At Maxsum, we’ve been at the forefront of championing Microsoft 365 Copilot adoption in Australian businesses and not-for-profit organisations, guiding them to navigate the shift from AI concern to curiosity to clarity, confidence and control.
Over the last 18-24 months, we’ve ridden the AI wave…from Generative AI peak hype in 2023, through the year of experimentation in 2024 as businesses starting opting into Microsoft 365 Copilot, to 2025 as the year we saw AI really start take flight with the introduction of Copilot Agents. Here’s what we’ve learnt.
What We’ve Seen on the Frontlines
At Maxsum, we’ve spent the last 12-18 months running AI Strategy Engagements, Copilot Enablement Workshops, Hackathons, and Executive Briefings and Demos across Victoria and beyond. Here’s what we’ve seen crop up time and time again:
- Almost every business has staff who are already using AI to get their work done – whether their leadership team realise it or not…so yes, the time to start understanding and guiding AI use in your organisation is now.
- Most organisations we speak to are not maximising their use of the foundational productivity tools (like Microsoft 365) they already have to be able to be fully leverage AI “out of the box”. Without consolidating your data and workflows, workplace AI will not generate wins.
- The biggest barriers to adoption? Leadership – Data – Follow-through. Slow-to-act leadership teams, disparate and dispersed data storage and handling, and teams and businesses not capitalising on initial momentum.
- The most successful adopters? Have started small – really small – one team, one use case, one process, piloted a series of high-value scenarios, and then scaled up and out gradually and with purpose.
Lessons We’ve Learnt – So You Can Cut to the Chase!
1. Not all the hype is hype…there is great stuff here! Just make sure it solves a real problem…
Many AI projects start with excitement over capabilities (“Copilot can do X, Y, and Z! Could it even do XYZ altogether?”) but lack a clear business goal. AI can be so impressive and produce such high quality outcomes upfront that it’s easy to get wrapped up in a solution for a problem that you may not have! Teams try out cool stuff and experiment aimlessly, but executives struggle to see how and where AI actually really moves any needle in the business. The successful rollouts we’ve led had a strong business use case and desired outcome from day one, paved with
- Clear use cases tied to business pain points. For instance, one client – a construction services firm – identified executive report writing as a time sink for their leadership team. We honed in on that scenario for their Copilot pilot. The result was a 30% reduction in time spent producing monthly reports, immediately proving ROI to the board – not least of all because their board packs were ready well ahead of time for a change! By contrast, another organisation that simply “turned on Copilot for everyone” saw very patchy uptake because employees weren’t sure where or what to use if for.
- Defined success metrics. Whether it’s faster proposal turnarounds, fewer customer emails for your service team, or improved employee engagement scores, successful pilot teams decided up front on how to measure success. One COO we worked with set an explicit goal to cut routine admin time recorded by 20% via Copilot within 3 months. Having that target kept the implementation team focused and allowed leadership to track progress.
- Executive sponsorship and vision. Make no mistake – adopting AI in any meaningful way requires change, and change needs leaders who will lead by example and keep the momentum rolling. Top-performing projects had a CEO or COO visibly championing the effort (“This is our path to working smarter, not just an IT project”). They communicated the “why” to the whole organisation with real examples of how AI would empower growth, efficiency, or better service, rather than framing it as just a trendy tool.
Why this matters: As an executive, you set the tone. If you treat Copilot or any AI as a strategic initiative that must earn its keep, your teams will align efforts with business outcomes. Without that, AI remains a shiny object that might never justify itself.
2. People and Process Make or Break It
One of the biggest lessons from our front-line engagements: the technology is by far the easiest part. The harder part is getting your people to use it optimally, consistently, and safely. Here’s what we’ve observed:
- Employees span the spectrum from eager to anxious. In workshop after workshop, we meet “AI champions” who can’t wait to offload grunt work to Copilot and folks quietly worrying if AI will make their role redundant. Both are valid perspectives. A key early step is addressing that elephant in the room – reassure your teams that Copilot is a tool to assist them, not replace them. And mean it: reinforce that message by highlighting examples of AI freeing people for higher-value work or work-life benefits, and never positioning AI as a cheap substitute for human talent.
- Training and hand-holding are essential. We’ve yet to see a scenario where you just enable AI and watch productivity soar overnight. Users need to learn how to use Copilot effectively (e.g. how to phrase prompts to get good results, or where it can integrate into their daily routine). In our internal Copilot Hackathon at Maxsum, even tech-savvy team members initially struggled with getting useful outcomes – the breakthrough came when we had a guided “prompt-crafting” session. Lightbulbs went off across the room as people realised small tweaks in how they asked Copilot could dramatically improve the outputs. Post-training, their confidence—and actual usage—jumped significantly. Lesson for leaders: invest time in upskilling your staff and provide a form for ongoing knowledge sharing. Consider short training sessions, user guides, or an internal support channel where employees can ask “How do I get Copilot to do X?” and share tips.
- Embed AI into business processes. Another pattern among successful adopters: they didn’t leave AI usage to individual whim. Instead, managers revisited team workflows to intentionally weave Copilot into existing, standard processes. For example, a sales manager we worked with adjusted her team’s proposal-writing process: now, the first step is “Draft with Copilot, then refine,” whereas before a rep would stare at a blank page. By normalising AI as part of a process, usage went from occasional to routine. Look at your key processes (from month-end reporting to customer support ticket handling) and ask: could AI support a step here? If yes, make it an expected part of the workflow.
- Identify champions in each department. We found it very effective when organisations set up “AI champions” group or AI Council. These are tech-savvy or enthusiastic individuals who pilot new Copilot features, collect feedback, and help co-workers figure out how to apply them in context. One manufacturing company’s COO established an AI Council early in their rollout, and it paid off – adoption rates in each department correlated with whether their council rep was actively coaching colleagues. Champions can also surface issues and raise best practice ideas, acting as liaisons between the frontlines and leadership.
Why this matters: As CEO or COO, recognising that your people determine the success of AI will shape your implementation plan. Budget for training, bake AI into the fabric of daily work, and create a support network. The goal is to move your organisation’s culture to one that embraces AI as a helpful colleague rather than a mysterious black box or, worse, a threat.
3. Set Clear Guardrails – Define “What’s AI-OK”
With great power comes great responsibility – and some risk. A recurring theme in our client engagements is risk management around AI, especially Copilot which can touch a lot of your data. Common concerns we’ve heard from executives include: “Will Copilot expose our sensitive data?” “How do we prevent mistakes or misinformation?” “What about compliance obligations?” The good news is these risks can be managed, but they must be addressed early. Our experience suggests:
- Create an AI use policy at the outset. If you have not done so, convene your legal, IT, and HR folks to draft guidelines on acceptable use of generative AI in your business. It should cover basics like data privacy (e.g. “do not feed client-identifiable data into unapproved AI tools”), quality control (“a human must review all AI-generated content before publishing”), and compliance with industry regulations. Maxsum formalised our own Generative AI Acceptable Use Policy internally and for many clients, this step brought much-needed clarity. It sets the tone that AI is embraced, but within safe bounds. People then know what’s “AI-OK” and what’s not.
- Choose the right tools and settings. We advise sticking to enterprise-grade AI platforms (like Microsoft 365 Copilot) where you have governance control, rather than random free AI apps. In one case, a well-meaning manager tried out a third-party AI meeting assistant in leadership meetings; IT quickly intervened when it wasn’t clear where those recordings were going. Ensuring your deployments route through approved, secure systems is key — and luckily Microsoft, for example, has built Copilot to honour your Microsoft 365 data security, access controls, and compliance configurations.
- Address “shadow AI” usage. An insight from our frontlines: if you don’t provide sanctioned AI tools, employees will likely use whatever they can find to fill the gap (we’ve seen everything from developers quietly using ChatGPT to troubleshoot code, to HR staff using AI on a personal phone to draft policy documents). It’s the classic shadow IT problem, now in AI form. The remedy is twofold: (a) offer a safe, approved alternative (so they don’t feel the need to go rogue), and (b) educate everyone on why chucking company data into random AI apps is dangerous. We often include real examples in these talks – like the Samsung incident where engineers accidentally leaked sensitive code via ChatGPT – to make it concrete. Setting clear do’s and don’ts, combined with providing the right tools, will channel enthusiasm safely.
- Monitor and iterate. Treat AI governance as an ongoing process, not a one-off checkbox. We recommend establishing an oversight group (often that same AI Council or your IT governance board) to regularly review how AI is being used, assess any incidents or near-misses, and update policies. One CEO we work with requested a quarterly “AI risk report” from his CIO after rolling out Copilot. It highlights things like usage stats, any anomalies or breaches of policy, and forthcoming AI feature changes that might need attention. This kind of proactive oversight from the top sends a message that we’re embracing innovation, but we’re nobody’s fool about it. It strikes the balance between avoiding paralysis (no, you don’t need to wait for regulators to write the rules for you – that could take years) and avoiding recklessness (trusting everything will be fine because Microsoft’s doing it). The truth is in the middle: you define your own comfort zone and enforce it.
Why this matters: Boards and leadership are right to ask hard questions about AI risks. By setting guardrails early – and communicating them clearly – you not only mitigate those risks but also build trust. Your team will feel more confident using Copilot if they know the rules of the road and see that leadership is actively managing the journey. “Responsible AI” stops being a slogan and becomes part of how you do business.
4. Quick Wins Fuel Momentum But Look for Longer-Term Plays
Rolling out AI is not a flip-a-switch affair; it’s a journey. It’s important to get some runs on the board early to justify the investment and build positive buzz internally – but equally important to keep an eye on the bigger picture of transformation. Here’s how to balance the quick wins with the long game:
- Pilot a small-scale, high-impact, low-complexity use case first. From our projects, the ideal starter use case has three qualities: it solves a visible pain point, it’s feasible with current AI capabilities, and it won’t cause major harm if Copilot’s output is imperfect. For example, automating the first draft of standard operating procedures or internal newsletters can save many hours, and any mistakes are easily caught in review (no external impact). On the other hand, jumping straight into, say, using AI to draft legally binding contract clauses might be too high stakes to begin with. One of our clients started with Copilot generating weekly sales report summaries for the exec team – a task that ate up a sales manager’s Monday morning every week. When the AI-generated reports consistently hit the mark (with minor edits), it was a huge credibility boost for the whole AI program. With that success story circulating in the office, other teams became more open to trying Copilot for their tasks.
- Celebrate and publicise the early successes. Don’t underestimate the power of internal PR. We’ve seen momentum really take off when leadership actively communicates wins: “Marketing saved 50 hours this quarter thanks to AI-assisted content creation,” or “Our IT service desk resolved tickets 30% faster by using Copilot to draft responses.” These don’t have to be massive, but make them known. People love to see practical examples from peers in their own organisation – it moves AI from abstract to real. Consider a brief mention in your all-hands meeting or an internal email highlighting the “Copilot Win of the Month.” It not only rewards the teams involved but spurs others to think, “Maybe we could try that.”
- Plan beyond the pilot: scale thoughtfully. Quick wins are phase one. As you prove value, the question becomes how to extend AI across the enterprise sustainably. A common mistake is to attempt a big-bang rollout after an initial win, without a roadmap. We encourage developing a staged adoption plan. For instance, after a successful pilot in the finance department, one CEO authorized expanding Copilot to all frontline sales staff but with additional training and a support buddy system in place, knowing their needs would be different. The next phase might involve integrating AI deeper into workflows or connecting Copilot with other business systems (e.g. your CRM or project management tool) for even greater impact. Outline these phases and requisite resources early. This not only provides a vision to strive for but also sets expectations that true transformation is a marathon, not a sprint.
- Watch for process drift and feedback. As AI usage scales, keep listening to the users on the ground. We learned a lot from feedback loops: in one case, employees reported that Copilot occasionally made up customer names in draft emails – a clear case of AI “hallucination”. Because we had a feedback mechanism, we caught it and reminded everyone to double-check names against the CRM. You might need to adjust processes or provide refresher tips as real-world usage uncovers quirks. Stay agile; refine the playbook as you go.
Why this matters: Early wins justify the endeavour and get everyone excited, which is valuable currency in change management. But longevity of AI’s benefits comes from continually scaling and improving how it’s used. As an executive, you need to shepherd your organisation through that entire lifecycle – from pilot to widespread adoption – and keep the focus on outcomes at each stage. Think of yourself as both a sprinter (celebrate the quick win) and a marathon runner (pace the journey).
What Winners Look Like
The frontlines of AI adoption have taught us that the winners aren’t necessarily those who rush out first or spend the most – they’re the ones who learn fast, adapt, and stay aligned with their core business goals. AI is a tool to serve your strategy, not the other way around. Keep that principle front and centre, and you’ll avoid the common traps.
What we’ve learned from the frontlines is this: Artificial intelligence in the workplace is as much about people and process as it is about algorithms and data. It’s a team sport that needs leadership to captain it and team players working towards the same goals. With thoughtful guidance and a readiness to learn, your organisation can turn ad-hoc experimentation into tangible results.
Are you ready to lead your organisation’s AI transformation from the front? The opportunity is here, and those that seize it boldly – and wisely – will shape the future of their industries. At Maxsum, we’re excited to continue partnering with businesses on this journey, sharing what we’ve learned and learning new lessons together. Let’s talk.
