Alina Kukarina discusses the importance of initiating AI transformation prior to selecting the technology.

      Investment in AI has surged, with organizations rolling out copilots, agents, and generative AI across various functions. However, the anticipated business transformation that many leaders expected can still be challenging to pinpoint. An analysis from 2026 revealed that almost 40% of companies monitoring AI-related cost savings achieved less than 10%, while 90% intended to boost their budgets. Moreover, 38% of finance leaders and 39% of CEOs felt it was too soon to assess whether AI was proving valuable.

      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 investigate how organizations make decisions before introducing technology. She suggests that the common reaction to unsatisfactory results is to scrutinize the technology, models, or employee buy-in. However, a more significant issue may lie earlier in the process—organizations might start implementation without clearly defining what they seek to improve.

      This disconnect can be termed a “thinking gap.” Companies often create financial plans, implementation timelines, and technology roadmaps, yet the structured thinking that links these elements tends to receive less focus. Kukarina notes, “The initial question often becomes, ‘Where can we use AI?‘ A more valuable starting point would be to ask, ‘What are we trying to improve, and why?’”

      This distinction is crucial, as technology can enhance an existing workflow with great efficiency. If that workflow includes unnecessary steps, ambiguous ownership, flawed data, or decisions heavily reliant on human judgment, automation can exacerbate these issues when scaled. Kukarina emphasizes a fundamental principle: “Process evaluation should precede AI evaluation. Organizations must grasp how work is performed, where value is generated or lost, and where human judgment remains critical before assigning technology a role.”

      In her methodology, she outlines five interconnected elements: Problem, People, Process, Technology, and Outcome. The problem sets the objective. People highlight those impacted and where judgment, expertise, and trust are significant. The process illustrates how work is conducted. Technology determines which technology to adopt and if AI can offer substantial help. The outcome specifies the business and human results that leadership anticipates improving, which should be established early on.

      This framework also provides a stronger foundation for leadership decisions, including financial implications. AI initiatives come with costs related to software, infrastructure, training, governance, integration, and possible errors. Their benefits can encompass customer satisfaction, employee experience, service quality, and operational performance. Kukarina advocates for leaders to evaluate these dimensions collectively. For instance, an AI-generated response might enhance speed while affecting customer perception, whereas an AI-driven employee development plan could influence trust and motivation.

      Success metrics warrant the same careful examination as implementation strategies. License counts, user metrics, and token utilization may indicate activity and expense, whereas business impact necessitates a broader view. Kukarina points to saved time, retention, employee satisfaction, reputation, and the quality of customer engagements as examples of metrics that can indicate whether technology is creating real value.

      Research on AI transformation underscores the significance of this broader viewpoint. Approximately 70% of potential AI value resides within core functions like sales, marketing, manufacturing, supply chain, and pricing—areas where redesigning workflows can lead to significant consequences. Another study discovered that workflow redesign had the strongest correlation with EBIT impact among 25 organizational attributes evaluated.

      Consequently, people must be incorporated into the implementation equation from the outset. Kukarina’s work at Deeply Human Innovation includes strategic advisory services, intelligence and research, innovation programs, and ecosystem-building aimed at linking technological ambitions with human and organizational factors. Her insights suggest that employee responses can provide crucial operational insights. Resistance may indicate accumulated change fatigue, unclear roles, inadequate preparation, or practical issues overlooked in executive planning.

      This viewpoint also shapes her “Well-Being Compass,” which is founded on four principles: proactive thinking, purpose-driven decisions, humanity-centered design, and adaptability. The framework prompts leaders to consider scenarios where models, markets, regulations, workforce expectations, or business environments evolve. It also encourages them to challenge assumptions, such as universal data readiness or the belief that every new AI capability has a designated place within the organization.

      Her philosophy goes beyond the implementation of AI. Deeply Human Innovation aims to integrate humanity-centered thinking into the tools, teams, and systems that are shaping the digital landscape. This perspective situates organizational decisions within a broader social context, recognizing that business choices can impact employees, customers, communities, and future generations.

      “The quality of our tools matters, and so does the quality of the world those tools help us create,” Kukarina states. For leaders, this broadens the AI discourse from mere deployment to accountability. The strategic advantage may lie in cultivating the discipline to determine where intelligence fits, how people engage, and which outcomes merit investment.

      In essence, technology can hasten execution, but the responsibility for strategic judgment lies with humans. For Kukarina, that judgment initi