Responsibly adopting AI to unlock the value in your data

Balancing the promise of AI with practical safeguards for your data

The value proposition

One of the areas I focus on when working with businesses on their digital transformation objectives is knowledge management. I've seen first-hand how powerful it is when organisations make their knowledge accessible to their teams and customers. Making relevant information immediately accessible where it's needed, significantly improves both the user and customer experience.

Here a a few ways we can use AI to improve data discoverability, accessibility and usability (we call this data democratisation).

Simplifying search and discovery

Modern AI-powered search engines don't just index files; they understand the context behind queries. Natural language processing (NLP) enables staff to ask questions in plain English (“Show me last quarter's sales by region”) and get relevant, accurate answers drawn from across multiple systems. This reduces reliance on technical experts and empowers staff to find the information they need independently.

Automating data interpretation

AI can summarise complex datasets, highlighting key trends, anomalies, and insights without requiring users to dive into pivot tables or write SQL queries. Tools like automated reporting and AI-driven dashboards transform raw data into clear, digestible summaries, making analytics accessible even to non-technical team members.

Democratising predictive insights

Machine learning models are no longer just for data scientists. Many AI platforms now offer user-friendly predictive analytics, allowing teams to forecast trends, identify risks, and spot opportunities with minimal training. This gives marketing teams, sales staff, and operational managers an edge in making proactive, data-driven decisions.

Personalising data experiences

AI can tailor the way data is presented based on an individual's role, preferences, and past behavior. Rather than sifting through irrelevant reports, employees receive personalised dashboards and alerts highlighting what matters most to their job. This improves engagement and drives smarter, faster action.

Reducing information overload

Ironically, more access to data can sometimes mean more confusion. AI helps by prioritising and filtering information, focusing attention on the most critical metrics or changes. Instead of being overwhelmed by hundreds of KPIs, staff are guided to the insights that matter most.

Here are some reference case studies outlining some innovative ways that bussiness are leveraging AI to get results from their data: AI-Powered Data Insights and Accessibility: Case Studies

In practice

Recently we implemented a case management solution for a customer who deals with a very large volume of varying and complex cases.

Previously, customers had to wade through hundreds of pages of technical documents and policies to find the information they needed, often leading them to log a case just to get clarification.

Once a case was logged, it had to be manually triaged and assigned to the correct team. Then the consultant handling the case had to dig through the same dense material, along with past cases, to find the right answers, a slow and frustrating process for everyone involved.

We tackled these challenges in a few simple ways, mostly leveraging natural language processing (NLP) and implemented the following:

• On the website, there's now the ever more popular chat bot which can answer questions from customers in natural language and as a bonus, do so in almost any language. It's trained on only the customer's knowledge articles and will automatically create a case to avoid the risk of hallucination.

• When a customer logs a case, the system scans knowledge articles in real time and presents relevant excerpts as they type, deflecting cases before they are submitted.

• Cases are automatically triaged and assigned to the correct team based on their content.

• Consultants working on cases are automatically presented with relevant knowledge articles and similar past cases. The AI also helps populate responses and summarises communication history as they work.

The result? Dramatic improvements across the board: better customer experiences, faster case resolution, reduced workloads for consultants, and higher overall case quality.

The risks

Now that we've seen all of the benefits we can glean, lets talk about the risks.

In the rush to embrace AI, it's easy to overlook a simple truth: the more you feed it, the more it knows, and sometimes, it knows far more than you intended. From sales forecasting tools to virtual assistants that cheerfully summarise your inbox, AI is quietly threading itself through the fabric of our businesses.

The pitch is simple and seductive: give AI access to your data and, in return, it will make life easier for you and your team. And sometimes, AI genuinely delivers on that promise. But the part that often gets lost between the glossy demo and the dotted line is this: once AI has access to your business data, regaining full control isn't as straightforward as it seems.

We've been here before, in a way. Those of us who remember the early days of email servers or "open file shares" (the Wild West days of network security) know that convenience often came at the price of some "oops" moments involving sensitive documents. Today's AI tools are turbo-charged versions of those lessons, and the stakes are much higher.

Before we grant AI unfettered access to our sensitive data, it's worth taking a moment to understand what really happens when AI gets across your sensitive data. Not to slam the brakes on innovation, but to make sure we're still the ones driving the car.

What’s actually happening behind the scenes

Most modern AI tools are only as powerful as the information you allow them to access. They thrive on data, the more historical transactions, customer emails, support tickets, inventory lists, and financial reports you feed them, the sharper their insights and suggestions become.

The catch? The boundaries between "necessary access" and "too much access" blur very quickly. What starts as simply "connecting to the case management system" might quietly evolve into the AI hoovering up emails, file shares, contracts, and meeting notes, because "it helps make better recommendations."

The kicker is that many AI systems aren't designed to "forget" easily. Once data is ingested, it's very hard to cleanly retract or redact it. Worse still, if the AI is linked into external cloud services, or if it's learning from your inputs to improve itself, you may not have full visibility into where that data is stored, copied, or even processed.

In short, handing your AI the keys to a few filing cabinets might soon look more like inviting it to make itself at home in your entire office.

What to watch for

As AI becomes more embedded into everyday operations, the risks that businesses face are evolving too. Some are obvious, but others creep in quietly until they are suddenly a much bigger problem than expected.

Here are the major areas where I've seen risks creep in:

• Data leakage: AI systems can inadvertently expose confidential information. This could happen through overly helpful "auto-complete" features, shared outputs, or predictive suggestions that draw on sensitive internal data.

• Over-permissioning: It often feels easier to grant AI systems broad access "just in case." The trouble is, once you open the gates, monitoring and limiting that access later can be surprisingly tricky.

• Training data risks: If your data is being used to train models (especially in vendor-managed environments), you could lose ownership or control over parts of your intellectual property.

• Compliance and regulatory breaches: Laws like GDPR, HIPAA, and your country specific Privacy Act place strict conditions on how personal and sensitive data can be handled. AI systems working across jurisdictions can inadvertently cause violations without anyone realising until it is too late.

• Third-party vendor risk: Many AI solutions are built or hosted by third parties. If you don't have clear contractual controls over how your data is used and protected, you are exposing your business to another layer of vulnerability.

• Stealth AI: Applications that are introduced into your business operations without formal oversight or clear governance. It can include employees using AI-powered tools or plugins without IT or management approval, often because they are trying to work more efficiently.

I wouldn't say that these risks are reasons to avoid AI altogether. But they are strong arguments for treating AI deployments with the same caution and governance you would apply to handing over your financial records, client files, or strategic plans to a new employee or vendor.

Consider a simple example: you grant AI access to your entire document management system, unaware that Jeff from payroll has stored an Excel file listing employee salaries in a folder he assumed was private. Normally, this sort of slip-up might go unnoticed. But now, thanks to AI's ability to scan and reference everything it can access, any staff member could casually ask, "Who earns what around here?" and get an answer.

Of course, the implications get far bigger, more complex, and frankly, scarier when the same AI agents are used to power public-facing tools like website chatbots and self-service knowledge bases.

How we mitigate these risks

It pays to be deliberate about how you roll out AI. Here are some practical steps I'm seeing successful business take:

• Apply the principle of least privilege: Only give AI systems access to the minimum data they need to perform the tasks you actually want automated. Resist the temptation to "just give it everything" for convenience.

• Know your data: Understand what sensitive information you have, where it lives, and who currently has access to it. You cannot protect what you don't know exists.

• Classify and tag sensitive information: Make it easier to automatically restrict or alert when AI tools are handling high-risk data types, like customer personally identifiable information (PII), payroll records, or trade secrets. Using sensitivity labels on documents is a popular approach to achieving this.

• Choose AI vendors carefully: Scrutinise contracts to understand how vendors handle your data. Ask the awkward questions: Is my data used to train broader models? Is it stored outside my jurisdiction? Who has access to it?

• Implement clear internal policies: Make sure staff are trained on responsible AI usage. Often, the biggest breaches happen not because of malicious intent, but because people simply don't realise how powerful the tools they are using have become.

• Build in monitoring and oversight: Just like you wouldn't hire a new employee without supervision, don't let AI tools operate without regular checks. Review what data is being accessed, how outputs are generated, and whether anything feels off.

Treat AI like you would treat any new team member: with opportunity, yes, but also with boundaries, checks, and accountability. A little scepticism today can save a lot of headaches tomorrow.

Closing thoughts

AI is no longer an experiment running in the background. It is becoming a business-critical tool, and with that comes a new kind of responsibility. If businesses treat AI with the same rigour they apply to financial reporting, cybersecurity, and customer trust, the benefits will be enormous.

Ignoring the risks, or worse, assuming someone else is managing them, is a recipe for painful lessons down the line. But facing those risks with open eyes, strong guardrails, and a culture that values both innovation and caution? That is how business will unlock the true value of the tooling.

As always, a little thoughtfulness now is worth a lot less regret later.

This is an ever evolving area, that’s become a keystone of many of the solutions I’m implementing with my customers, so I’m sure it’s a topic that I’ll be revisiting as new insghts emerge.

Stay tuned.