A Model for Responsible AI Adoption
In a recent meeting with a CIO and their team, I walked through some of the key considerations for responsible AI adoption. While each person nodded positively in fierce agreement about what they should be doing, the conversation quickly turned sideways towards: "By the way we're switching on Microsoft Copilot next week for everyone in the company". Ughh. It was like the previous 30 minutes never even happened.
The cognitive dissonance was so thick in the air I could barely make out the people in the room.
So I left. Composed myself and wondered if there's a better way to be talking about responsible AI to the people who are adopting it with abandon.
So I've been mulling over the right language. The words that might resonate. Terms like governance and guardrails don't seem to cut through. People often think I'm from the Department of "No" or a special branch of the Fun Police. The AI failures are real but so are the tantalising opportunities AI affords.
A recent botched Copilot deployment in Melbourne for a large corporate provided instant ... unrestricted ... access to employee payroll data. So if you wanted to know the salary of your CEO, your boss or your workmates you could just ask Copilot. Oops. Then came the kill switch, damage control and incident comms. The fun suddenly vacated the building as Legal came in to assess the damage. I can honestly tell you it isn't fun dealing with a failed project, a privacy breach or being in the newspaper for all the wrong reasons. Not every failure will end up as a headline, but a serious AI breach costs time, money, reputation and someone often gets fired. To read more about this AI mistake visit this LinkedIn post.
While employees and companies have become mesmerised by what AI can do, much less time has been spent considering how it should be deployed.
This article isn't about responsible AI adoption per se. It's about a subtle mindset shift, by using a helpful heuristic, Boards and executives can frame AI adoption with a simple lens.
The AI conversation often begins with technology but should go much further. AI gives organisations extraordinary Speed. It enables unprecedented Scale. But it must operate Safely. And achieving all of that requires increasing organisational Sophistication.
Speed - how will AI speed up our repetitive, non-value adding tasks?
Scale - how can AI be deployed to process high volumes of transactions both accurately and where it's most needed?
Safe - How can we ensure AI errors or failures are not amplified (at speed and scale)?
Sophisticated - How do we re-design how we deploy and what we do to get the best out of both AI and our human resources?
The first thing almost everyone notices about AI is its incredible speed. If speed is AI's most obvious advantage, then scale is its most transformative. Humans can only perform one task at a time. AI can perform the same task thousands of times, simultaneously and more accurately. Speed and scale aren't the problem in and of themselves, it's just that I find companies are somewhat unsophisticated (aka immature) in their deployment approach. The outcome is they unconsciously bypass the important steps that prevent high-profile failures, errors or harms (see my Copilot case study above).
Sophisticated
As AI becomes easier to access, deploying it successfully becomes less about technology and more about organisational maturity. Many AI failures are not caused by poor algorithms. They are caused by poor governance.
Companies can treat AI as a replacement for human expertise rather than as a tool to support people.
Companies allow employees to use public AI systems without considering privacy obligations, intellectual property risks or commercial confidentiality.
Employees rely on incorrect AI answers which convincingly and persuasively appear to be correct.
Last month, it was reported EY Canada published a cybersecurity report on “cyber threats and fraud in loyalty systems” that was later withdrawn after researchers found it was heavily contaminated by AI-generated errors. 70% of the references were fake, incorrect, or unusable.
Police forces in England and Wales were using shadow AI tools such as Microsoft's Copilot, without a policy, to help draft official court statements and intelligence reports. The officers didn't know, or realise, the AI was hallucinating by making up facts that appeared to be real. Police then relied on these AI-generated intelligence reports to take inappropriate action in the community.
In January this year, the scandal ultimately forced the early retirement of the Chief Constable Craig Guildford.
These examples highlight an important lesson. Sophisticated AI adoption isn't about selecting the right model or agentic agent.
It requires organisations to redesign business processes, establish governance frameworks, train staff, document decision-making, monitor system performance and ensure accountability always remains with people.
This approach aligns closely with the DTA AI Governance Standard, which consistently reinforces that human oversight should be designed into AI supported business processes rather than added as a final approval step.
Organisations also need to become more sophisticated in deciding where AI should and should not be used. Not every decision should be automated. Decisions involving vulnerable individuals, legal rights, healthcare treatment or significant financial consequences often require far greater human involvement than drafting a meeting summary or classifying routine emails.
Sophisticated companies will deploy AI where:
Boards are AI literate so they can make appropriate strategic choices
Executives understand the AI risks, can confidently make Go or No-Go decisions and have useful plans for what can go wrong.
Their staff will know when to question AI outputs and whether to use AI or not.
Their processes will include appropriate human oversight and
Their systems will enable ongoing monitoring rather than one-off testing.
Satisfying company stakeholders are the prime reason for greater sophistication:
Customers are increasingly choosing organisations they believe use AI responsibly.
Regulators expect stronger governance.
Investors and insurers are asking more questions about AI risk.
Employees want confidence that AI supports rather than replaces their professional judgement.
Sophistication is becoming the foundation that allows AI to scale safely.
Safe
AI safety is about managing risk, not eliminating it. One of the biggest misconceptions about AI is that a system is either "safe" or "unsafe." AI safety is much more like aviation safety or workplace safety. Risk can never be eliminated completely, but it can be understood, managed, monitored and continually improved.
This is the philosophy sits at the heart of the Australian Government's Digital Transformation Agency AI Governance Standard. The Standard encourages organisations to move beyond simply asking whether AI works and instead consider broader implications such as:
Is the AI being used appropriately?
Could people be harmed?
Are privacy and security adequately protected?
Is there appropriate human oversight?
Who is accountable if something goes wrong?
Are we continuously monitoring the AI after deployment?
Amazon, Workday and iTutor Group all fought costly legal battles over AI recruitment tools that discriminated against job applicants. In all cases, the AI systems relied on flawed hiring patterns in historical data to illegally filter applicants, automatically favouring some demographic groups over others. The companies and computer systems are not prejudiced or bigoted in an intentional way. The AI surfaced and amplified historical hiring patterns which lead to serious discrimination legal claims by disaffected job applicants.
The Starbucks Korea "Tank Day" controversy is a poignant case study in how AI-generated content can create catastrophic reputational harm when human oversight fails. Starbucks Korea launched a promotion for its new "Tank" tumbler range on 18 May 2026, branding the event as "Tank Day" and using the slogan "thwack on the desk." The marketing team reportedly used an AI tool to generate campaign ideas and slogans. Unfortunately, the AI-generated concepts unintentionally echoed two highly sensitive events in South Korean history. The date, 18 May, is associated with the 1980 Gwangju Massacre, while the slogan referenced a phrase linked to the 1987 torture death of student activist Park Jong-chul. The AI confused a marketing campaign with tragic historical events. Next, some managers who approved the campaign never opened the email attachments containing the offensive marketing materials.
Safe AI is about designing systems where humans remain accountable, risks are identified early, monitoring continues throughout the AI lifecycle and organisations are prepared to intervene whenever unexpected behaviour occurs.
Responsible organisations don't assume the AI will always be right. They design controls that minimise the consequences when the computer makes a mistake or the computer says "No".
Where to from here for AI Adoption?
The let's use AI conversation often starts with the exciting technology opportunity. Woo-hoo! Let's get with the times!
AI offers organisations extraordinary Speed.
It enables unprecedented Scale.
With increasing organisational Sophistication ...
... it can operate Safely.
In the months and years ahead, responsible AI deployment will become a defining characteristic of successful organisations. I just hope there the high-profile failures and the new Robo-debts will be few and far between.
Stay safe,
Bruce
AI. Use responsibly.
ABOUT ME
I partner with mid-size companies to confidently adopt AI, prevent high-profile failures and avoid the expensive mistake.
I write all my own content, you can tell by the odd typo and occasional missing word. I use AI for my research.
To learn about my upcoming public AI Governance workshops visit: Public workshops
To learn more about AI Governance, check out my Hitchhikers Guide to AI Governance Podcast.
To listen visit: Hitchhikers Guide to AI Governance Podcast
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