The majority of organizations now use AI in at least one function: 88%, according to McKinsey – but only 6% report a significant impact at the company level. This is not a failure to adopt AI. It is a reflection of how organizations use artificial intelligence.
To make an analogy with the past, the first cars used horse-drawn carriages and simply added an engine: the same chassis, seats and roads. It took a long time to redesign the chassis. Technology arrived before ideas caught on and cars were reinvented.
Something similar is happening with artificial intelligence. Companies are optimizing operations without rethinking how they create value. According to the same study, only 23% of organizations using generative AI have redesigned their workflows for the new technology. The others build very fast carriages and have not yet learned to adopt a new business model.
AI’s biggest impact may not come from doing existing work faster, but from discovering entirely new ways to create value and generate revenue.
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The four stages of AI value
Peter Drucker famously defined efficiency as “doing things well” and effectiveness as “doing the right things.”
Efficiency saves money – working faster and using less expensive products for an existing pie – while effectiveness saves money by growing the entire pie. Both are important, but require different organizational muscles.
Phases one and two (the first two columns) in the AI value chart above are like factory work, focusing on scalability, predictability, and high performance. These are cost-driven and measurable.
Phases three and four (the last two columns) are like laboratory work, built for experimentation, agility and flexibility and where new and unproven paths are tested.
The factory mentality often wins in internal budgeting because it is easier to see and quantify efficiency gains. It is harder to see improvements in effectiveness – the laboratory mentality – until an experiment is successful.
The success of the experiment
Here’s an example of how experimentation can work: Tech entrepreneur Pieter Levels thought the only way to find out if a company would work was to launch it: experiment. Many projects later, many generate more than $250,000 per month combined.
In another example, IKEA has implemented a “Billie” chatbot in 2021 to manage customer service. It resolved 47% of all customer requests, or 3.2 million interactions. Costs have decreased, a classic result of the first phase.
But 53% of the requests were questions that Billie couldn’t answer. IKEA saw this as an opportunity, not a failure. The company retrained 8,500 call center agents as remote interior design consultants and created an entirely new sales channel.
The result: €1.3 billion in new revenue in 2022 from a channel that didn’t exist before the experiment.
Marketing against the four horsemen
Advertising executive Rory Sutherland puts it bluntly:The 4 corporate enemies of innovation.” he says it bluntly. Most large organizations are interested in 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 disproportionately punished when things go wrong and therefore disincentivized from trying something new.
Testing mandates should come from the marketing department, especially marketing operations, because it is responsible for future revenue, not from the four horsemen departments.
Marketing operations already works at the intersection of data, technology, customer signals and business outcomes and can run experiments quickly and cost-effectively.
In the IKEA example above, solutions emerged in a log of customer interactions and through experimentation, not in the boardroom. The people who could read that log and act on it were in marketing.
How to create AI value for your business
If you have recently adopted AI, your organization is probably in the first or second phase of using AI to create value, using the factory mentality and satisfying shareholders with efficiency. The wave of efficiency is a necessary precondition to go further and create more value with artificial intelligence.
The AI value of phases three and four cannot be planned. It must be discovered through deliberate, fast, and inexpensive experimentation. A planned AI roadmap isn’t necessarily the answer: developing the strength to experiment at volume and follow the right signals is.
