AI investment has expanded rapidly, with organizations introducing copilots, agents, and generative AI tools across functions. Yet the business transformation many leaders anticipated can remain difficult to identify. A 2026 analysis found that nearly 40% of companies tracking AI cost savings achieved less than 10%, while 90% planned to increase their budgets. Additionally, 38% of finance leaders and 39% of CEOs considered it too early to determine whether AI was delivering value.
For Alina Kukarina, co-founder of Deeply Human Innovation, those figures point toward a broader leadership question. Her experience across digital transformation, software, and management training has led her to examine how organizations make decisions before technology enters the picture. The natural reaction to disappointing results can be to examine the technology, the model, or employee adoption. A deeper issue may sit earlier in the process. Organizations can begin implementation before defining precisely what they are trying to improve.
The thinking gap
This gap can be described as a “thinking gap.” Businesses typically develop financial plans, implementation schedules, and technology roadmaps, while the structured thinking that connects those elements can receive less attention. Kukarina says, “The starting question often becomes, ‘Where can we use AI?’ A more useful starting point would be to ask, ‘What are we trying to improve, and why?’”
The distinction matters because technology can accelerate an existing workflow with remarkable efficiency. If that workflow contains unnecessary steps, unclear ownership, weak data, or decisions that depend heavily on human judgment, automation can amplify issues at scale. Kukarina points to a simple principle: process evaluation should precede AI evaluation. Organizations need to understand how work happens, where value is created or lost, and where human judgment remains essential before assigning technology a role.
Five connected elements
In her approach, Kukarina considers five connected elements: Problem, People, Process, Technology, and Outcome. The problem establishes the purpose. People reveal who is affected and where judgment, expertise, and trust matter. The process shows how work currently happens. Technology identifies which tool to choose and whether AI will provide meaningful assistance. The outcome defines the business and human results leadership expects to improve and needs to be defined from the outset.
This sequence also creates a stronger basis for leadership decisions, including financials. AI initiatives carry costs involving software, infrastructure, training, governance, integration, and potential mistakes. Their value can extend across customer satisfaction, employee experience, service quality, and operational performance. Kukarina argues that leaders benefit from examining those dimensions together. For example, an AI-generated response may increase speed while influencing how a customer perceives the organization, while an AI-generated employee development plan may affect trust and motivation.
Measuring what matters
Success metrics deserve the same scrutiny as implementation plans. License counts, user numbers, and token consumption can describe activity and cost, while business impact requires a wider lens. Kukarina points to time saved, retention, employee satisfaction, reputation, and the quality of customer interactions as examples of indicators that can reveal whether technology is contributing meaningful value. Without agreed metrics, AI pilots can remain isolated experiments that never connect to strategic performance.
Research on AI transformation supports the importance of this broader view. Around 70% of potential AI value sits within core functions such as sales, marketing, manufacturing, supply chain, and pricing, areas where workflow redesign can have substantial implications. Another study found that workflow redesign had the strongest relationship with EBIT impact among 25 organizational attributes studied. That finding reinforces the argument that how work is structured before AI is introduced matters more than the sophistication of the model itself.
People and resistance
People therefore become part of the implementation equation from the beginning. Kukarina’s work at Deeply Human Innovation includes strategic advisory, intelligence and research, innovation programs, and ecosystem-building designed to connect technological ambition with human and organizational considerations. Her experience suggests employee reactions can provide valuable operational information. Resistance may reveal accumulated change fatigue, unclear responsibilities, insufficient preparation, or practical issues that executive planning has missed.
Leaders who treat resistance as a signal rather than an obstacle can uncover the hidden assumptions behind an AI project. For instance, if frontline workers are skeptical of an AI copilot, the concern may not be about the technology itself but about how performance will be judged, whether errors will be blamed on employees, or whether the workflow was designed without their input. These are human questions, not technical ones, and they can be answered before launch.
The well-being compass
This perspective also informs Kukarina’s “Well-Being Compass,” built around four principles: proactive thinking, purpose-driven decisions, humanity-centric design, and adaptability. The framework encourages leaders to consider scenarios in which models, markets, regulations, workforce expectations, or business conditions change. It also invites them to challenge assumptions such as universal data readiness or the expectation that every new AI capability belongs somewhere in the organization.
Proactive thinking means anticipating the second-order effects of automation. Purpose-driven decisions require clarity about why an AI initiative exists beyond cost reduction. Humanity-centric design puts the experiences of employees and customers at the center of the process. Adaptability prepares organizations to shift course when the evidence demands it. Together, these principles offer a practical way to evaluate whether an AI investment is aligned with the organization’s deeper mission.
Kukarina’s philosophy extends beyond AI implementation. Deeply Human Innovation’s purpose is to embed humanity-centric thinking into the tools, teams, and systems shaping the digital world. That perspective places organizational decisions within a wider social context, where business choices can influence employees, customers, communities, and future generations. The long-term value of AI is not only measured in productivity gains but in the kind of workplaces and markets it creates.
“The quality of our tools matters, and so does the quality of the world those tools help us create,” Kukarina remarks. For leaders, that expands the AI conversation from deployment to responsibility. The strategic advantage may come from developing the discipline to decide where intelligence belongs, how people participate, and which outcomes deserve investment.
Technology can accelerate execution, but strategic judgment remains a human responsibility. For Kukarina, that judgment begins before implementation, with the questions leaders choose to ask. The organizations that thrive in the age of AI will be those that start with purpose, scrutinize their own workflows, listen to their people, and only then decide which technologies deserve a place in their future.