Alina Kukarina on why AI transformation begins before choosing the technology
August 18, 2026 – 2:28 pm
AI investment has expanded rapidly, with organizations introducing copilots, agents, and generative AI across functions. Yet the business transformation many leaders anticipated can remain elusive. A 2026 analysis revealed 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, these figures highlight a broader leadership question. Her experience in digital transformation, software, and management training has led her to explore how organizations make decisions before technology enters the picture. She suggests that leaders often mistakenly blame technology, models, or employee adoption when facing disappointing results. Instead, she argues for bridging what she calls the "thinking gap".
Organizations often develop financial plans, implementation schedules, and technology roadmaps while neglecting the structured thinking that connects these elements. Kukarina emphasizes:
“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?’"
This distinction matters because technology can streamline inefficient workflows but amplify existing issues if those workflows contain unnecessary steps, unclear ownership, weak data, or heavy reliance on human judgment. Kukarina adheres 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."
In her approach, Kukarina focuses on five interconnected elements: Problem, People, Process, Technology, and Outcome. The problem defines the purpose. People reveal who is affected and where expertise matters most. Process shows how work currently unfolds. Technology identifies suitable tools and AI’s potential assistance. The outcome specifies the desired business and human results that leadership aims to improve from the outset.
This framework strengthens decision-making, including financial considerations. AI initiatives carry costs related to software, infrastructure, training, governance, integration, and potential mistakes. Their value extends beyond customer satisfaction and employee experience to service quality, operational performance, and more. Kukarina encourages leaders to assess these dimensions holistically. For instance, while an AI-generated response may speed up communication, it also influences a customer’s perception of the organization; an AI-driven employee development plan might impact trust and motivation.
Therefore, success metrics deserve equal attention as implementation plans. Counting licenses, users, or token consumption only describes activity and cost; assessing business impact requires broader lenses: time saved, retention rates, employee satisfaction, reputation, and quality of customer interactions, to name a few.