The real AI opportunity is creating new value

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A majority of organizations now use AI in at the least one function — 88%, in response to McKinsey — but only 6% report significant enterprise-wide impact. This isn’t a failure of AI adoption. It’s a mirrored image of how organizations use AI.

To take an analogy from the past, the primary cars used horse carriages and easily added an engine — the identical frame, seating, and roads. It took an extended time for the chassis to be redesigned. The technology arrived before the considering caught up, and cars were reimagined.

Something similar is happening with AI. Companies are optimizing tasks without rethinking how they create value. According to the identical study, only 23% of organizations that use generative AI have redesigned their workflows for the new technology. The rest are constructing very fast carriages and haven’t yet learned how one can adopt a new business model.

The biggest impact of AI 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 4 stages of AI value

Peter Drucker famously defined efficiency as “doing things right” and effectiveness as “doing the proper things.”

Efficiency saves money — working faster and using inexpensive products for an existing pie — while effectiveness makes money by growing the entire pie. Both matter, but they require different organizational muscles.

Stages one and two (the primary two columns) within the above graphic of AI value are like factory work, which focuses on scalability, predictability, and high performance. These are cost-driven and measurable.

Stages three and 4 (the last two columns) are like laboratory work, which is built for experimentation, agility, and suppleness, and where new, unproven journeys are tested.

The factory mindset often wins in internal budgeting since it’s easier to see and quantify efficiency gains. It’s harder to see gains in effectiveness — the laboratory mindset — until an experiment succeeds.

The success of experimentation

Here’s an example of how experimentation can work: Tech entrepreneur Pieter Levels thought the one technique to discover whether an organization would work was to ship it — to experiment. Many projects later, several generate greater than $250,000 per thirty days combined. 

In one other example, IKEA deployed a chatbot “Billie” in 2021 to handle customer support. It resolved 47% of all customer inquiries, or 3.2 million interactions. Costs dropped, a classic stage one end result.

But 53% of inquiries were questions Billie couldn’t answer. IKEA saw this as an opportunity, not a failure. The company reskilled 8,500 call center staff as distant interior design advisers and built a wholly new sales channel.

The result: €1.3 billion in new revenue in 2022 from a channel that didn’t exist before the experiment.

Marketing versus the 4 horsemen

Advertising executive Rory Sutherland puts it bluntly in “The 4 Corporate Enemies of Innovation.” puts it bluntly. Most large organizations are concerned with cost-cutting and regulatory paranoia, not innovation. 

Finance, compliance, procurement, and HR departments — what he calls the “4 horsemen of the bureaucratic apocalypse” — are disproportionately punished when things go incorrect and due to this fact disincentivized from trying anything new.

Experimentation mandates should come from the marketing department, specifically marketing ops, since it’s accountable for future revenue, not the 4 horsemen departments.

Marketing ops already works on the intersection of knowledge, technology, customer signals, and industrial outcomes, and may run experiments quickly and inexpensively.

In the IKEA example above, solutions surfaced in a customer interaction log and thru experimentation, not within the boardroom. The people equipped to read that log and act on it were in marketing.

How to create AI value in your company

If you lately adopted AI, your organization is likely in stages one or two of using AI to create value, using the factory mindset, and pleasing shareholders with efficiency. The efficiency wave is a needed precondition for going further and creating more value with AI.

Stage three and 4 AI value can’t be planned. It have to be discovered through deliberate, fast, and cheap experimentation. A planned AI roadmap isn’t necessarily the reply — constructing the muscle to experiment at volume and follow the proper signals is.

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