digital transformations.

The huge volumes of available data, affordable data storage, and maturity of cloud processing have led to the growth of machine learning (ML), a subfield of AI. ML helps businesses solve complex business problems faster and more effectively and drive business outcomes directly impacting the enterprise’s bottom line.

The growing number of investments in AI and machine learning (ML) reflect the foundational shift businesses are undergoing to drive innovation, reduce risks, and break the process complexity gridlock to deliver faster value.

Embrace the AI Promise with Realistic Expectations.

AI has massive potential. With adequate understanding of the AI capabilities, CIOs can maximize business value by exploring the multiple, real-world AI experiences to align business priorities with near-term opportunities.

Thus, it’s important for business leaders to understand the different types of AI that exist today and make best case for their real-world applications.

Broadly speaking, AI falls under three categories:

  • Weak AI: Also known as narrow AI or artificial narrow intelligence (ANI), weak AI has a narrow scope and performs only one type of task really well. It doesn’t replicate or mimic human intelligence but simulates human behavior with the parameters of its learning algorithms defined by human interference.

 Examples of weak AI include voice-activated assistance, self-driving cars, image and facial recognition, disease prediction tools, email spam filters, weather applications, and advanced chess programs. 

  • Strong AI: Also known as general AI or artificial general intelligence (AGI), strong AI has a complex algorithm that makes it mimic human-like intelligence to act in different situations, but without human intervention. Strong AI includes the ability to plan, reason, learn, make decisions and solve puzzles. Though currently it’s the theoretical next level of AI, it has somewhat evoked the fear among people that they might lose their jobs to smart machines if it becomes a reality.

Examples of strong AI include AI-powered games and science fiction movies.

  • Super AI: Though a vague concept at this point, artificial superintelligence (ASI) involves machines becoming self-aware enough to outsmart human intelligence and behavioral ability — from creativity, to general wisdom, to problem-solving.

Such machines have been imagined to be capable of all abstractions, which are impossible for humans to think. It’s being speculated that ASI would have faster ability to process and analyze data accurately to make far superior decision-making and problem-solving capabilities.

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AI Driving Measurable Gains in Business Value

Human brain helps us learn and act from experience. Similarly, machines identify patterns from the historical data and make smart decisions and their cognitive abilities significantly improve over time. This has led to businesses increasingly adopt AI to rethink their core processes and models.

According to the World Economic Forum, the universe contains 40 times more bytes than observable stars in the sky. Amid volatility, uncertainty, complexity, and ambiguity (VUCA) environment, businesses need to adopt a more logical and rational approach to decision making. And, that can be started by modernizing the way to leverage data.

AI is just the right solution.

The Cusp of AI-led Business Innovation

AI taps into the power of this massive amount of data, including dark data, at an unimaginable scale and has emerged as a critical enabler of business innovation. The maturity of cloud processing has also contributed in the growth of AI.

However, many research reports and surveys have indicated that the gap between enterprises leading the AI deployment and those grappling to capitalize on it or caught up in the early stages of exploration is widening. But, this isn’t surprising – not only the technical challenges roadblock achieving AI-led impact at scale, but also company-wide change management is critical to the success of AI deployment.

The PwC report – 2020 AI Predictions has somewhat similar findings. It reveals that although 90% of executives are optimistic about the potential of AI, many business leaders are facing a “reality check” in its implementation, which is proving to be more of a challenge than expected.

While experimenting with AI in pockets establishes the potential benefits of AI, it’s critical to move past experimentation to leverage its full potential.

The businesses that are witnessing AI multiply value for them have been operating with the right data, strategy, talent and use cases and focusing on solving the critical business problems. Such organizations have successfully taken the lead in key areas of their AI-led digital transformation and revenue growth to outsmart their competitors.

Reduced Costs, Increased Agility and Sustainable Competitive Advantage   

Early adopters are more likely to reap sustainable competitive advantages with AI – a powerful force of change. According to a Deloitte Automating with Intelligence study, organizations deploying intelligent automation initiatives expect a 24% average cost reduction over the next three years.

AI helps businesses add intelligence throughout their core capabilities and processes to transform insights into action, unlock new levels of innovation and business agility, and yield sustainable competitive edge.

AI Interest Bubble?

While companies seem to be staggeringly investing into AI, the moot question is – are we really just in the latest bubble of AI interest? Perhaps yes.

Many companies have been leveraging AI only for building chat bots or voice-based assistants or running some ad hoc projects on one or more business functions. Simply put, moving past the proof-of-concept (POC) to production stage has been a big challenge for most organizations that have adopted AI but are failing to tap its true potential.

Productionalize enterprise-wide AI projects at speed.