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Key Takeaways AI fails without clean, transparent data; poor inputs scale mistakes faster than human processes.
Strategic value comes from integrating AI into workflows, not running isolated experiments.
AI evolves faster than companies can integrate it strategically. This dynamic has become even stronger as we move toward 2026. Today, around 78% of businesses use AI in at least one business function – up from 55% in 2023.
Most of the cases of AI adoption affect marketing and customer service, and only 27% of companies use it in operational processes. The question now is why such a rapid technological uptake so rarely translates into strategic advantage? And how can companies move beyond the trap of ‘experimentation without integration’, where AI tools operate on the surface level but don’t systematically transform the business?
1. The principle of data transparency
AI is only as effective as the data it consumes. According to the PEX Report 2025/26, 52% of more than 200 professionals mentioned poor data quality and availability as their number-one challenge in AI maturity, ahead of internal expertise (49%), regulatory concerns (31%) and resistance to change (30%).
Clean, centralized and standardized data is the starting point for correct and productive cooperation between businesses and AI. Any “holes” or inconsistencies in data create distortions that AI algorithms then will only scale.
In 2024, The New York Times shared that Google’s AI Overviews in search served very dubious and inaccurate responses due to poorly filtered public web data. Google faced immediate public backlash and renewed scrutiny of its rollout strategy, proving that weak data governance can threaten even the world’s most advanced AI companies.
Related: Stop Using AI to Hype Up Your Story, Start Using It to Get Work Done
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