‘Your scientists were so preoccupied with whether they could, they didn’t stop to think if they should’. While this famous line from Jurassic Park is a poignant reminder of the dangers of unchecked ambition, it can also be applied to today’s rapidly evolving and fragmented AI landscape.
The mainstream availability of AI has compounded issues with shadow IT, as employees increasingly sidestep governance to deploy powerful, self-service AI tools. In this environment, many businesses are faced with how to manage the element of control when unmanaged AI systems start making critical business decisions based on fragmented, unverified data.
Like John Hammond’s ambitious yet doomed theme park, some organizations are now creating something powerful without fully understanding the risks or having proper containment measures in place.
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It’s become a business imperative to find ways to ensure AI-ready data is trusted, compliant, and seamlessly connected. Here we explore the unintended consequences of AI-driven shadow IT and why businesses need a structured approach to data management to avoid costly mistakes.
Georgia Tangalakie
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The rise of AI-powered shadow IT
Shadow IT is not a new challenge, but AI takes it to a new level. With so many generative tools now readily available, employees can solve problems, generate content, or make recommendations at speed. This happens often without needing any technical expertise or approval.
This speed is both a blessing and a risk. In their enthusiasm to experiment and move fast, teams often pull data from disparate sources, bypassing enterprise-grade controls in favor of quick, isolated fixes. Over time, these short-term solutions accumulate, and organizations are left with a patchwork of systems, models and insights that don’t speak the same language.
The risk isn’t just that teams are duplicating efforts or misinterpreting data. Business-critical decisions affecting customers, supply chains, product development and strategic direction are increasingly being made based on unverified siloed information. When AI systems operating on flawed data foundations make recommendations that influence growth strategies, the potential for bias or error multiplies exponentially.
Unify and trust your data
The antidote to this growing risk is not to clamp down on experimentation. It’s to build the right data foundation, one that supports innovation while maintaining context and integrity.
This means giving employees access to high-quality, AI-ready data from across the business. It’s essential to build one harmonized layer that connects all business AI applications and ensures that everyone from developers to decision-makers can rely on a single source of truth.
This foundation keeps context intact, so the entire business can see where, how, when and why data was produced, building trust and accurately informing decisions. When data is unified, it also supports regulatory demands and keeps the business agile to future compliance requirements.
The cost of siloed data and duplicated spend
There’s a significant cost benefit to this too. When growth is the unanimous business goal, organizations cannot afford to hemorrhage spend on an inefficient IT landscape.
It’s estimated that organizations today spend up to 50% of their IT budgets on data and analytics, with a significant portion of that going on attempts to harmonize disconnected data sources. Yet, despite these efforts, many businesses still lack a continuous, unified data layer that brings these sources together in a coherent, usable way.
That’s not just inefficient, it’s a missed opportunity. In the age of AI, the power of data lies not just in how much you have, but in how well it’s connected. Without a shared foundation, AI models risk drawing the wrong conclusions or being trained on outdated information.
This in turn leads to additional budgetary pressures. Businesses need to confidently scale AI across functions, knowing insights are accurate, secure and compliant.
From raw data to business outcomes
To move from raw data to real business outcomes, organizations need more than just infrastructure. They need a strategic approach to data and analytics that supports decision-making at every level.
This means combining new technologies with existing business processes to create enriched, curated data products that deliver meaningful value. It means equipping users with advanced analytics, benchmarking tools and AI-powered insights applications that can both interpret the data and recommend actions.
This strategic approach helps limit the spread of shadow IT by reducing the need for employees to seek out unapproved tools or shortcuts. By aligning data initiatives with established governance frameworks and cultural values, organizations can ensure consistency, compliance and trust in the data being used. At the same time, it creates space for innovation and agility, enabling teams to move quickly and confidently within a well-defined structure.
When done right, the benefits are clear: smarter decisions, faster responses and better outcomes across the board.
Creating a culture of AI confidence
Ultimately, the question businesses need to ask is not whether they’re prepared to use AI, but whether they’re ready to do it responsibly and reliably.
Readiness starts with a strong data foundation, ensuring that information is accurate, accessible and well-governed. It means empowering teams with tools and guidance to innovate responsibly, creating a culture where experimentation with the right tools is encouraged.
The lesson from Jurassic Park was not that innovation is dangerous. It’s that innovation without structure, without guardrails and without consideration of the bigger picture can quickly spiral out of control.
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