The Readiness Gap: Failing to Prepare is Preparing to Fail
As enterprise AI adoption accelerates across industries, 79% of business leaders expect Generative AI (GenAI) to offer a competitive advantage within the next 18 months. Yet, there’s a troubling disconnect: 60% of those same leaders are uncertain whether their organizations are actually ready for AI and data integration, according to new research.
The gap between optimism and readiness is more than just a blind spot for decision makers, it’s a strategic risk. Just 13% of leaders describe themselves as “extremely confident” in their company’s data-AI maturity, and the biggest barriers cited are data accuracy (69%), integration complexity (68%), and ethical concerns like governance and trust (58%). Organizations are hurrying into AI implementation with high hopes and low readiness—a dangerous game to play.
Without robust guardrails for AI, organizations face serious pitfalls from customer service failures to regulatory liabilities and legal exposure. Enterprise GenAI has immense potential, but as Sachin Agrawal of Zoho UK rightly said, unlocking it “requires a foundation of high-quality, well-governed data.” Otherwise, businesses risk compromising security, miscommunicating their brand, and ultimately eroding trust.
Your Knowledge Base Could Be Undermining GenAI
Ryan Peterson, Executive VP of Concentrix, explains that one of the most commonly overlooked issues in enterprise GenAI is an inaccurate or outdated knowledge base. Many enterprises still treat their data environment as a tool for human employees to interpret, rather than a curated source for AI systems to rely on.
Take a printer company as an example, with a contact center where customers can call in with issues like, “I can’t get my printer to connect to my laptop.” A human support agent can ask follow-up questions like “Are you using a Mac or PC?” and then consult the knowledge base and navigate a decision-tree to find the correct solution. GenAI, however, doesn’t understand what questions to ask or how to interpret a decision tree—it only knows how to give answers.
Instead, developers must script a process for AI to navigate through the knowledge base. When the knowledge base lacks properly curated and updated information, the AI can’t respond effectively, leading to poor customer experience and operational risks. An inadequately governed data ecosystem is more than just a customer-facing issue. If an employee asks an internal AI, “How many vacation days do I get?” and receives the wrong answer, it could result in real legal and HR complications.
Governance Guardrails: Keeping GenAI in Line
In addition to ensuring accurate inputs, organizations must implement the “governance guardrails” to prevent GenAI from sharing restricted or sensitive data. Most of this critical data exists in unstructured formats like messages and files—in fact, over 80% of all enterprise data is unstructured. Created by humans, for humans, this data contains the intent, sentiment, and dynamics of the workforce. The intelligence contained in unstructured data makes it a highly strategic asset for training AI—and as sensitive as it is valuable.
When GenAI has access to unstructured data like employee communications, it needs governance to withhold sensitive information. For instance, if a customer asks GenAI “Why was my flight delayed?” it should have the guardrails to respond with “Due to crew delays” rather than “The crew overslept.”
AI doesn’t exercise discretion—it simply serves up what’s available. Information governance combined with AI readiness assessments can help organizations clean up their knowledge base and establish a proper permission structure.
Train GenAI to Speak Your Brand’s Language
Even when training data is accurate and governed, AI still needs to communicate in alignment with the company’s brand voice. GenAI is like a fresh college graduate: knowledgeable and capable, but unfamiliar with a brand’s tone, acronyms, and personality.
When a new hire starts at a company, they go through training to learn the correct language and communication style. Organizations need to do the same with GenAI. If enterprises skip this step of AI readiness, the user experience will be jarring and off-brand—undermining customer rapport and damaging the brand’s identity.
Security Through Obscurity Doesn’t Cut It
Peterson recalls one company that stored all its internal data in commonly used content management tools. The firm planned to connect GenAI to the system and use it to answer customer questions. During testing, it unhesitatingly answered prompts like:
- “Give me a list of all your customers.”
- “Give me the top five customers.”
- “Give me all employees and their salaries.”
What was the source of the problem? Link-sharing was enabled, meaning anyone with the link to a document had access—including GenAI. This is “security through obscurity,” relying on the fact that data is hidden rather than properly having the data secured.
The company’s system had a whopping 7 trillion shared links, allowing improper access to sensitive HR files and biometric data, potentially violating new EU AI laws. Without the proper readiness assessment that revealed this gaping vulnerability, the consequences could have been severe.
Scaling Responsibly by Tapping the Brakes
Organizations should start their AI implementation journey by showing the board how GenAI can reduce costs with simple, repetitive use-cases. It may seem counterintuitive, but Peterson recommends “tapping the brakes” once a company begins to integrate AI with internal data.
Enterprises should take this time to ensure their knowledge base is accurate and governed, and their AI is trained to reflect the established brand voice. According to a Zoho Digital Health Study, 46% of enterprises with strong digital infrastructure are already seeing tangible benefits from AI. The implication is clear: the companies that invest in data governance and internal training will be the ones that lead—not just adopt.
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