The majority of organizations now use AI in at least one function – 88%, according to McKinsey – but only 6% report a significant impact on the overall business. This is not a failure of AI discovery. It is an indication of how organizations are using AI.
To take a classic analogy, the first cars used horse-drawn carriages and simply added an engine – same frame, seats, and roads. It took a long time for the chassis to be redesigned. Technology came before thinking, and cars were reimagined.
The same thing happens with AI. Companies develop jobs without rethinking how they create value. According to the same study, only 23% of organizations using productive AI have restructured their flow of new technology. Some build very fast carriages and haven’t learned how to use a new business model.
The biggest impact of AI may not come from making existing work faster, but from finding entirely new ways to create value and generate revenue.
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Four stages of AI value
Peter Drucker defined efficiency as “doing things right” and success as “doing the right things.”
Efficiency saves money – working faster and using the least expensive products in the existing pie – while efficiency makes money by growing the whole pie. Both are important, but they require different organizational muscles.
The first and second stages (the first two columns) in the above diagram of the value of AI are similar to factory work, which focuses on the ability to measure, predict, and optimize performance. This is cost driven and measurable.
The third and fourth sections (the last two columns) are like laboratory work, designed for testing, agility, and flexibility, where new, unproven trips are tested.
The factory mindset is often successful in internal budgeting because it is easy to see and measure efficiency gains. It is very difficult to see the benefits in efficiency – the attitude of the laboratory – until the experiment is successful.
Test success
Here’s an example of how testing can work: Tech entrepreneur Pieter Levels thought the only way to find out if a company would work was to ship it – testing. Many projects later, few generate more than $250,000 per month combined.
In another example, IKEA used the chatbot “Billie” in 2021 to manage customer service. It resolved 47% of all customer inquiries, or 3.2 million interactions. Costs are down, the result of the first phase.
But 53% of the questions were questions that Billie could not answer. IKEA saw this as an opportunity, not a failure. The company also retrained 8,500 call center employees as remote in-house consultants and built an entirely new sales channel.
The result: €1.3 billion in new revenue by 2022 from a channel that didn’t exist before the test.
Marketing is compared to the four horsemen
Marketing executive Rory Sutherland puts it bluntly in “The 4 Corporate Enemies of Innovation.” it’s a mouthful. Most large organizations are concerned with cost cutting and regulatory paranoia, not innovation.
Finance, compliance, procurement, and human resources departments — what he calls the “four horsemen of the bureaucratic apocalypse” — are unfairly punished when things go wrong and therefore disempowered from trying anything new.
Test instructions should come from the marketing department, especially ops, because you are responsible for future revenue, not the four horseman departments.
Sales machines are already working at the intersection of data, technology, customer signals, and trading results, and can run tests quickly and inexpensively.
In the IKEA example above, the solutions came from customer interaction logs and evaluations, not from the boardroom. People equipped to read that log and work on it were in the market.
How to create AI value for your company
If you’re a new AI adopter, your organization is probably in the first phase or two of using AI to create value, implement an industry-leading approach, and delight shareholders with efficiency. A wave of efficiency is a necessary condition for moving forward and creating more value with AI.
The AI value of the third and fourth stages cannot be edited. It must be detected by a deliberate, rapid, and inexpensive test. A planned AI roadmap is not the answer – building muscles to evaluate volume and follow the right signals.
