Artificial Intelligence (AI) is top of mind for leading corporations these days – 96.4% of top executives reported earlier this year that AI was the number one disruptive technology that they were investing in, up from 68.9% just two years ago. In addition, 80% of these executives identified AI as the most impactful disruptive technology, up from 46.6% two years earlier.
Yet, for many organizations, Artificial Intelligence remains a mystery. For specialists, AI implies a very specific connotation in terms of intelligence demonstrated by machines, in contrast to the more common usage of AI which encompasses all varieties of machine assisted learning, most notably machine learning, deep learning, and natural language. For the sake of this discussion, we will assume the broadest definition of AI. As corporations struggle to understand the applications and benefits that AI can deliver for their organizations, firms have established AI Centers of Excellence, AI Labs, and other sandboxes for piloting AI capabilities tied to business use cases.
So, how can we demystify AI to deliver measurable business value? Rob Thomas, a senior IBM executive who is General Manager of IBM Data and AI, which includes Watson, has a few ideas. He has outlined his framework for AI adoption in a newly published report from O’Reilly Media, “The AI Ladder”. In the report foreword, O’Reilly Media Founder and CEO Tim O’Reilly observes, “Everyone is talking about ‘AI’ these days, but most companies have no real idea of how to put it to use in their own business”. O’Reilly emphasizes the cultural change in mindset that accompanies adoption of any new technology approach, noting that “each new technology revolution breeds new business leaders” who let go of old assumptions about the way things work. O’Reilly notes, “only later do companies realize how much they might need to change their business model to truly make use of new capabilities”. IBM’s Thomas observes, “AI is not about executing a single business project – it’s about changing an entire business culture. It’s about creating a culture of iteration, experimentation”.
The report employs the metaphor of the “AI Ladder” to describe a series of stages that a firm must pass through to become AI-enabled and realize measurable business value. The concept of the AI Ladder is premised on the notion that organizations require a prescriptive approach to understand where they are in their AI maturity. By diagnosing and understanding the stage of maturity of each organization, a firm can then employ the AI Ladder as a framework for outlining the steps and capabilities that will guide the organization toward realization of the benefits that result from machine and human augmentation. The basic tenets of the AI Ladder can be summarized as:
- Start with the business problem that you are attempting to address
- Understand your data requirements – these are the foundation for AI success
- Develop the right skills to leverage AI capabilities
- Focus on algorithmic trust and data integrity to ensure credibility
- Recognize the need for cultural and business model change.
A central message of The AI Ladder is that AI success depends upon data, and effective data management provides the fundamental building block for AI enablement. It was only with the advent of Big Data in recent years, that proliferating sources and volumes of data could be combined with massive computing power. This has helped AI emerge from decades of nascent experimentation, as a foundation of data collection, organization, and analysis provides the foundation upon which AI capabilities and algorithms are deployed. The AI Ladder describes the challenges that corporation face in organizing their data to enable AI when confronted by issues including lack of data, too much data, and lack of quality data. It’s long been understood that organizations typically invest 80% of their efforts in preparing their data so that it can be used productively. Thomas echoes the critical linkage between successful data management and effective AI enablement, noting “the vast majority of AI failures are due to failures in data preparation and organization”.
New generations of tools and technologies are helping reduce the amount of time that corporations now spend on data preparation, with the result that firms can focus efforts on analysis and business results. Companies are moving to modern data architecture approaches that are enabling simpler data collection and access. The advent of Cloud Computing has accelerated the adoption of AI through the migration of data assets to the Cloud. By renting rather than purchasing data storage capabilities, corporations are transforming the economics of data management, and realizing increases in speed and efficiency. They are no longer responsible for managing data access and security, no longer required to make large capital investments in computer hardware purchases, only pay for and access their data as they need it, and as a result, accelerate adoption of Big Data and AI.
So, what does this all mean for business? A 2017 study by PwC, reported that “Global GDP will be 14% higher in 2030 as a result of AI – the equivalent of an additional $15.7 trillion. This makes it the biggest commercial opportunity in today’s fast changing economy”. A 2017 Gartner study argues that Artificial Intelligence will create more jobs than it eliminates, and that by 2021, “AI augmentation will generate $2.9 trillion in business value and recover 6.2 billion hours of worker productivity”. IBM’s Thomas suggests that although today there is only a 4%-8% adoption rate of AI within the corporate world, within the next 18-24 months we are likely to see this rate explode t0 80%-90%. This would suggest that AI is rapidly reaching the moment of critical mass that will lead to business adoption and business value. IBM’s Thomas concludes, “AI is at execution state”. Have you launched your AI Center of Excellence? It appears that the time to act is now.