You Don’t Have an AI Problem. You Have a Why Problem.

When Citadel’s Ken Griffin recently said that generative AI has yet to deliver meaningful alpha for hedge funds, many saw it as proof that AI’s promise had been overstated. But as our CEO, César D’Onofrio, pointed out in a recent article, the issue isn’t the technology, it’s how we use it.

Across industries, companies are wrestling with the same paradox. AI adoption has become mainstream, yet in most cases, tangible results remain elusive. According to McKinsey’s State of AI 2025 report, 78% of organizations already use AI in at least one business function, but only 21% have redesigned workflows to truly capture value. The data confirms what many already know: the technology works, but the impact doesn’t always follow.

At Making Sense, we see this reality come up everywhere: in conversations at events, in discovery meetings with new clients, and in the early stages of advisory work. Many companies jump into AI because competitors are doing it, not because they have a clear purpose. The excitement is real, but the “why” and “how” are often missing from the conversation.

From experimentation to execution: Where most companies get stuck

Many organizations approach AI as an endpoint, not an enabler. They start with a solution in search of a problem, experimenting with new tools or models before defining what business outcome they want to achieve.

We often see this in projects that aim to “use AI” to speed up manual tasks, like automating the entry of data into spreadsheets, without asking why those spreadsheets exist in the first place. In many cases, the real solution isn’t smarter automation, but rethinking the process entirely, replacing spreadsheets with integrated systems that eliminate the need for manual work altogether.

Others underestimate the invisible groundwork, cleaning, structuring, and governing data, without which even the most advanced model fails to deliver value. And almost every company underestimates the human side of adoption.

These challenges tend to manifest in three patterns we’ve repeatedly seen in client engagements:

Solution-first thinking. Teams pursue AI initiatives to “stay ahead” rather than to solve a clearly defined business challenge. The result is isolated pilots that never scale or connect to the company’s real workflows.

Data without direction. Many teams discover that before talking about AI, they first need to fix how data flows through their organization.

Lack of human alignment. AI success depends on people’s trust and adoption. When employees don’t understand how AI supports their work, resistance grows and progress stalls.

These gaps explain why so many AI initiatives fail to move from experimentation to execution. The companies that do succeed take a different path. They begin with a clear understanding of what problem they are trying to solve and how data and people will enable it. In other words, they start with strategy, not technology.

What changes when AI meets human judgment

The turning point comes when companies stop treating AI as a replacement for human intelligence and start using it to amplify it.

AI can process data faster, scale operations, and uncover patterns that humans might overlook. But context, empathy, and creativity remain fundamentally human strengths. When these forces work together, combining machine speed with human judgment, the outcome is not automation for its own sake but elevation of work.

We’ve seen this dynamic clearly in our work with Esquire Deposition Solutions, a leading LegalTech firm. The challenge was to streamline the manual, time-consuming processes behind case preparation and transcript management. Instead of designing systems that removed human input, the focus was on empowering legal professionals with better tools, automating repetitive tasks while preserving human control over judgment-intensive work.

The result wasn’t about replacing expertise but amplifying it. Attorneys could dedicate more time to strategy, argumentation, and client service, the kind of high-value activities where human intuition and reasoning matter most. AI served as a bridge between efficiency and expertise, not a barrier.

The same principle applies across industries. In finance, predictive analytics can identify trends or anomalies, but value only emerges when decision-makers act on those insights with domain knowledge. In manufacturing, data-driven systems can forecast maintenance needs, but it’s still human teams who determine the optimal operational response.

As Stanford University’s AI Index 2025 puts it, “the most impactful AI applications are those that augment, rather than substitute, human capabilities.” The future of AI isn’t about automation, it’s about augmentation.

Beyond efficiency: AI as a driver of reinvention

For years, the promise of AI revolved around efficiency, faster processes, lower costs, fewer errors. But today, the most forward-looking companies are using AI for something far more strategic: reinvention.

Efficiency is no longer the goal; it’s the baseline. The real opportunity lies in using AI to create new forms of value, new products, and even new business models.

We’ve seen this shift firsthand in our collaboration with CCI Puesto de Bolsa, a financial services firm in the Dominican Republic. Their goal wasn’t simply to automate internal workflows, it was to create a foundation for smarter, data-driven decision-making across the organization. Through a modernization effort that unified data sources and streamlined core systems, CCI gained the ability to analyze information faster and make better investment and client decisions.

The lesson was clear: digital modernization and AI capabilities are most powerful when designed around strategic intent. Technology becomes a lever for agility and growth, not just operational optimization.

This mindset shift is happening across industries.

• In retail, AI is enabling hyper-personalized recommendations and conversational shopping experiences.
• In healthcare, real-time patient monitoring is paving the way for preventive care models.
• In manufacturing, predictive analytics and digital twins are unlocking entirely new service-based revenue streams.

As McKinsey notes, the organizations capturing the greatest value from AI are those that “redesign processes and workflows to enable entirely new sources of growth.” The companies leading this shift don’t see AI as an efficiency tool, they see it as a creative catalyst.

Making Sense’s approach: Turning AI into a long-term capability

At Making Sense, we often meet organizations at two very different stages of their AI journey. Some have spent years experimenting with disconnected pilots that never reached scale. Others are just beginning to explore how AI could enhance their operations. Despite their differences, both share a common challenge: bridging the gap between experimentation and execution.

Our work typically begins with understanding context. Before suggesting a solution, we help teams clarify what problem is worth solving and what kind of data and processes support it. That discovery phase often brings the alignment needed to move from abstract ideas to measurable goals.

Next comes strategy and enablement. We work with teams to design a roadmap that balances technical feasibility with organizational readiness. That might mean rethinking data architecture, building a strong governance model, or defining what success looks like in business, not technical, terms.

Finally, we emphasize adoption. AI creates value only when people use it confidently. That’s why we invest in change management, data literacy, and cross-functional collaboration. These elements ensure that the technology doesn’t operate in isolation but becomes part of how the organization thinks and makes decisions.

Our experience has shown that lasting success comes when companies treat AI not as a one-time project but as a capability, one that evolves through continuous learning and iteration.

When data, product design, and engineering execution come together in a feedback loop, innovation stops being an experiment and starts becoming an operating model.

Redefining value through collaboration

The debate around AI often swings between extremes. Some see it as overhyped, others see it as unstoppable. The truth lies somewhere in between.

AI by itself will not transform a business. But when combined with human intelligence, clear strategy, and sound data foundations, it can redefine how value is created and delivered.

We’re already seeing that transformation take shape. The leaders capturing real impact are not the ones racing to adopt every new model, they’re the ones asking better questions, aligning their teams, and building systems where technology enhances human decision-making.

At Making Sense, we believe the next frontier of AI isn’t about automation or scale, it’s about connection, between insights and actions, between data and judgment, between what’s possible and what truly matters.

Ready to explore how AI can accelerate real impact in your organization?
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