With organizations spending significantly on AI, there is a need to realize important benefits from innovation with AI to realize meaningful operational improvements, strategic gains, and financial returns.
Factors that Do and Don’t Drive High Value
Let’s start with what isn’t driving value – headcount reduction, “ AI washing ” (with savings from other initiatives), creating a Chief AI Officer / Chief Data and AI Officer roles, providing vague project guidelines, innovation theater, etc. Not surprisingly, organizations getting the most from AI and innovation are those that clearly define what value means, who they hold accountable for delivering it, and how good they are at measuring what matters – both with hard and soft metrics, meeting near and long term business objectives, the ability to attract and retain top talent, progress on becoming a learning organization, competency at innovating for impact and new value creation, etc.
For the organization to be better positioned to achieve the important benefits from innovation with AI, here is what matters –
1. Be clear on what type of value you are trying to achieve
The great majority of respondents to a recent HBR survey say they are getting value from AI. How they define that value, however differs. For example, 14 % of respondents report receiving a great deal of value from AI, but only a slight return on investment in the technology. Similarly, 9 % report moderate value but substantial ROI. What’s going on here ? Put simply – Value is in the eye of the beholder. From these responses, it appears “ AI is doing what we want it to do ” – with some organizations seeking short-term ROI, while others are pursuing long-term transformation. An example of this different perspective is Prem Natarajan, EVP and head of enterprise AI, data, and staff technologies at Capital One, commented on why the bank is pursuing the latter objective – A focus on short-term value is why many enterprises never make the technology transformation that unlocks long-term value. Only the companies built on a modern tech stack and deeply invested in proprietary data will be in a position to transform their business by putting AI at the center of it. Because there are different objectives and metrics, leaders need to be clear on what they’re trying to achieve and why.
2. Seek value in both products and processes – even if the payoff isn’t immediate.
Most organizations focus on AI value from internal process improvements. Several executives indicated they were equally or more focused on AI in customer offerings. For example, Laura Matz, chief science and technology officer at Merck KGaA in Darmstadt, Germany (with operations in life science, healthcare, and electronics) said the company has both process-oriented and product-focused AI initiatives underway. Most of the products or new businesses that incorporate AI are relatively new and in the pilot stage, so it is difficult to value them until they gain additional scale. While AI-enabled offerings may require new ecosystems, new business models, or new technologies to be developed – with more than 350 years in business, Merck KGaA has a long-term perspective on innovation. Philippe Rambach, chief AI officer at Schneider Electric, the French energy technology multinational, described a deliberate dual focus. Internal AI applications deliver more immediate financial returns, helping employees work faster and better while providing enhanced support for customers. Customer-facing AI at Schneider represents a longer-term strategic play focused on capturing market share in evolving markets. Each requires different approaches to measuring success and different timelines for realizing value. AI can also increase a company’s value according to Julien Sauvagnargues, president of Olympus Corporation of Americas, whose medical endoscopes use AI to identify potentially cancerous polyps and reduce the administrative burden of documentation, indicated that companies need to think about AI offerings as a way to protect market share, as well as creating new value. We’ve determined if we don’t have AI capabilities in our products we will lose market share. Olympus also uses AI for personal productivity objectives but is not measuring the productivity benefits – yet.
3. Use all the tools in the AI toolbox.
Generative AI dominates media coverage, but it’s not what most organizations find most valuable. Among the survey respondents, 50 % said their companies get the most value from analytical AI, such as dynamic pricing or customer targeting. Rule-based AI, often found in anti-money-laundering systems, insurance underwriting, and healthcare clinical decision support, as well as in robotic process automation was a close second with 40 % of respondents indicating these tools produced the most value. Only 9 % choose generative AI and just 2 % agentic AI. Interestingly, agentic adoption is an indicator of AI value with adopters 22 % more likely to report achieving a great deal of value from AI overall than non-adopters, and more likely to be employing relatively mature economic value practices.
4. Adopt a framework or method for achieving value.
Whether custom-built or drawn from the management literature, a structured approach to moving AI from idea to production to measured value is often essential for creating value. Ally Financial, the American bank holding company, has a custom “ AI playbook ” that guides its business lines from use-case exploration through responsible production deployment. In another case, an electrical utility uses the “ stage gate ” approach – more common in R&D – to manage the same journey. A common approach is a digital product focus, which manages both internal and external AI offerings as products from conception to implementation and ongoing use. Shamin Mohamed, the EVP and chief information and technology officer of CarMax, the U.S. used-car retailer, credited a product orientation as the single most important factor in achieving value “ It brings a structure for proposing benefit, reviewing it over time, changing the business, and having stakeholders take accountability for value achieved ”. Any value framework must also include a means of getting your data ready for AI with 55 % indicting unready data as an impediment to value, in the HBR and other surveys.
5. Involve the CFO and the finance function in achieving and certifying value.
Most organizations assign AI value accountability to chief data/analytics/AI officer (38 %) or individual functional executives (35 %). While only 2 % assign it to the CFO, when they are responsible for achieving AI value, 76 % of organizations reported they achieved a “ great deal ” of value. That compares to 53 % under CIOs or CTOs, and only 32 % under functional executives. It should be recognized finance brings rigor, credibility, and organizational authority that other roles often lack. Several companies indicated their finance departments collaborate with technology executives to assess and certify value from AI. An example of this is Nimish Panchmatia, who oversees AI for Singapore-based DBS Bank, described their approach : Since 2021, the bank has reported the economic value delivered by data analytics and AI in its Annual Report. They track outcomes from A/B testing, quantify the difference as economic value, and each unit’s CFO validates their respective figures, which then roll up to an aggregate group-level number.
6. Train both users and executives on AI.
There is a two-tier challenge : 58 % of organizations haven’t trained employees in AI productivity and tool use, while 29% acknowledge leaders lack the understanding to drive AI value creation. Organizations that invest in both employee upskilling and leadership AI fluency see a 23 % advantage in value realization. Critically, employee adoption isn’t the barrier — only 13 % cite workforce resistance as an inhibitor to AI value. Employees aren’t resisting, but rather waiting for effective senior leadership and the removal of other barriers like missing value frameworks and unready data.
7. Follow an AI economic value maturity model.
These models predict meaningfully different levels of value achievement. This economic maturity model is based on three components –
- Simply put AI systems into production – pilots and experiments can be useful for learning but yield no economic value. The more production use cases the better.
- Another component of the maturity model is to assess the value of production use cases, ideally both before and after implementation.
- The third component of the maturity model is to aggregate value across the organization and report it, at least informally.
Assessing your AI Economic Maturity Model
Stage 1 : Only Unmeasured Pilots
The organization runs AI experiments but doesn’t measure outcomes.
Stage 2 : Production Without Assessment
In this stage, the organization has moved beyond pilots and has deployed AI in production for use in actual business practices – but aren’t evaluating the business impact it’s having – as a strategy to move past the pilot phase.
Stage 3 : Pre-Implementation Assessment
In this stage, organizations take a slightly more structured approach to thinking about the business impacts of AI implementation, but only on the front end of the process : They justify AI projects with ROI projections and business cases, but still don’t validate outcomes.
Stage 4 : Post-Implementation Assessment
Here, organizations measure individual AI use cases after deployment, a step that produces a surge in value and a major inflection point in the AI adoption journey. However, since many companies seem to get stuck here or becomes a bottleneck to continuing on the AI journey.
Stage 5 : Aggregated Annual Assessment
In this stage, organizations roll up AI value across the portfolio annually, convert benefits into an aggregated level of value for the organization, and make the results available informally throughout the firm. This enables leadership to compare total value from AI against total expenditures on AI and can also enable comparisons to revenue and profit growth or decline. This more formalized process creates a significant step up in value creation.
Stage 6 : Formal Reporting
In this stage, organizations report AI value to boards, investors, or public markets, which requires a high level of rigor and accountability of AI value measurement. This is the second and largest inflection point in creating value from AI.
Conclusion
Some executives expressed caution about external reporting. One noted concern that analysts might respond by pressuring the company to increase its dividend if strong AI returns were publicly disclosed. But the executives we interviewed generally felt that formal external reporting represents the highest level of AI economic maturity when it can be achieved responsibly. The path to AI value is not primarily a technical challenge — it is a management one. Every organization now has access to AI technology, but only some will deploy it in ways that generate real and measurable economic returns. These seven factors, and especially the maturity model, offer a practical roadmap for becoming one of them.
April 8, 2026 From HBR Review – by Thomas H. Davenport and Laks Srinivasan
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