Alina Kukarina discusses how the AI transformation process starts even before selecting the technology.

      Investment in AI has surged notably, as companies launch copilots, agents, and generative AI throughout various functions. However, the anticipated business transformation is often challenging for many leaders to pinpoint. An analysis from 2026 revealed that almost 40% of companies monitoring AI cost savings saw less than 10% improvement, while 90% intended to boost their budgets. Furthermore, 38% of finance leaders and 39% of CEOs believed it was premature to assess whether AI was generating value.

      For Alina Kukarina, co-founder of Deeply Human Innovation, these statistics highlight a broader issue in leadership. Her background in digital transformation, software, and management training has prompted her to consider how organizations make decisions prior to the introduction of technology. She notes that when faced with unsatisfactory results, the instinct might be to scrutinize the technology, model, or employee uptake. However, a more fundamental issue may lie earlier in the process: organizations sometimes initiate implementation without clearly defining what improvements they seek.

      This gap can be referred to as a "thinking gap." Typically, businesses create financial plans, implementation timelines, and technology roadmaps, yet the logical reasoning that ties these components together tends to receive less focus. Kukarina emphasizes the need to shift the initial inquiry from "Where can we use AI?" to "What are we trying to improve, and why?"

      This distinction is crucial since technology can enhance existing workflows with remarkable efficiency. However, if those workflows contain superfluous steps, ambiguous responsibilities, inadequate data, or decisions heavily reliant on human judgment, automation can exacerbate problems when scaled. Kukarina asserts that “process evaluation should precede AI evaluation.” Organizations must grasp how work is performed, where value is generated or lost, and where human judgment remains vital before assigning technology a role.

      Her methodology encompasses five interrelated elements: Problem, People, Process, Technology, and Outcome. The problem defines the intention. People indicate who is affected and where judgment, expertise, and trust are critical. The process illustrates the current workflow, while technology determines which tools to select and whether AI will add substantial value. The outcome outlines the business and human results that leadership aims to enhance and should be articulated from the start.

      This structured approach also reinforces the foundation for leadership decisions, including financial considerations. AI initiatives incur costs related to software, infrastructure, training, governance, integration, and potential errors. Their value may impact customer satisfaction, employee experience, service quality, and operational efficiency. Kukarina suggests that leaders gain from evaluating these factors collectively. For instance, an AI-generated response may not only improve efficiency but also affect customer perception, while an AI-crafted employee development plan may influence trust and motivation.

      Success metrics also deserve thorough examination like implementation plans do. Simple metrics like license counts, user figures, and token usage can indicate activity and costs, but assessing business impact requires a broader perspective. Kukarina cites time savings, retention, employee satisfaction, reputation, and the quality of customer interactions as indicators that can demonstrate whether technology is delivering significant value.

      Research into AI transformation highlights the importance of this expansive view. Approximately 70% of potential AI value is concentrated in core functions such as sales, marketing, manufacturing, supply chain, and pricing, where workflow redesign can have a significant impact. Another study showed that workflow redesign had the strongest correlation with EBIT among the 25 organizational attributes analyzed.

      Thus, people become integral to the implementation equation right from the start. Kukarina’s work at Deeply Human Innovation involves strategic advisory, intelligence and research, innovation programs, and ecosystem-building initiatives aimed at aligning technological ambitions with human and organizational needs. Her experience indicates that employee feedback can yield critical operational insights. Resistance might signal accumulated change fatigue, unclear roles, inadequate preparation, or real-world issues overlooked in executive planning.

      This viewpoint also shapes her “Well-Being Compass,” which is centered around four tenets: proactive thinking, purpose-driven decisions, humanity-centric design, and adaptability. This framework encourages leaders to contemplate scenarios involving shifts in models, markets, regulations, workforce expectations, or business conditions. It also prompts them to question assumptions such as universal data readiness or the belief that every new AI capability has a place within the organization.

      Her philosophy transcends AI implementation. The mission of Deeply Human Innovation is to integrate humanity-centric thinking into the tools, teams, and systems shaping the digital landscape. This belief situates organizational decisions within a broader social context, where business choices can impact employees, customers, communities, and future generations.

      Kukarina states, “The quality of our tools matters, and so does the quality of the world those tools help us create.” For leaders, this broadens the AI dialogue from mere deployment to encompassing responsibility. The true strategic advantage may arise from cultivating the discipline to determine where intelligence belongs, the role of people, and which outcomes warrant investment.

      In essence, while technology can expedite execution, strategic judgment remains a human obligation. Kukarina believes that this judgment initiates prior to implementation,