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description: AI transformation consultant Alina Kukarina argues that most enterprise AI initiatives underperform not because of technology choices but because organizations...
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# Alina Kukarina on why AI transformation begins before choosing the technology

**[The Next Web](https://daily.dev/sources/tnw)** · 5 min read · 0 upvotes · 0 comments

## Summary

AI transformation consultant Alina Kukarina argues that most enterprise AI initiatives underperform not because of technology choices but because organizations skip the 'thinking gap' between ambition and implementation. She proposes starting with 'what are we trying to improve, and why?' rather than 'where can we use AI?', and outlines a five-part framework (Problem, People, Process, Technology, Outcome) for evaluating workflows before layering on automation. She cites data showing nearly 40% of companies see under 10% cost savings from AI despite rising budgets, and that workflow redesign has the strongest link to EBIT impact among organizational attributes studied. Her broader Well-Being Compass framework emphasizes proactive thinking, purpose-driven decisions, humanity-centric design, and adaptability in AI strategy.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://thenextweb.com/news/ai-transformation-begins-before-technology>

## Questions this post answers

### why do so many enterprise AI projects fail to deliver expected cost savings

Many AI initiatives fail to transform businesses because organizations start implementation before defining what they are trying to improve, skipping process evaluation. A 2026 analysis found nearly 40% of companies tracking AI cost savings achieved less than 10%, even though 90% planned to increase AI budgets, often because automation amplifies existing workflow problems rather than fixing them.

_Leaders weighing AI investment can follow ongoing coverage of transformation strategy on daily.dev._

### what framework can leaders use to decide where AI fits in their organization

Alina Kukarina, co-founder of Deeply Human Innovation, proposes a five-element sequence: Problem, People, Process, Technology, and Outcome. Problem defines the purpose, People identifies who is affected and where judgment matters, Process maps how work currently happens, Technology is chosen only afterward, and Outcome defines the business and human results expected before implementation begins.

_Anyone structuring an AI rollout can track frameworks like this through daily.dev._

### what metrics should companies use to measure AI business impact beyond usage stats

License counts, user numbers, and token consumption only describe activity and cost, not business impact. Alina Kukarina recommends measuring time saved, employee retention, employee satisfaction, reputation, and quality of customer interactions instead, since research shows workflow redesign has the strongest relationship with EBIT impact among organizational attributes studied.

_Teams defining AI success metrics can follow this kind of analysis on daily.dev._

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---

Tags: [#leadership](https://daily.dev/tags/leadership), [#digital-transformation](https://daily.dev/tags/digital-transformation)

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