A new chapter of business transformation is under way. We are entering the AI era.

The potential impact of generative AI on business is grabbing all the headlines, but it is not the whole of the AI story. I have been a business and strategy consultant for over 30 years and for the past ten, I have been focused on the application of data and analytics. In recent years, the use of data analytics to drive business transformation has been revolutionized by various forms of AI.

I don’t use the word ‘revolution’ lightly. I believe that AI represents a technological breakthrough comparable to the development of the internet. We are only just beginning to see a glimpse of all the disruptions it will enable but one of those, I am certain, is a radical disruption of management consultancy itself. From this moment, all our efforts to improve process management and drive ever-greater business efficiencies will look outdated. We now have the power to genuinely transform our businesses: to reimagine the fundamentals of our operations and find previously unthought-of business opportunities.

The reason for this is both complex and simple. We have access to unimaginable amounts of new data about consumer behavior and market trends – and AI-driven technologies are making it possible to explore and manipulate this data in ways no human or machine previously could. That addresses one of the great problems of recent years, when a lot of people fell for the notion that “data is the new oil”. They wasted a great deal of money accumulating vast data lakes, in which many of them drowned.

The problem has been twofold: the mistaken assumption that all data is valuable, and the fact that, until the advent of AI, there was too much data for humans to handle. Data warehouses filled up with massive volumes of data that might or might not have had value. Business leaders failed to decide what answers they wanted from data; data experts focused on the technical aspects of managing data, and were not consulted about where valuable insights might be found. The old approach can be described as ‘data up’. The new approach must be ‘value down’.

From scientific management to big data

In my latest book, The Fifth Phase, I argue that we have entered a new stage of business transformation. Management consultants like me have been trying to make businesses more effective for over a century. We can put a date on when that started: 1911, with the publication of Frederick Winslow Taylor’s The Principles of Scientific Management. Taylor’s ideas used the scientific method to improve new industrial processes, which had developed largely by rule of thumb. In every instance, Taylor would use observation and measurement to calculate the “one best way” of carrying out a task. The aim, of course, was to improve the efficiency of these industrial processes – and, in general, it worked.

With the arrival of readily-available and affordable computing power in the latter half of the 20th century, business moved on to the second phase of transformation, which I have called Taylorism with computers: Material Requirements Planning (MRP) and its more sophisticated successor, Manufacturing Resource Planning (sometimes referred to as MRP II, but often still as MRP). The laudable aim was to ensure that manufacturers had exactly the right inventory needed, at exactly the right time, to produce the precise amount of goods they had sold. Then, as new software allowed businesses to computerize key functions, Gartner coined the term Enterprise Resource Planning (ERP) to reflect the new enterprise-wide scope of these systems.

The third phase of business transformation was characterized by various movements aimed at further improving the efficiency of existing processes, including Total Quality Management, Lean Manufacturing, Six Sigma and Lean Six Sigma. The central philosophy of all these approaches was to take an existing business and make it more streamlined and efficient. They did not contemplate reimagining those businesses and finding ways they could be radically better – or completely different.

Around the turn of the millennium, the development of the internet and mobile telephony began to change everything. There wasn’t much point in being the most efficient dinosaur on the planet when the future was being won by tech-driven disrupters. This was the start of the fourth phase: big data. The belief (or hope) was that the way to find exciting new business opportunities was to accumulate data – any data. Companies boasted that they would be ‘data driven’, without explaining exactly what this meant. I fear that this phase will go down in history, in general, as a waste of time and money.

Insight-led and data-enabled

The fifth phase of business transformation will be insight-led and data-enabled. Data experts and business leaders will work together to decide which insights have the power to potentially revolutionize their business and will test packets of data to see if they have the potential to deliver the desired insight, using hugely powerful AI tools. For example, it is now possible to use machine-learning methodologies and natural language processing to analyze huge volumes of near real-time social-media interactions to discover what consumers are talking about and what most interests them.

It is possible to create virtual representations of our core business models and play with a truly astonishing number of variables, using market data, to see which optimizations produce the most profitable results.

And it is possible to model incredibly complex chemical and biological processes. The US company Biovista is using AI to plot the known pharmacology of some 50 million chemicals against every possible clinical outcome. This could revolutionize drug development and other aspects of medical care. It has already revealed ways that existing drugs could potentially be used to treat entirely unexpected disease, and led to the development of entirely new drugs. An analysis of this complexity was impossible prior to AI.

The fifth phase offers us the power to reimagine our markets and our businesses. We need to stop seeing data as the province of IT, and start seeing CIOs and data analysts not as service providers, but as essential partners in devising radical new business strategies. ‘Process management’ will never be the same again.