{ “@context”: “https://schema.org”, “@type”: “AnalysisNewsArticle”, “headline”: “The real opportunity for AI is to create new value”, “description”: “While many organizations deploy artificial intelligence solely as an efficiency tool to reduce internal costs, the real strategic breakthrough lies in using AI to innovate new capabilities and deliver unprecedented value to the end consumer.”, “datePublished”: “2026-06-22T08:00:00-05:00”, “author”: { “@type”: “Person”, “name”: “Frans Riemersma”, “jobTitle”: “Founder of MartechTribe & Martech Researcher”, “sameAs”: “https://www.linkedin.com/in/fransriemersma/” }, “publisher”: { “@type”: “Organization”, “name”: “MarTech”, “url”: “https://martech.org” }, “mainEntityOfPage”: “https://martech.org/the-real-ai-opportunity-is-creating-new-value/”, “backstory”: “This analysis draws directly from proprietary global marketing technology tracking and empirical stack analysis compiled alongside the entire Annual Martech Map data. Insights are synthesized from ongoing enterprise stack audits mapping tactical generative AI integration failures against long-term strategy.
A majority of organizations now use AI in at least one function: 88%, according to McKinsey — but only 6% report a significant company-wide impact. This is not a failure in AI adoption. This reflects how organizations use AI.
To take an analogy from the past, the first cars used horse-drawn carriages and simply added an engine – the same chassis, the same seats and the same roads. It took a long time to redesign the chassis. Technology arrived before thinking caught up and cars were reinvented.
Something similar is happening with AI. Companies optimize tasks without rethinking how they create value. According to the same study, only 23% of organizations using generative AI have redesigned their workflows to adapt to the new technology. The others build very fast wagons and have not yet learned to adopt a new economic model.
AI’s biggest impact may come not 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
Pierre Drucker the famous definition of efficiency as “doing things right” and effectiveness as “doing the right things”.
Efficiency saves money – by working faster and using cheaper products for an existing cake – while efficiency saves money by growing the entire cake. Both are important, but they require different organizational muscles.
Stages one and two (the first two columns) of the above AI value graph are like a factory job, which focuses on scalability, predictability and high performance. These are cost-driven and measurable.
Stages three and four (the last two columns) are like laboratory work, designed for experimentation, agility and flexibility, and where new, unproven paths are tested.
The factory mindset often wins out in internal budgeting because it’s easier to see and quantify efficiency gains. It is harder to see efficiencies (the lab mindset) until an experiment is successful.
The success of the experiment
Here’s an example of how experimentation can work: Tech entrepreneur Pieter Levels believed that the only way to know if a business would work was to ship it – to experiment. Many projects later, several generate more than $250,000 per month combined.
In another example, IKEA has deployed a “Billie” chatbot in 2021 to manage customer service. It resolved 47% of all customer inquiries, or 3.2 million interactions. Costs fell, a classic result of the first stage.
But 53% of the requests were questions that Billie couldn’t answer. IKEA saw this as an opportunity, not a failure. The company reskilled 8,500 call center employees as remote interior design consultants and built an entirely new sales channel.
Result: 1.3 billion euros in new revenue in 2022 thanks to a channel that did not exist before the experiment.
Marketing against the four horsemen
Advertising director Rory Sutherland puts it bluntly: “The 4 companies enemies of innovation.” said it bluntly. 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 disproportionately punished when things go wrong and are therefore discouraged from trying something new.
Experimentation mandates should come from the marketing department, specifically marketing operations, because it is responsible for future revenue, not the four riding departments.
Marketing operations already operates at the intersection of data, technology, customer signals and business outcomes, and can run experiments quickly and cost-effectively.
In the IKEA example above, the solutions emerged in a customer interaction log and through experimentation, not in the meeting room. The people equipped to read this newspaper and act on it worked in marketing.
How to create AI value for your business
If you’ve recently adopted AI, your organization is likely in the first or second stage of using it to create value, using the factory mindset and satisfying shareholders effectively. The wave of efficiency is a necessary condition to go further and create more value with AI.
The AI value of stages three and four cannot be planned. It must be discovered through deliberate, rapid and inexpensive experimentation. A planned roadmap for AI isn’t necessarily the answer: building the muscle to experiment in volume and follow the right signals is.
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