Jesse Todd, CEO, EncompaaS, explains why AI tools are only as effective as the data behind them and how enterprises can unlock the value of unstructured information.
AI has transformed the way enterprises manage and interact with information.
Processes that once relied on manual effort – retrieving records, reviewing contracts, searching archives – are now being streamlined through automation. Whether it is improving productivity or reducing risk, AI is quickly becoming a core enabler of digital transformation.
But there is one critical truth every enterprise must acknowledge: AI is only as powerful as the data that fuels it. Even the most advanced AI tools – no matter how well trained – will underperform or introduce risk if the underlying data is inconsistent, unstructured or poorly governed.
In fact, poor-quality data has emerged as the number one obstacle holding AI initiatives back from their full potential.
Data readiness: the unspoken barrier
A recent study found that while four in five business leaders expect generative AI (GenAI) to create competitive advantage within 18 months, 60% lack confidence in their data’s readiness. Without AI-ready data, these leaders risk falling short of their AI goals.
This disconnect between expectation and preparation is growing. Organisations are eager to implement GenAI tools to support knowledge work, automate compliance or deliver intelligent search. But few have taken the necessary steps to ensure their data is structured, complete and governed from the source.
Without AI-ready data, even the most promising initiatives risk stalling or failing to scale. The result is wasted investment, underwhelming results and growing distrust in the very tools meant to drive innovation.
Why data matters at every level
GenAI applications can range from the relatively simple to the highly complex. On one end, chatbots help customers find answers in seconds. On the other, GenAI is analysing massive pharmaceutical datasets where precision is non-negotiable.
Even a basic GenAI function – such as retrieving the wrong version of a contract – can escalate into a serious compliance issue if it exposes sensitive content to unauthorised users. AI must not only surface accurate results but also respect access controls and ensure that it does not expose intellectual property and personal data.
That is why the data foundation matters. Data must be enriched with accurate metadata, structured and permissioned before feeding it into GenAI. Disorganised, incomplete or unsecured data undermines output quality and erodes trust in AI-generated results – requiring human rework that cancels out promised efficiency gains.
Why AI trust depends on data readiness
Trust in AI outcomes is becoming mission critical. Consumers expect tools like ChatGPT or Microsoft Copilot to deliver reliable answers. For enterprises, the bar is even higher: the data fueling these tools comes from internal repositories – emails, file shares, document libraries and content platforms like SharePoint – not the public internet.
If these data sources are incomplete, mislabelled or unstructured, the GenAI system will reflect those flaws. It may hallucinate, overlook key information or apply outdated logic. And because GenAI tools present their results with confidence, users may not immediately recognise that something is wrong until it is too late.
Inaccurate data leads to inaccurate decisions. Worse, it erodes the credibility of your AI programme and increases the burden on teams who must manually verify or correct outputs.
What it means to be AI-ready
So, what makes data AI-ready? At its core, AI-ready data is structured, accessible, trustworthy and governed. That means:
• Structured: organised in a way that GenAI tools can interpret and query effectively
• Enriched: tagged with metadata that gives content meaning and context
• Permissioned: protected by role-based access controls that prevent misuse or exposure
• Normalised: consistent across systems to support scale and interoperability
• Governed: managed at the source to ensure real-time compliance and accuracy
Enterprises that can meet these criteria are not just better positioned for AI success – they are reducing risk, increasing efficiency and setting the foundation for long-term innovation.
Fortunately, intelligent data management platforms now exist to make this process far less manual. Automation can help organisations find, classify and normalise data at scale – turning it into an asset rather than a liability.
The hidden value in unstructured data
Consider that up to 90% of enterprise business value is locked in unstructured data. That includes emails, PDFs, SharePoint files, media and more. These sources contain valuable insights that could drive product innovation, uncover compliance risks or improve operational performance – if only the data were accessible, searchable and usable.
AI-driven data preparation unlocks this potential, transforming information into a strategic asset for growth, efficiency and innovation.
Why many enterprises still struggle
Despite the surge in AI investment, many organisations still lack a dedicated data strategy or AI leader to prepare data for AI. That is a missed opportunity. CIOs may own the technology stack but the responsibility for data is often fragmented across legal, compliance, IT and the business.
This lack of centralised ownership creates an accountability gap. GenAI initiatives move forward but foundational work around data quality and readiness lags behind. The result is AI that functions but does not inspire confidence.
A strong foundation for AI success
The reality is, GenAI tools are only as trustworthy as the data they consume. Poor data leads to poor outcomes. Enterprises that prioritise data readiness for AI now will gain the greatest competitive edge tomorrow.
