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What are we learning about transformation? What are the pitfalls to be avoided and the success factors to be leveraged?
Well, here is an interesting window into what experts are saying about AI Transformation based on contributions to the PEX Network:
The peacocking phase. Many organizations are still in what has been called the “peacocking phase of AI”—showing off impressive interfaces, demonstrations and generative AI add-ons. They may attract attention, but visibility should not be confused with transformation.
Redesigning the business. True AI transformation goes beyond adding AI to existing processes. It means rethinking how the business operates and creating ways of working, products or services that would not be possible without AI.
Transformation is a muscle. Transformation should not be treated as another project with a beginning and an end. It is an organizational capability—a muscle developed through repeated experimentation, learning and adaptation.
Garbage in, garbage out. AI cannot compensate for weak foundations. If systems are fed incomplete, unreliable or unstable data, they will produce equally unreliable results. Data readiness is therefore central to AI readiness.
Agents don’t always know when they’re wrong. AI agents may be highly capable, but they cannot necessarily recognize when the data they are using is inaccurate. Human oversight, verification and judgment remain essential.
Beware of AI slop. AI-generated work can look impressive at first glance while proving generic, inaccurate or even nonsensical on closer examination. This “AI slop” frustrates users, reduces trust and ultimately slows adoption.
Build AI literacy. Reducing poor-quality output is not simply a technical challenge. People need the ability to ask better questions, evaluate AI-generated work and recognize when the answer cannot be trusted.
Define success before starting. Organizations should decide what AI success means before launching an initiative. Is the objective to reduce costs, increase speed, improve quality or strengthen customer satisfaction? Realistic and specific outcomes provide the basis for meaningful evaluation.
Discipline matters more than budget. Effective AI adoption does not always require a large investment. Alignment around the problem, disciplined execution and sound human judgment may matter more than the size of the technology budget.
Apply ROI discipline. AI initiatives should be evaluated with the same rigor as other capital investments: What are we investing? What outcome do we expect? How quickly can we demonstrate value?
Business first, AI second. The strongest starting point is not the technology but the business problem—whether that is inefficiency, cost, delay or poor customer experience. AI is a potentially powerful tool, but it should be selected because it helps solve the problem, not because the organization feels it must “do AI.”
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