The AI Hype Trap: Why Most CEOs Struggle To Unlock Real Business Value

Forbes

3 hours ago

Diganta Sengupta is a seasoned technology leader with deep expertise in artificial intelligence, Gen AI, Cloud computing, and blockchain.
While collaborating with clients on cutting-edge AI initiatives, I've had a front-row seat to the rapidly evolving landscape of generative AI (GenAI). There's no doubt that it's a transformative force, and the excitement is palpable.
Leaders see GenAI as a powerful enabler of innovation, efficiency and even cultural change within their organizations. But beneath the surface of this enthusiasm, a more sobering reality has started to emerge.
I observed leadership become enthusiastic about leveraging AI to unlock insights from massive operational datasets, but the reality quickly became evident. Despite deploying advanced models, the organization lacked the foundational elements for scalable impact.
In other words, data was siloed, inconsistent and often not AI-ready. Teams were stretched thin across too many pilot projects without clear alignment to business workflows. Flashy prototypes drew attention but failed to deliver lasting value without reengineering the underlying processes.
This mirrors a broader trend. Seventy percent of CEOs fear that flawed AI strategies could lead to their removal, while 54% fear that competitors may already have more advanced AI implementations.
AI systems learn from historical data. If that data encodes human biases against certain demographics, regions or business units, the AI will reproduce and even amplify those biases.
While developing a prototype using certain datasets for a utility company, for example, I grappled with significant challenges around bias and fairness. These issues persisted despite the presence of seemingly robust governance frameworks. As we trained our AI models on historical operational and customer data, I noticed embedded biases tied to region, demographics and internal processes. These biases not only surfaced in the model outputs but were, in some cases, amplified.
My two cents: CEOs must invest in bias-detection tools, diverse development teams and transparency mechanisms long before deploying AI at scale. Without these guardrails, AI initiatives stall as risk-averse stakeholders balk at unverified "black-box" systems.
In another project integrating a large language model (LLM)-powered chatbot with an enterprise ERP system, I encountered AI hallucinations as the model confidently generated inaccurate and misleading information about customer orders. Despite rigorous prompt engineering and system tuning, we noticed that the LLM occasionally fabricated responses about inventory levels or order status.
This experience echoed findings from a 2024 Boston Consulting Group survey, which revealed that while 75% of executives ranked AI among their top priorities, only 25% reported realizing substantial benefits from their AI initiatives.
Tackle hallucinations with robust validation pipelines, keep human-in-the-loop review for critical outputs and ongoing monitoring of model performance.
This is where the challenge becomes even more complex. In many of my AI pilots in the oil and gas sector, I've repeatedly seen issues like inconsistent formats, missing metadata and a lack of standardized governance across departments severely impact model performance. Despite having large volumes of rich data, much of it couldn't be used without extensive manual cleanup. Efforts to unify data governance were often sidelined in favor of launching high-profile AI initiatives.
A Harvard Business Review Analytic Services survey similarly found that most companies' data is largely not ready for enterprise-wide AI, citing poor data quality as a key barrier. Without strong cross-functional data stewardship and quality assurance, even the most advanced AI models fall short.
Before spending on fancy models, CEOs must champion cross-functional data governance, setting up practices on creating common taxonomies, automated data-quality checks and centralized platforms. Only then can AI be relied upon to deliver accurate, actionable insights.
Working on the previously mentioned utility AI project also brought light to another critical and often underestimated concern—security and governance challenges that surround enterprise AI deployments.
As we integrated sensitive operational and customer data into AI workflows, it became clear how vulnerable these systems can become without rigorous controls. Inadequate access management, insufficient encryption and lack of monitoring can create openings for potential ransomware attacks and unauthorized data exposure.
In one survey, 35% of respondents cited mistakes or errors with real-world consequences and 34% pointed to not achieving expected value as top barriers. Both are rooted in security vulnerabilities and governance shortcomings.
CEOs must elevate AI risk management to the same level as financial or operational risk. This includes rigorous model-risk frameworks, data-privacy impact assessments and alignment with evolving regulations such as the EU's AI Act.
To harness the full potential of AI, I recommend applying practical, accountable strategies that organizations can adopt to drive real, scalable impact.
• Establish cross-functional data governance. Form a governance council with IT, compliance and operations to ensure data ownership, accountability and consistent standards.
• Implement data quality controls. Deploy automated checks for outliers, schema validation and data freshness to improve input reliability and mitigate bias.
• Address LLM hallucinations with RAG. Use retrieval-augmented generation (RAG), prompt chaining and fallback mechanisms to reduce hallucinations.
• Align AI projects with business goals. Prioritize initiatives tied directly to key KPIs (for example, safety, cost reduction, etc.), which can improve adoption and leadership support.
• Pivot away from noncritical use cases. Reallocate resources from low-impact projects to high-impact workflows like downtime alerts for field engineers.
• Focus on responsible AI deployment. Emphasize transparency, accountability and strategic value delivery to build trust and ensure scalability.
CEOs who view AI adoption as a multidimensional transformation rather than a plug-and-play technology will be the ones ready to move beyond the hype and truly harness the AI power. The future of competitive advantage lies not just in having AI, but in embedding it thoughtfully and responsibly into the fabric of the enterprise. This will help transform AI from a conceptual promise to a tangible asset and help drive innovation and growth for the organizations.
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