This week’s Let’s Talk explores how teams can adopt AI through practical training that builds confidence and capability without disrupting day-to-day work.
What most teams are actually struggling with isn’t motivation, it’s integration. How do you help people use AI in a way that fits into how they already work? How do you build skills without pulling everyone out of delivery mode? And how do you avoid turning AI into “one more thing” that slows people down instead of speeding them up?
In this Let’s Talk, our experts draw on hands-on experience working with teams at different stages of AI adoption. They’ll share what works in practice, where teams typically get stuck, and how leaders can encourage experimentation without losing control or momentum. Expect practical examples, honest trade-offs, and a clear-eyed look at what AI training should look like when speed and productivity actually matter.
Manav Khurana, Chief Product and Marketing Officer at GitLab
“GitLab has released its 2025 DevSecOps research report, The Intelligent Software Development Era. The findings reveal a growing “AI Paradox” where AI accelerates coding. Yet teams in Australia are becoming less efficient overall due to fragmented tools, rising compliance demands, and mounting pressure to upskill. DevSecOps professionals lose 7 hours per week to inefficient processes. The data shows a sector embracing AI at pace while struggling to manage its consequences, and indicates that success in the next era of DevSecOps will hinge on an organisation’s ability to balance speed, security, and evolving AI-driven skill sets
This survey illustrates what we call the ‘AI Paradox,’ where coding is faster than ever, yet the lack of quality, security, and speed across the software lifecycle is causing friction on the road to innovation. Toolchain fragmentation has created bottlenecks for developers, and AI agents are amplifying the issue. Organisations need a new framework to match the speed of software development in the age of AI, one that provides intelligent orchestration across the entire software lifecycle while addressing the interconnected requirements of AI orchestration, governance, and compliance that individual point tools simply cannot solve.”
Sonia Eland, Executive Vice President and Country Leader, Australia and New Zealand, HCLTech
“One of the most common mistakes organisations make with AI is treating training as a one-off event. In fast-moving environments, capability building needs to be continuous, practical and aligned to how people actually work.
A strong AI program begins with a foundational level of education across the organisation, explaining what AI and GenAI are, how they differ, where they can be applied, and the company’s own principles for safe and responsible use. Creating this shared common language is essential before progressing to deeper or role-specific development.
From there, leaders should prioritise roles rather than tools. Every team engages with AI differently – from analysing data and automating workflows to enhancing customer interactions. Training is far more effective when it’s tailored to how AI supports each role. The most meaningful development often comes from on-the-job learning, where teams apply AI directly to their own processes, priorities and challenges.
Speed also comes from decentralising capability. AI expertise shouldn’t sit with a small group of specialists. Equipping teams with simple frameworks, shared best practices and access to experts enables learning to happen in real time and in context.
Leadership mindset is just as critical. When AI is framed as an opportunity to experiment and learn, rather than a risk to control, adoption accelerates. This approach has helped HCLTech treat AI capability as a people and change initiative, driving faster adoption, stronger engagement, and more meaningful business impact.”
Adam Gregory, Senior Director, ANZ, LinkedIn Talent and Learning Solutions
“The key is creating a culture of continuous, personalised learning, where employees can build AI skills without stepping away from their day-to-day responsibilities and in ways that align with their career ambitions.
The most effective teams treat AI upskilling as part of the flow of work, not in one-off training sessions. Self-paced, short, and easily digestible content that is supported by role-relevant examples allow people to experiment with AI tools and apply what they are learning immediately, without slowing productivity.
Here’s how to make it work:
- Empower choice: Offer curated AI learning paths so employees can focus on what matters most to their role.
- Keep it bite-sized: Encourage micro-learning, such as short lessons that fit into a coffee break or between meetings.
- Connect to growth: Position AI upskilling as part of career development, not an extra task.
- Lead by example: When leaders are actively building their own AI capabilities, teams follow.
Platforms like LinkedIn Learning reflect this shift toward flexible, on-demand learning – giving teams access to practical AI content whenever they need it. The goal is not training for training’s sake, but to foster a culture where learning feels frictionless and future-focused. When people feel in control of their growth, they adopt new technologies faster and with greater confidence.”
Liam D’Ortenzio, Global Head of People at Employment Hero
“Artificial intelligence holds enormous promise for the workplace. Used well, it can unlock productivity gains at a scale while amplifying human potential. But like any powerful tool, its impact depends on how effectively we learn to use it.
Implementing AI into an organisation works best when it is treated as core infrastructure, not a side project. At Employment Hero, AI is built into how work gets done – one of our principles is that we are an AI First company. Our team is expected to use it, supported by easy to understand enablement and clear standards.
We set baseline rules to ensure the responsible use of AI from day one and from there, it is woven into everyday workflows. Teams are given clear, practical use cases that save time straight away, such as brainstorming ideas, summarising information, or analysing patterns. We also crowd source use cases from our Heroes to encourage innovation and accelerate upskilling.
Leadership should always set the pace. When leaders use AI openly and talk through how they apply it, learning spreads fast. AI becomes a habit, productivity lifts, and teams build skills that keep pace with how work is changing.”
Hany Mosbeh, Senior Vice President MEAPAC, JAGGAER
“Some leaders worry that AI’s steep learning curve may hinder productivity. In practice, AI training doesn’t need to be complex or time-consuming. When introduced thoughtfully, it can help teams work more efficiently without slowing them down.
Start by applying AI to familiar, everyday tasks — particularly those involving repetitive analysis or large volumes of data. In areas like sourcing, contract or supplier management, AI can quickly reduce manual effort and improve accuracy, making its value clear and tangible.
Learning should be embedded into existing workflows wherever possible. Long, standalone training sessions often create friction, while short, practical demonstrations and real-life use cases allow employees to learn as they work. This approach keeps momentum high and minimises disruption.
Creating space for experimentation is also critical. Teams are more likely to adopt AI when they feel supported to test and refine how they use it without fear of making mistakes. Clear guidelines around data use and accountability help build confidence and trust.
As seen across JAGGAER and our customer base, the most successful AI adoption strategies don’t slow teams down, they empower them to make better decisions, faster, while freeing them up for higher-value work.”
Gry Stene, Speaker, Author and Human-Centred AI & Tech Advisor, Gry Stene
“Most teams don’t struggle with AI because it’s complex. They struggle because it’s introduced without context, confidence, or care.
I’ve been working with AI in one form or another since the late 1980s, and leading tech teams for almost four decades. The pattern is consistent. When new technology slows people down, it’s rarely the technology. It’s how it’s led.
The first reframe is this. You’re not actually training AI. You’re shaping how people think, decide, and work with it. That’s why I talk about rAIse IT right.
AI behaves less like a tool and more like a fast-learning junior team member. If you don’t set boundaries, values, and expectations early, it will create inconsistency, risk, and confusion. Just like people do. Being a good parent to AI means guiding what it’s allowed to do, where human judgement is essential, and how quality is checked.
Start small and practical. Show teams how AI supports the work they already do, drafting, summarising, analysing, sense-checking, rather than adding another system to learn. Role-specific use builds confidence quickly.
Most importantly, lead visibly. When leaders model responsible, thoughtful AI use, teams follow.
AI doesn’t slow teams down. Poor leadership does.”
Anthony Capano, Regional Director, APAC, Intuit Mailchimp
“The biggest hurdle with AI isn’t motivation; it’s knowing where to start. To avoid slowing your team down, make AI feel useful straight away, rather than another project that steals time from already busy people.
Start small. Pick one task that regularly creates bottlenecks, like reporting, personalising emails, or organising customer data, and introduce a simple tool that does the heavy lifting. Teams build skills through quick wins and low-risk experimentation. When they see AI working in real situations, they understand the value and build confidence using it.
According to our Marketing Equaliser report, a lack of skills is the biggest barrier to adoption (39%) globally, yet nearly all marketers surveyed (98%) believe AI will improve effectiveness. That optimism is worth tapping into, but the shift has to be gradual, not overwhelming.
Over time, the goal isn’t just to use AI, but to weave it into everyday work; reducing acquisition costs, improving personalisation, and speeding up campaign planning. The more you use AI, the more useful it becomes, helping teams move faster and focus on the work that actually grows the business.”
Amber Johnson, Head of AI | Workforce Optimisation, MYOB
“At MYOB, we train teams to use AI without slowing them down through an approach we call AI Everyday. The idea is simple: make AI a natural, trusted part of day-to-day work, not an extra task or a separate initiative.
AI Everyday is built around three pillars: mindset, skillset and toolset – how we think about AI, the skills needed to use it safely and effectively, and the tools that help teams put it into practice.
Our MYOB Business Monitor data shows around 40% of small and medium-sized businesses expect AI to deliver productivity or efficiency gains in 2026. That highlights a real opportunity to work smarter by weaving AI into daily operations. But the real upside goes beyond efficiency. It’s about rethinking how work gets done and creating conditions for innovation.
AI Everyday shifts the focus from speed to capability. Teams build confidence and curiosity, learn where AI adds value, where human judgement still matters, and how to operate within clear guardrails around data, accuracy and trust.
Over time, AI stops being new or disruptive. It becomes part of how teams work, helping them deliver better outcomes while focusing more energy on the work that really matters.”
Logan Nathan, Founder and CEO, i4T Global
“In my book, “Think Digital – Rewired for the AI Age”, I’ve talked extensively about why training your team on AI shouldn’t feel like you’ve hit pause on the business so everyone can “go, learn”. If it slows people down, it won’t stick. The leadership move is to make AI learning part of the work, not an extra thing on top of it.
Start small and practical. Pick two or three real, repeatable tasks (client emails, meeting notes, proposal drafts). Run a short pilot with a few volunteers, grab what works, then roll it out to everyone with a simple prompt cheat sheet.
Keep the rhythm light: a 5-minute weekly show-and-tell where someone shares one win and one flop (and what they learned). That “fail fast, learn fast” culture is what turns curiosity into capability, without the fear factor.
Set clear guardrails so people feel safe: what not to paste into tools, when a human must double-check, and which tools are approved. Nominate a couple of “AI champions” to help teammates and surface roadblocks for you to remove.
And here’s the kicker: measure outcomes, not hours spent training. If your team is saving time, reducing rework, or responding faster to customers, you’re doing it right. AI adoption isn’t a workshop. It’s a habit led from the top.”
Jarrod Kinchington – Vice president Australia and New Zealand – Smartsheet
“When teams ask how to use AI without losing momentum, the answer isn’t more passive training sessions; it’s creating a secure, structured environment where AI fits naturally into their everyday work. Using our own context at Smartsheet, we’ve seen the strongest adoption when people start small, apply AI to real tasks, and continuously build capability over time. This continuous improvement loop allows teams to experiment, learn quickly, and scale what works—without halting progress.
We’ve also found that enablement is strongest when employees experiment with AI in context. That’s why we embed AI directly into our platform, delivering AI that fits into existing workflows, keeps data secure, and evolves with the business without forcing new ways of working. This allows people to try using AI to generate summaries, analyse data, and even design and start new projects, all while executing real work.
Trust makes this work at scale. Teams move faster when clear guardrails define how and when AI can be used, and when data is protected and governed. Zero-trust principles, strong identity controls, and audit trails support responsible adoption, particularly in regions like Australia, where high privacy expectations prevail. Transparency is also critical. When AI shows what data it used, how insights were generated, and why recommendations were made, employees gain confidence and are better equipped to use AI responsibly.”
Leanne Shelton, CEO, HumanEdge AI Training
“While it might seem counter-productive, you need to actually set time aside to do the team training. And it must be done all together in a room away from day-to-day life.
Why? Well, AI is still a confusing concept for many and being misused and overused by others. As their leader, it’s your responsibility to create a safe space for open discussion and training/customising your company’s chosen GenAI tool (Copilot, ChatGPT, etc) on your business, brand voice, and customers. This will ensure everyone is on the same page – and ensure consistent quality going forward.
From my experience as an AI trainer, you need a minimum of three hours for training. This allows time for discussion about where AI could fit into day-to-day roles, customising your tool, and prompt experimentation in relation to real-life tasks. However, the training support needs to continue via buddy groups, regular AI discussions, and follow up training. It’s not something you can be trained up on in a day and expect a major culture shift.”
Kumar Mitra, Executive Director, CAP & ANZ, Lenovo Infrastructure Solutions Group
“Australian organisations are keen to realise the value of AI, and success starts with people. Lenovo’s 2025 CIO Playbook highlighted that employee training and upskilling remain the most important factors in turning AI ambition into real impact.
The good news is that building AI capability doesn’t require long, disruptive training programs. Given how fast AI is evolving, it shouldn’t. What matters more is helping teams use AI in practical, relevant ways that support their day-to-day work.
Rather than focusing on complex or specialised skills upfront, organisations can start small – enabling employees across roles to apply AI to a few high-value use cases, such as automating routine tasks or summarising information. This helps teams see immediate benefits without slowing them down.
The most effective learning often happens in the flow of work. Lightweight guidance, clear guardrails, and peer support can be more impactful than formal courses alone, especially when expectations are clear and experimentation is encouraged.
When AI training is role-relevant and allows people to build confidence at their own pace, adoption becomes more sustainable. Ultimately, developing AI capability is an ongoing journey. Organisations that keep the focus on people, outcomes, and continuous learning will be best positioned to adapt as AI continues to evolve.”
Matthew Owens, Director, Annexa
“We look at AI less as a capability rollout and more as a working habit that evolves with the team.
Internally, it isn’t top-down. Our people are genuinely enthusiastic about what’s emerging and regularly bring new ideas to the table. The people doing the work know where the bottlenecks and pain points sit and we make sure they have secure tools they can use to find solutions. When someone shares a particularly useful AI-enabled approach, adoption follows naturally.
Those ideas feed into open sessions where we narrow focus to the AI tasks that will save the most time. Meeting preparation, transcription, discovery notes and document drafting were early wins. More recently, we’ve been exploring agent-style AI embedded within existing workflows, moving beyond prompts into more proactive support.”
Caitlin Stephens, Chief of Staff, APAC, Eagle Eye
“Many organisations struggle with AI adoption because they overthink the rollout. Staff are often already using tools like ChatGPT and Claude to get work done, whether it’s sanctioned or not. The real question is how to bring these capabilities into the light so everyone benefits.
At Eagle Eye, we’ve found success by identifying AI champions: curious team members who can guide others through practical, relatable use cases. These champions focus on continuous learning and knowledge-sharing, building confidence across teams.
Leadership’s role is to set a clear mandate, establish guardrails around risk, and encourage safe experimentation. Share available tools, celebrate wins openly, and talk honestly about failures and learnings.
You don’t need to overhaul everything. Many daily systems already have AI assistants built in. Start small, test your use case, collect data and look for gains.
The key is maintaining a growth mindset and recognising that individuals will collaborate with AI differently based on their roles.”
Billy Loizou, AVP & General Manager, APAC, Amperity
“Training teams on AI only becomes a problem when it pulls people away from their actual jobs.
The better approach is to meet teams where they already are. We see marketers get value quickly when AI helps them understand customer behaviour or test ideas faster. Data teams benefit when AI reduces repetitive work or lets them ask questions without jumping through technical hoops.
When people see AI saving time, they don’t need convincing. They use it.
The catch is data. AI surfaces data issues very quickly, and if teams don’t trust what’s underneath, adoption stalls. That’s why the organisations making real progress start by unifying their customer data and fixing identity first.
From there, it’s about focus. Start with one workflow, prove it works, and expand. Training sticks when results show up in day-to-day work, not in slide decks.”
Steve Evans, Founder and CEO, ConnectOS
“For businesses working with distributed teams, AI training should focus on amplifying the expertise you’ve already hired for. Offshore specialists like accountants, developers and customer service specialists bring professional skills. AI makes those skills faster and more scalable.
Start with role-specific applications. Show your finance team how to use AI for data analysis and reporting. Train customer service staff to draft responses and summarise client histories. Give developers tools to accelerate code review and documentation.
The key is embedding AI into existing workflows rather than treating it as a separate capability to learn. Short, practical sessions work better than lengthy courses, particularly when coordinating across time zones.
When offshore teams can deliver faster turnarounds and higher-quality outputs, everyone benefits. Clients get more value, team members develop sought-after skills, and your service offering becomes more competitive.”
David Fischl, Legal Digital Transformation Lead Partner and Corporate and Commercial Team Lead Partner, Hicksons | Hunt & Hunt
“Today’s fast-paced business environment requires organisations to integrate AI training in ways that do not disrupt productivity. At Hicksons | Hunt & Hunt, we use AI to work smarter for clients, enabling us to handle growing complexity at speed, and free up our people’s time to focus on higher value, judgment-based work rather than repetitive tasks.
Some of the ways we have trained our team at Hicksons | Hunt & Hunt include:
- Integrating training into daily work tasks to boost efficiency of existing processes, and create a practical learning environment which doesn’t compromise deadlines or productivity.
- Use existing intelligent tutoring systems which provide 1-1 AI-powered support, working to eliminate the traditional bottleneck of waiting for supervisor guidance, enabling immediate answers to specific questions.
- Implement team-specific training programs to ensure team members focus solely on modules relevant to their role and team, maximising efficiency and eliminating unnecessary time investment.
- Keep training adaptable and content fresh to keep up with the rapidly-changing nature of AI that requires training content to be regularly updated, to prevent wasting time on obsolete information.
- Encourage collaborative, group-based learning where team members share discoveries, exchange effective prompts, and discuss outcomes, fostering a unified, knowledge-sharing environment that strengthens individual capabilities and team cohesion.
This modern and practical approach to training simultaneously enhances individual learning and builds a more capable, forward-thinking organisation ready to embrace AI innovation.”
Greg Wilkes, CEO of Develop Coaching
“AI can lift productivity, but badly run training can feel like a speed bump. The trick is to build confidence fast, focus on real work and remove friction, not add more of it.
Start with purpose first. Don’t run generic “AI 101” sessions. Ask teams what tasks they dread or repeat every day. Then show practical shortcuts for those exact jobs with tools like ChatGPT, Bard or Copilot. Real examples beat theory every time.
Next, do micro-learning in the flow of work. Short 15-minute sessions once or twice a week beat long all-day workshops that pull people off billable work. Send follow-up snippets: one prompt hack per day, quick videos, or examples tailored to your business lingo. Keep it simple.
Pair people up. Run peer clinics where early adopters help others on real tasks. This spreads know-how without needing an expert in every room.
Measure impact early. Track time saved on common tasks, error reduction or faster client responses. Celebrate wins publicly so others get curious, not cautious.
Finally, set clear guardrails on data and privacy so people feel safe experimenting. If they trust the process, they’ll adopt faster, not slower. AI should accelerate work, not overcomplicate it.”
Maria Kathopoulis, CEO & Chief Marketing Officer at UNTMD Media
“You don’t train people to “use AI.” You train them to use AI to remove work, not add it.
Start with one use case per role.
Sales can use AI for call summaries, outreach and follow-up prompts.
Marketing can use it for content variations, audience insights and ad testing.
Operations can use it for SOPs, reporting and workflow automation.
AI becomes a multiplier when it replaces effort. If it feels like extra work, it has been implemented incorrectly. The goal is to make AI their co-pilot, not another task.”
Kathryn Giudes, Founder and Managing Director, Orca Opti
“Most AI training focuses on capability: how to write prompts, which tools to use, what’s possible. That’s the wrong starting point for regulated businesses.
Before your team experiments with AI, establish clear boundaries. What data can be shared with external models? What outputs require human review? Where do audit trails need to exist? Without these guardrails, you’re teaching people to drive fast on a road with no lines.
Training should cover safe usage alongside productive usage. When teams understand the rules, they experiment with confidence rather than caution. They move faster because they know where the edges are.
The businesses getting value from AI aren’t those with the most advanced skills. They’re the ones who built governance into operations from day one, so adoption could scale without creating risk.”
Tim Mole, Head of Data & AI JAVLN
“AI is a modern productivity game-changer unlike anything we’ve seen before. Its impact isn’t limited to one department, it benefits all business functions.
You don’t need heavy training, the key is incremental learning. AI skills are best built in small steps: learn one capability, apply it immediately, then build on it. This creates a flywheel of confidence and impact. Like any skill, success requires conviction, curiosity, and a mindset shift. The ‘aha’ moment is when staff transition from using AI as an “improved Google” to understanding its ability to act as a capable personal assistant.
An approach that is working well for us is when leaders develop an ‘AI Achiever’ culture which encourages and supports learning AI skills and achievements. Like all learning, it is likely you will see a short-term hit to productivity; but from our experience, AI tools quickly deliver positive outcomes and value.
Start with core foundations such as effective prompting with Gemini/ChatGPT delivered through the rich online material available and ‘lunch & learn’ sessions. Follow with focused training on everyday AI embedded tools such as Google/Microsoft Office, CRM etc. Progress to AI agents – the holy grail – which will unlock your greatest productivity.”
Lee Robson, Director, iStories
“Rather than view AI training as time taken time away from the day-to-day, try embedding AI upskilling directly into daily operations, turning potential slowdowns into immediate productivity gains.
Start with micro-sessions: 5-minute tutorials on tools like AI-driven content generators applied to live tasks. This “learn-by-doing” approach lets teams experiment in sandbox environments while maintaining output, and building confidence without dedicated downtime. Teams see 20-30% efficiency boosts as AI handles repetitive chores like data entry, freeing hours for strategic storytelling.
In tandem, look to train “AI champions” – quick learners who become in-house guides – first, then foster peer sharing via quick demos or instant messaging channels. This collaborative model accelerates adoption, as employees trust colleagues over external sessions.
And finally, provide prompt libraries, ongoing tip sheets, and weekly 1-hour pilots on high-impact tools, tracking progress via simple KPIs. Question the status quo: Why let manual drudgery stifle innovation when AI unlocks human creativity?”
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