AI has the potential to drive your business to new levels of efficiency; levels that are beyond the scope of the digital technologies that currently drive Digital Transformations. This article aims to explain why that is so and how you can make it happen.
The biggest organizational change driver today is the initiative known as Digital Transformation. In short, the goal of Digital Transformation is to streamline businesses and organizations.
The basic idea of digital transformation is to change jobs and business processes based on improvements driven by digital technologies. And, in that way, you make your company more efficient.
It is, therefore, obvious to think of AI as a change driver that is a part of a Digital Transformation process.
But, AI works with different dynamics because the core of the AI changes is different than with digital technologies. AI drives change based on knowledge, not data. This might seem like just an academic statement, but it makes all the difference, and this article will tell you why that is so.
The basic premise of Digital Transformation is that technology is the driver of change. Hence, the name “Digital” and “Transformation.” The idea is that technology has the potential to drive exponential change as opposed to organizations that experience build-in resistance towards changes.
This concept can be illustrated in the figure below. The technological changes will typically have an S-shaped development. The smartphone is an obvious example of a technology with the potential for creating a tech-driven change.
Apple’s iPhone has been on the market for more than ten years and is an example of a technology that has grown through a life cycle of change that follows an S-shape.
The first-generation iPhones were revolutionary but still had many shortcomings. They were simply not utilizing the full potential of the platform, so the technology change curve started out a bit flat.
Smartphones were used, but they were slow, but it was difficult to check emails and there were few apps, etc.
The significant improvements came with the introduction of the iPhone 4, 5, and 6, which you could say lies on the steep part of the change curves. The smartphone’s overall hardware and software potential was unfolding, the Apps ecosystem was growing, and it became clear what use the technology had for a massive mainstream audience.
Today's new iPhones have all been better than the previous ones, but the changes are small and relatively minor improvements. The iPhone as a technology is now at the top of the technology change graph. Thus, a three-phase curve follows:
Calibration: The technology is introduced, but must first find its true potential.
Scaling: The technology potential is becoming apparent, it is widely used.
From Revolution to Evolution: Marginal improvements are constantly occurring, but they are ever less significant The logarithmically organizational changes.
The dynamics of organizational change over time follow another momentum than tech changes does.
The basic idea here is that over time, it will be harder and harder to introduce significant change in an organization.
There is build in resistance in lots of areas such as:
The Corporate Management
Basic human behavior
Organizational power positions
Current workflows and processes
IT system design
This does not mean that organizational change is not possible at all. It just means that any change will happen at a logarithmic rate and not at an exponential rate that is possible with technology-driven changes.
There are many studies that confirm the above dynamics. The most well-known is Martec’s law. The potential of the Digital Transformation.
The difference between the speed of the changes that technology can create and the ones that can be created organizationally is called Digital Transformation. This gap is marked with shades of blue in this figure.
The figure has two key points in relation to Digital Transformation. Namely, there are two levels of Digital Transformation:
The unachievable Digital Transformation potential
The actual Digital Transformation potential
The last part is the transformation that is possible with Digital Transformation. What you can see is that the potential lies between what technology can drive, and the rate of change that organizations under normal circumstances would be willing to implement.
The cracked travel company Thomas Cook is an example of how to be caught in a fatal way of not realizing your Digital Transformation potential.
Thomas Cook has traditionally sold travel products in England through a large number of local store offices located in High Street shops all over the UK.
One of the problems with Thomas Cook was that management was fully aware that the business model was outdated. They knew that they should convert much more of their sales into online channels.
But, the culture of buying trips in a store was so central to the company’s DNA that the management didn’t dare to challenge it. And in this way, organizational resistance contributed to the inability to drive significant Digital Transformation.
This is not just the case of Thomas Cook. In general, the biggest obstacle to experiencing exponential change is not due to a lack of access to the newest technology. The core problem is a company’s ability to adapt to the latest technological realities. Data is not the best tech change driver
The other major obstacle with the concept of Digital Transformation is that change is based on data. We are all taught that data is the king, the more data, the better, etc. So that data in itself is a problem that might sound a bit strange. But it is never the less very true. Here is why.
Data is the underlying factor driving the change in the Tech part of the graph. The core concept of a Digital Transformation is to redesign jobs and processes and workflows in a data-driven manner. If you look at what’s driving the Tech changes, it’s information and data.
The illustration below is showing different levels of enriched knowledge. It is known as the Knowledge Pyramid.
It is a pyramid because the upper layers are based on the lower ones. More knowledge is added to each step as you go up. The pyramid starts with data located at the bottom.
Data: A collection of facts in raw or in unorganized form.
Information: Organized and structured data that has been cleared of errors. It can, therefore, be measured, analyzed, and visualized.
Knowledge: Learning is the central component of the knowledgeable part. Here you learn on the basis of insights and understanding of data and information.
Wisdom: The last level is wisdom. Here, Reflection is the central component, as well as being an action-oriented stage.
In short, you can say that data and information describe the world as it was. The concepts of Knowledge and Wisdom are looking forward and orienting themselves to what we can do now and in the future.
And exactly this fact is the most central reason why AI as a change driver has more change potential than Digital Transformation projects could have. Because AI bases change on knowledge, not data. AI can create Knowledge-based changes.
The basic premise of Digital Transformation projects is that they are based on data and information that are backward in their orientation.
AI-driven improvements are based on knowledge, not data or information. And it makes a huge difference in two key areas:
Knowledge can also change how organizations function without reaching the constraints that today prevent them from getting the S-curve of a tech-driven change.
Knowledge can drive tech changes with a higher potential for change than information-driven change can.¨ AI will change organizations in radical new ways.
The biggest problem for the Digital Transformation’s ability to create change is the inertia of the organization that one wants to change.
As mentioned earlier, there is not one single source of change resistance in an organization. It comes from habits, culture, employee competency gaps, unions, existing processes and workflows, permanent job descriptions, general human change resistance, internal political power struggles to name just a few. So, the core reasons why a Digital Transformation process reaches its limit will be on the organizational part, not the tech part.
Thomas Cook should have closed more stores and focused more on online sales. This is just one example of technology (e-commerce) changing the organization (closing sales outlets), but that didn’t happen (fast enough). Thus, Thomas Cook was affected by both resistance to change in their organization and failure to adapt to new technological opportunities. So, what makes changes based on knowledge different?
AI creates organizational change by challenging the basic premises on how tasks are solved in an organization. The ultra-short explanation is that AI enables you to automate knowledge and automate decision-making.
This means that the AI is going to solve the tasks currently carried out by either jobs, processes, and IT systems.
It is the sum of such tasks that AI will take over in our companies. As well as the increasingly complex tasks that AI would be able to carry out, which will enable AI to create organizational change at a level that would never be possible with digital technologies.
An example of this might be a voice-based chatbot that can pick up the phone and keep a qualified conversation with someone at the other end. For example, the booking of a table in a restaurant (see this video for just such a case).
It is the sum of such tasks that AI will take over in our companies. It will also be able to perform increasingly complex tasks that will drive the change.
The dynamic of such factors will enable AI to create organizational change at a level that would never be possible with digital technologies. It is called Knowledge-driven change potential in the graph above.
The ways how AI will changes how an organization runs means that the organizational resistance factors that apply to Digital Transformation changes will no longer be as effective.
The main reason for this is that AI features will perform a specific task that complements what we do already in an organization. Like the example with the phone and bookings.
So, we will not see AI be used as a driver to implement fundamentally new systems and workflows. AI will be used to perform specific takes as in this example. Just better and more efficient than the current way. And this makes it more challenging to resist than if you were to redesign your workflows and processes. A new level of Tech change.
There is another reason why AI-driven change is at a different level than Digital driven change. The quality of the tech level that one can achieve with technology-driven knowledge is fundamentally higher than the level one can reach with data-driven technology.
The difference is illustrated in the figure below. The graph of data-driven change follows the flow we saw during the review of the digital transformation possibilities. Thus, an S-shape with decreasing value.
AI as a Tech Change Driver
An example of knowledge-driven technology could be the technology behind self-driving cars. The software behind them is based on advanced AI algorithms. And the changes that they will be able to drive are at a level of higher change potentials than data-driven technologies.
AI Will Drive a New Level of Business Change
It is the sum of both technological advancements and the new organizational opportunities that AI provides that will collectively drive AI’s opportunities to create organizational change.
The key points are that AI-driven changes will not be affected by the same obstacles experienced with Digital Transformation changes. And at the same time, the level of technological opportunities that open with AI will be at a level that is far above what is possible with conventional IT solutions.
Overall, this means that one can expect a level of advancement similar to that described in the figure below.
The AI Business Potential vs Digital Transformation
Thus, a level that must be expected to be higher than what can be achieved with Digital Transformation processes. This is because the dynamics of AI-driven takes place without the organizational constraints of a Digitale Transformation. And because the technological potential is simply greater.
The big question then is how will this happen in practice, and how does AI change unfolds from a strategic perspective.