Axulu

Tag: governance

  • Your Copilot Is Underused: What AI Can Do Across the Information Estate

    Written for the CIO.

    Most organisations are sitting on more AI capability than they use. For a CIO, the near-term win isn’t a new platform — it’s turning licences you already own into embedded, governed capability across the estate.

    Ask most CIOs about AI and the conversation jumps to strategy, platforms and risk. All valid. But there’s a more immediate, less glamorous opportunity hiding in plain sight: the AI capability your organisation has already bought and is barely using. The licences are live; the value is leaking away unclaimed.

    Closing that gap is a distinctly CIO job, because it’s about the estate — adoption, integration, standards and governance — not a single clever tool.

    The value that’s already paid for

    Across a typical information estate, the highest-frequency wins are unremarkable and enormous in aggregate. Long email threads distilled to the decisions that actually need making. Meetings turned into clean action lists with owners and dates. Documents compared so changes and risks surface in seconds instead of a line-by-line read. Scattered notes and numbers assembled into a first-draft report or board update a human then sharpens. Knowledge buried across files and inboxes made findable.

    These aren’t future promises. They run today, largely inside the productivity suite your people already live in. The reason the value isn’t landing is rarely capability — it’s that nobody has treated adoption as a deliberate programme.

    The CIO reframe: adoption is a capability, not a rollout

    Here’s the misunderstanding that quietly wastes the spend: treating AI as a tool you deploy rather than a capability you embed. Deploying Copilot is a purchase. Embedding it is the work — deciding which tasks to point it at, showing people how it fits their real workflows rather than a generic demo, configuring it properly, and setting the standard for what “good and safe” looks like.

    And the tool itself matters less than the discipline around it. Two configuration questions alone — is our content being used to train the model, and how do history and memory behave — separate responsible use from quiet exposure, and most organisations have never deliberately set them. Governance isn’t the brake on adoption here; it’s what makes adoption safe enough to encourage.

    There is no single best AI

    A point that matters more at estate level than anywhere: there’s no single winning tool, and different tools are better at different jobs. One sits natively inside your Microsoft data and email and is unbeatable for everyday admin. Another is stronger at structuring long documents, drafting policy, or careful analysis. A third is a capable generalist. The CIO skill isn’t crowning a winner — it’s assembling a small, deliberate, well-governed stack matched to the jobs your organisation actually does. Standardising on one tool because choosing felt tidy is how you underperform on everything it’s weak at.

    The leadership question

    The question to put to your own estate: where are our people already paying for AI capability they aren’t using — and what’s stopping us embedding it deliberately, with the data controls set? The answer is usually a short, high-return adoption backlog.

    Try this prompt

    Map the quick wins across a team’s real workflows:

    “Act as an adoption adviser for a CIO. Here are the main recurring tasks in [team/function]: [list them]. For each, tell me how an AI assistant already inside our Microsoft environment could help today, what to configure so our data isn’t exposed, who should own the output, and how I’d measure whether adoption actually stuck. Prioritise by value and ease.”

    The output is a practical adoption plan grounded in tools you already own, not a business case for more spend.

    What to do next

    Pick one or two high-frequency workflows, embed AI properly with the configuration set and an owner named, and measure whether usage sticks. That proof — real adoption on real work, governed — is worth more than any platform pitch, and it’s the credible basis for deciding what to standardise and where a second tool genuinely earns its place.

    In closing

    For a CIO, the fastest AI value this year probably isn’t a new platform at all. It’s the deliberate, governed capture of capability you’ve already bought — matched to the right jobs across the estate.

    If your information and technology leadership would value a session on capturing that value — which tool for which job, configured and governed sensibly — that’s exactly what Savant and Axulu run. Where it helps, Savant can also connect you to fractional or interim IT and information leaders who’ve driven estate-wide adoption before.

  • Prompting for Executives: How to Get Useful Work Out of AI in 30 Minutes

    Most executive disappointment with AI is a prompting problem in disguise. A small set of techniques — learnable in half an hour — is the difference between a novelty and a genuinely useful tool. And the most important of them is really a governance skill.

    There’s a common, quiet verdict among senior people who’ve tried AI: “It was fine. Not the revolution I was promised.” Almost always, the tool wasn’t the problem. The request was. AI mirrors the quality of the instruction it’s given, and most first attempts are vague, so the answers are vague. The encouraging part is how quickly that’s fixed — the core techniques take about thirty minutes to learn and change the experience entirely.

    This isn’t about becoming a “prompt engineer.” It’s about a handful of habits that turn a flat tool into a sharp one — and one principle that matters more than all the techniques combined.

    The shift: from question to instruction

    The beginner’s mistake is treating AI like a search box — short, vague queries that get generic, hedge-everything answers. The fix is to treat it like a capable colleague you’re briefing: give it a role, context, the specific output you want, and the standard it’s being held to. Compare “what do you think of this plan?” with “act as a sceptical CFO; here is the plan and the numbers; identify the three weakest assumptions and what would have to be true for it to fail.” Same tool, completely different value.

    A few techniques that change everything

    Assign a role. Telling AI who to be sharpens everything it does. “Act as a cautious legal reviewer,” “act as a commercial sceptic,” “act as a risk analyst.” The role focuses the response far more than any amount of polite phrasing.

    Refuse to be flattered. This is the big one, and it’s worth dwelling on. Ask AI “show me why I’m right about this” and it will dutifully build your case — a confident, useless echo. Ask it “argue the strongest possible case against this decision, then tell me what I’m not seeing,” and you get something genuinely valuable. The model didn’t get smarter between those two prompts. You framed it to be honest rather than agreeable. The lesson generalises: a loaded question gets a loaded answer.

    Convene a panel. For any real decision, ask several roles at once: “Review this as a CFO, then as a legal reviewer, then as a red-teamer whose only job is to find what breaks.” You get a rounded critique instead of a single flat take — closer to a good leadership team than a chatbot.

    Make it check itself. AI can be confidently wrong. Adding “now verify that answer, show your reasoning, and flag anything you’re not sure about” catches a surprising amount of nonsense before it reaches your decision.

    Spot what should become a script. If you find yourself giving AI the same judgement task repeatedly with the same rules, that’s a signal it should become a fixed, repeatable process rather than a fresh ask each time — more reliable, and no longer dependent on the model’s mood.

    The principle that matters most: prompting is governance

    Here’s the idea that elevates all of this from technique to discipline. How you frame a request to AI doesn’t just shape the style of the answer — it shapes its honesty. “Show me why I’m right” and “show me why I might be wrong” are not two phrasings of one question. They’re a choice between comfort and truth.

    For a decision-maker, that’s not a writing tip. It’s governance. The framing you habitually use determines whether AI functions as a yes-man that launders your existing opinions, or as an honest adviser that improves your decisions. The problem people call “AI bias” is, in practice, very often just poor objective framing. Learn to frame for honesty and you’ve learned the single most valuable AI skill there is.

    The leadership question

    When you put a real decision to AI, ask yourself first: am I framing this to be challenged, or to be confirmed? If it’s the latter, you’ll get a comfortable answer and learn nothing.

    Try these prompts

    Three you can use today. For an honest critique:

    Act as a sceptical, experienced [CFO / operations director / legal reviewer]. Here is a decision I’m leaning towards: [describe it]. Argue the strongest case against it, identify the assumptions I haven’t tested, and tell me what would have to be true for this to go badly. Do not reassure me.

    For a rounded review:

    Review this from three perspectives in turn — a commercial sceptic, a risk and compliance reviewer, and a red-teamer whose only goal is to find the flaw. Give me each view separately, then the single biggest concern overall.

    To catch confident errors:

    Now verify your previous answer. Show your reasoning, identify anything you’re uncertain about, and flag any claim I should independently check before acting on it.

    What to do next

    Spend thirty minutes putting one real decision through those three prompts. The experience tends to convert sceptics faster than any demo, because the value is immediate and it’s on their own problem. For many teams the natural next step is a short, hands-on prompting session so the whole leadership group shares the same habits — particularly the framing-for-honesty discipline, which is too important to leave to chance.

    In closing

    AI isn’t underwhelming. Most people just haven’t been shown the half-hour of technique that makes it sing — and the one principle, that prompting is governance, that makes it trustworthy.

    If your leadership team would value that half-hour as a practical, hands-on session, Savant and Axulu can run it. It is low-friction, immediately useful, and often the gateway to the bigger conversation about doing AI properly.

  • The Cyber Insurance Question: Would Your Policy Pay If AI Caused the Breach?

    AI is the part of this conversation that gets people in the room. Cyber resilience is the part that keeps the board awake. The two are now connected — and most firms haven’t checked the join.

    There’s a question I’ve put to a lot of business owners, almost in passing: if you had a cyber claim tomorrow, are you confident your policy would actually pay? Most say yes without hesitation. Then I ask whether they could evidence the specific controls their policy assumes are in place — and the confidence drains out of the conversation.

    Insurance is contractual and every policy differs, so this needs care. A cyber policy is not an unconditional promise to pay. Many are written on the basis that the insured maintains certain security controls. If those controls turn out not to have been in place, a claim can become contested rather than straightforward.

    What most businesses misunderstand

    The misunderstanding isn’t that businesses are reckless about cyber risk. Most SME leadership teams are competing with other priorities: revenue, hiring, delivery, reporting deadlines and margin management.

    The hidden problem is that many firms have become more operationally fragile than they realise. Systems, suppliers, people, remote access and dependencies have multiplied, while the controls have not always kept pace.

    Where AI changes the picture

    For years, the textbook fraud has looked like this: a supplier’s mailbox is compromised, an invoice arrives looking normal except the bank details have changed, the payment goes out, and the money is gone before anyone notices.

    AI makes that harder to catch. The old defence was often human instinct — this email doesn’t quite sound like them. Writing style, tone and increasingly voice can now be imitated well enough to clear that bar.

    The attack isn’t new. What’s new is that the cues we relied on to catch it are becoming forgeable.

    The leadership question

    If you had to evidence your security controls to an insurer tomorrow, could you — today, with what is actually in place, not what you intended to put in place?

    And where are you still relying on a human noticing that “something doesn’t sound right” as a control?

    Three questions to take to your broker

    • What controls does our policy assume or require us to maintain, and are those written as conditions?
    • If we had a claim, what evidence would we need to produce to show those controls were in place at the time of the incident?
    • How does our policy treat social-engineering and authorised-push-payment fraud, as opposed to a technical breach?

    What to do next

    Read the conditions and requirements section of your cyber policy, and map your actual, current controls against it. Where they don’t match, you’ve found your priority list.

    No review guarantees a payout, and no article can promise a regulatory or insurance outcome. The aim is more grounded: make sure the controls your business is relying on actually exist, and that you could prove it.

    In closing

    AI is the attraction. Cyber resilience is the consequence sitting right behind it. A business that races to adopt AI while leaving its security foundations and insurance assumptions untested is moving fast in exactly the wrong direction.

    If your leadership team would value a clear-eyed session on where AI, fraud and cyber insurance now intersect, Savant and Axulu can help you check whether your controls match your cover.

  • Can You Use AI With Confidential, Financial or Customer Data?

    The question senior leaders in regulated and financial businesses keep asking has a yes-but answer — and a better version of the question. Trust in AI for sensitive work comes from the system around the model, not the model itself.

    It’s the question that comes up in almost every serious conversation with a CFO, a managing partner, or the leadership of a regulated business: can we actually use AI with our confidential, financial, or customer data — or is it simply too risky?

    The common framing is “is AI accurate enough to be trusted with this?” That treats trust as a property of the model. In serious, sensitive contexts, that’s not where trust lives.

    The reframe that changes everything

    A more useful question is not “is the AI right?” but “is it consistent and controlled enough to trust in this particular context?” That shift moves your attention from an unwinnable hunt for a perfectly accurate model to the thing you can actually build: a trustworthy system around whatever model you use.

    Trust comes from the system around the model, not the model in isolation. A capable AI with no controls, no accountability and no record is untrustworthy for sensitive work. A sensible AI wrapped in boundaries, human sign-off and an audit trail can be trusted in contexts the raw tool never could.

    What “the system around the model” means

    • Human accountability. A named person owns each consequential output.
    • Controlled data handling. Clear rules and technical controls on what data the AI may touch, where it is processed, and whether it is used to train anything.
    • Consistency over cleverness. For regulated processes, predictable behaviour matters more than occasional brilliance.
    • Defensibility and a record. You can show what was done, on what basis, with what data, and who checked it.

    In regulated work especially, this architecture matters far more than which model you picked. A generic public tool used casually is risky because it has none of this scaffolding.

    The leadership question

    For this sensitive process, do we have the accountability, the data controls, and the record that would let us defend our use of AI if we were ever asked to?

    Try this prompt

    Use this to triage your own data before any AI touches it:

    Act as a cautious risk and compliance adviser. Here is a business process that involves [type of data — e.g. client, financial, personal]. Help me classify: which parts of this data should never go into a general AI tool, which could be used only with controls, and which are low-sensitivity. For each, tell me what human accountability, data controls, and record-keeping I’d need for AI use to be defensible. Be conservative.

    What to do next

    Before using AI on any sensitive process, decide three things: who is accountable for the output, what data controls apply, and what record you would keep.

    In closing

    Yes, you can use AI with confidential, financial and customer data — but only as well as the system you build around it. The model is the easy part. Accountability, controls and defensibility are where trust is earned.

    If your leadership team would value help designing that system, Savant and Axulu can help make sensitive AI use defensible rather than nervous.

  • The AI Policy Your Business Needs Before Someone Pastes Client Data into ChatGPT

    Without a clear AI policy, a confidential-data incident isn’t a risk — it’s a matter of time. The fix is a one-page document most businesses could write this week, and the discipline to actually use it.

    Here’s a scenario playing out in businesses everywhere. A capable, well-meaning employee is under pressure. They have a long, sensitive document — a client file, a contract, a set of management accounts — and a public AI tool that could summarise it in seconds. There’s no rule telling them not to. So they paste it in.

    No malice, no recklessness — just the predictable result of useful technology meeting an absence of guidance.

    Why this is now urgent, not theoretical

    AI is genuinely useful, so people will use it. The most damaging exposure isn’t exotic; it is the ordinary paste of sensitive text into a public tool by someone trying to do their job well.

    The more regulated or confidential your work, the sharper the exposure. The material your business most needs to protect is exactly the material your people are most tempted to hand to AI.

    What the policy actually needs to say

    • Which tools are approved. Name them. “Use these; don’t use random tools you found online.”
    • What you may put in. General, non-sensitive internal material, defined clearly.
    • What you must never put in. Client data, personal data, financial details, and anything confidential or regulated.
    • Prompt and data hygiene. Strip identifying details, use redacted extracts, and never paste what you would never email externally.
    • Human review. Consequential AI output gets checked by a person before it is used.
    • Who to ask. A named owner for grey-area questions.

    That is a page. Most businesses could draft it this week — and it would prevent the majority of realistic incidents.

    The mindset shift: prompting is governance

    An AI policy isn’t really an IT document. It is a governance document. How your people interact with AI — what they put in, what they trust, what they check — is now part of how your business handles confidentiality, risk and accountability.

    The leadership question

    If an employee pasted a confidential client document into a public AI tool tomorrow, have we given them a clear, written reason not to — and would we even know they had?

    A one-page policy checklist

    • Which AI tools staff are allowed to use
    • What kinds of information they may put in
    • What they must never put in, with concrete examples
    • That important outputs must be checked by a human
    • Who to ask when unsure

    What to do next

    Write the one page this week, name an owner, and circulate it before you do anything more ambitious with AI. Sophistication can come later; the red lines cannot wait.

    In closing

    You don’t need a perfect governance regime to be safer. You need a clear page that stops the predictable mistake — and the discipline to make it real.

    If you’d like a practical, plain-English AI policy template and help tailoring it to your business, Savant and Axulu can provide that first concrete step.

  • AI Without a Data Breach: How to Let People Experiment Safely

    The choice isn’t between banning AI and risking a data breach. It’s a third path — controlled experimentation — that lets people capture the value without exposing the business.

    Most organisations approach AI risk as a binary. Either you lock it down to protect the business, or you let people loose to capture the upside. Framed that way, both options are bad: the lockdown drives usage underground, and the free-for-all sends confidential data into tools you don’t control.

    Controlled experimentation deliberately enables people to use AI on real work, inside boundaries designed to keep the business safe. It captures the value because it manages the risk, not despite it.

    Why the two obvious options both fail

    The ban fails because it doesn’t change behaviour, only visibility. People who found AI genuinely useful don’t stop; they move to personal devices and accounts.

    The free-for-all fails for the opposite reason. Without rules, well-meaning staff paste sensitive material — client data, financials, contracts — into whatever public tool is to hand.

    What a safe-experiment framework contains

    • An approved tool stack. A small, named set of tools the business has chosen and configured.
    • Clear acceptable-use rules. What AI may be used for, what data must never go into it, and where human judgement stays in charge.
    • Human review where it counts. Consequential outputs get checked before they are acted on.
    • An explicit “when not to use AI” list. Mature governance is as clear about the no-go zones as the green-light ones.
    • A usage audit and an owner. A named person keeps the framework current and answers grey-area questions.

    The point that’s easy to miss

    In regulated, financial, or otherwise sensitive work, the architecture around the tool matters more than the cleverness of the tool itself. Trust doesn’t come from the model being impressive. It comes from the system around it — the boundaries, review, controls and ownership.

    The leadership question

    Are we making it easy for our people to use AI safely — or are we leaving them to choose between not using it and using it dangerously?

    A short safe-experiment checklist

    • Have we chosen and configured a small set of approved tools?
    • Have we told people, in writing, what data may and may not go in?
    • Is there a clear rule that important outputs get a human check?
    • Have we named where AI must not be used at all?
    • Is there someone who owns this and reviews how it is actually being used?

    What to do next

    Set the boundaries first, then invite experimentation inside them. Start with the approved stack and the one-page acceptable-use rules. Then name an owner.

    In closing

    Growth with AI should mean growth with guardrails: real value, captured safely, by design.

    If your team would value help building a safe-experiment framework — approved tools, clear rules and the right ownership — Savant and Axulu can set that up for senior teams.

  • Shadow AI: Your Staff Are Already Using It — Is Your Data Leaving With Them?

    The biggest near-term AI risk in most businesses isn’t a rogue algorithm. It’s an ordinary employee pasting confidential information into a public tool — and a leadership team that has no idea it’s happening.

    Walk the floor of almost any business right now and you’ll find AI already in use. Not sanctioned, not logged, not discussed in a board paper — just quietly helping someone rewrite an email, summarise a document, or make sense of a spreadsheet.

    This is why, at senior events, the questions increasingly cluster around two topics: AI and security. The novelty of the demos wears off quickly. The worry that replaces it is more durable and more board-level: if our people are using these tools, what’s happening to our information?

    The mistake that makes it worse

    Faced with that worry, the instinct of a cautious leadership team is to ban AI. It feels responsible. It is, in fact, the single move most likely to make the problem worse.

    Banning AI doesn’t stop AI. It creates shadow AI. People who found the tools useful move to their phones, personal email, and home accounts. The work still gets done with AI; it just happens somewhere you can’t see, govern, or log.

    The everyday way data leaks is not exotic: someone pastes a client list, draft contract, management accounts, or a sensitive case file into a public tool to “just get a quick summary.”

    What good actually looks like

    • An approved tool stack. A small, named set of tools the business has chosen, configured and stands behind.
    • The right settings. Data-use, history and memory settings deliberately configured rather than left to chance.
    • A short, readable policy. A one-page answer to what can be pasted in, what must never be pasted in, which tools are approved, and who to ask when unsure.
    • Prompt and data hygiene. Strip identifying details, avoid whole confidential documents, and keep sensitive data out of public tools.
    • A named owner. Someone keeps the approved stack current, answers grey-area questions, and reviews actual use.

    Done well, this does not kill the upside. It lets people capture productivity gains without quietly exporting confidential information to do it.

    The leadership question

    Which data should never be pasted into a public tool — and does every member of staff know the answer?

    And the sharper one: if a member of staff had pasted a confidential client document into a public AI tool last week, would anyone in this business even know?

    A short shadow AI check

    • Do we actually know which AI tools our people are using today?
    • Have we told them, in writing, what they may and may not put into those tools?
    • Have we chosen and configured an approved set of tools?
    • Is there a named person responsible for AI use and grey-area questions?
    • For our most sensitive data, is there a clear “never paste this” line everyone understands?

    What to do next

    Run that check as an honest exercise, not a witch-hunt. The goal is to surface what is actually happening and replace silent, ungoverned use with visible, governed use.

    In closing

    Shadow AI is the point where AI curiosity quietly becomes a board concern. Handled badly, it is a slow leak of confidential information no one can see. Handled well, it is the moment a business decides to grow with AI and keep control of its own data.

    If your leadership team would value a structured look at where AI and data risk meet, Savant and Axulu can help turn invisible usage into governed adoption.