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[Blog Series - Part 2/3] - Bridging the AI Readiness Gap: Where Companies Fail and How to Fix It
[Blog Series - Part 2/3] - Bridging the AI Readiness Gap: Where Companies Fail and How to Fix It

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In Part 1, we explored the global AI investment surge, and the rising risks associated with investments outpacing readiness. This disconnect between AI’s potential and companies’ ability to harness it stems from a fundamental gap – businesses are simply not ready to absorb AI at the scale they are investing in. In this second part, we will dissect the critical readiness gap, exploring where companies frequently miss the mark and offering a roadmap for how they can prepare themselves for successful AI adoption.

The readiness gap begins with DATA. AI, in its simplest form, is a machine that consumes data, analyses patterns, and produces insights. But this entire process depends on the quality, quantity, and accessibility of data. Many companies underestimate the importance of having a solid data governance framework in place before embarking on their AI journey. Without clean, structured, and comprehensive data, AI systems cannot perform optimally. The issue is compounded by the fact that many companies operate with siloed data systems, meaning that critical data is scattered across different departments and platforms. This not only makes it difficult for AI to access the data it needs but also prevents companies from gaining holistic insights from their AI systems.

Another key factor in the AI readiness gap is TALENT. Despite widespread investments in AI technology, many companies have failed to invest in the workforce needed to support these systems. The gap in AI talent is well-documented. Many CEOs are concerned about the availability of key AI skills within their workforce, yet companies have been slow to allocate resources to reskilling and upskilling initiatives. A cautious approach to AI adoption, across countries like Japan, is a noteworthy contrast to the AI talent gap seen globally. By investing slowly and methodically, companies in such countries are giving themselves time to build the necessary skill sets internally. In many ways, this is a far more sustainable approach than the “move fast and break things” philosophy commonly seen in the West. AI is not just about having the technology; it’s about building an ecosystem of talent and infrastructure that allows that technology to thrive.

Analyst’s Perspective

In my view, the AI readiness gap is not just about technology and data. It’s also about the culture and mindset of organizations. The recent nasscom-EY Enterprise AI Adoption Index 2.0 Report outlines major challenges associated with scaled AI adoption across major industry verticals. These challenges are around data, lack of AI outcomes alignment, limited leadership commitment, and talent. AI requires more than just technical expertise; it requires a shift in how companies approach decision-making, strategy, and collaboration. One of the most overlooked elements of AI readiness is the ability of organizations to foster cross-functional collaboration. AI projects often fail because they are siloed within IT departments and disconnected from the business objectives they are meant to support. For AI to succeed, companies need to break down these silos and create an environment where data scientists, engineers, and business leaders work together to integrate AI into broader business strategies.

This requires a fundamental cultural shift. Companies must move away from seeing AI as a plug-and-play solution and instead view it as a continuous journey of learning and adaptation. This is where a cautious approach to AI adoption pays dividends. By not rushing into AI adoption, cautious adopters can ensure they are culturally and organizationally ready to embrace AI when the time comes. Hence, although many companies are eager to adopt AI, but without the right data, infrastructure, and talent, these investments are likely to fall short of expectations. Hence, as businesses continue to invest in AI, they must also invest in creating the right environment for AI to thrive.

In the final part of this series, we will explore how companies can recalibrate their AI strategies to ensure they are not only investing in AI, but also investing in AI success.


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Dhiraj Sharma
Principal Analyst

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