📚 Education transforms AI fear into understanding, empowering employees to embrace change.

🔒 Transparency in data governance builds trust, reducing privacy and security concerns.

🛠️ Proactive job redesign reassures teams, turning automation anxiety into career opportunities.

💬 Open conversations address AI misconceptions, fostering a confident, collaborative workplace culture.

🚀 Celebrating early AI successes reinforces enthusiasm, driving long-term organisational resilience.

Educating Your Organisation to Embrace AI

As an executive who has championed several technology transformations, you know that the toughest hurdles aren’t technical – they’re human. When you introduced an AI initiative in our boardroom, you were met with concerned glances and tough questions. Will AI displace jobs? Can we trust it with our data? Do our people even understand it? These are legitimate questions that reflect a broader resistance to AI adoption across many organisations. Overcoming this resistance comes down to one crucial strategy: education. In fact, in our Fast Implementation Track (F.I.T.) for AI projects, we emphasise an “Educate” phase as the foundation for success. By actively educating ourselves and our teams, we address fears and build the confidence needed to embrace AI.

I’ll expand on three common themes behind AI resistance – job displacement fears, data privacy concerns, and AI literacy gaps – and share how an education-focused approach can turn these challenges into opportunities.

Understanding the Roots of AI Resistance

Before we can overcome resistance, we must understand it. In conversations with colleagues and teams, I’ve found that most AI pushback boils down to three core anxieties:

• “AI will take our jobs.” Employees fear that automation and algorithms will make their roles obsolete. This fear of job displacement can breed mistrust and low morale if not addressed.
• “AI will misuse our data.” Both leaders and staff worry about data privacy and security. They question whether adopting AI could expose sensitive information or violate regulations, creating hesitation to proceed.
• “AI is too complex for us.” There’s often a knowledge gap – many people just don’t understand AI and feel intimidated by it. This lack of AI literacy fuels misconceptions and rumours, making folks resistant to something they perceive as an unknown threat.

Each of these concerns is valid. As executives, we should never dismiss them. Instead, we should tackle them head-on through education, transparency, and engagement. I’ve broken down the discussion into these three themes, with strategies under each to show how Educate (the F.I.T. component) comes into play. Throughout, you’ll notice a pattern: turning fear into understanding, and uncertainty into empowerment.

A conceptual image of a robot hand interacting with a keyboard, symbolising how AI can handle routine tasks. Executives must help employees see that such automation augments human work instead of eliminating it.

Confronting Job Displacement Fears with Empowerment

One of the most common fears you hear from employees (and even some managers) is that AI will replace them. In today’s workplace, “the fear of AI-led job displacement is becoming increasingly common”. It’s easy to see why – reports often highlight huge numbers of jobs “exposed” to AI, and sensational headlines feed anxiety that “AI is coming for our jobs!”. But as leaders, we have a responsibility to paint a more accurate and hopeful picture.

First, let’s clarify the reality. Yes, AI will change jobs – but change does not equal elimination. Studies indicate that while many roles will be impacted by AI, very few will be completely replaced in the near term. For example, a Goldman Sachs study estimated up to 300 million jobs could be affected by automation globally, but importantly noted that most of these jobs will be “complemented rather than substituted by AI”. In fact, economists found that currently only about 2% of annual job cuts can be attributed to AI adoption, suggesting that outright replacement is happening at a far lower rate than people fear. What these numbers tell us is that AI typically takes over specific tasks, not entire jobs – and it often creates new tasks and roles in the process.

You make it a point to share these facts when talking to your team. Seeing hard data that most jobs are only partially automated (and that new roles are emerging) helps defuse the “robot takeover” narrative. But data alone isn’t enough – we must also actively engage employees in envisioning their future with AI, rather than versus AI. Here are some approaches I’ve found effective:

• Emphasise augmentation, not replacement. You consistently communicate that the goal of your AI initiatives is to upgrade our human teams, not downsize them. For example, if AI can handle the repetitive 30% of someone’s workload, that frees them to focus on the 70% that requires creativity, relationship-building, and strategy. I often say, “We’re giving you a smart assistant, not a replacement.” This reassurance is crucial. It’s also credible when backed by examples – such as internal pilots where AI took over tedious data entry and employees were then reassigned to more analytical tasks they found rewarding. When people see AI as a tool to eliminate drudgery and amplify their impact, they start to view it as an ally.
• Provide reskilling and upskilling opportunities. Education is the antidote to job fear. We can’t just tell folks “don’t worry, you’ll do something else” – we need to help them develop the skills for those new opportunities. Invest in training programs for roles that AI will augment. For instance, when we introduced an AI-driven analytics platform, we offered workshops for our analysts to learn data interpretation and AI oversight skills. This gave them a sense of mastery over the new tools. They moved from fearing the AI to feeling “I can do more now that I know how to work with this.” The organisation benefits from a more capable workforce, and employees gain a portable skill set for the future. It’s a win-win that builds resilience – our people can adapt as technology evolves.
• Redesign jobs and career paths proactively. A constructive way to address “what will happen to my job” is to have an answer ready. Don’t wait for AI to make a role partially redundant – get ahead by redesigning the role to incorporate AI and outlining that plan to the role’s occupant. Create new job descriptions like “AI-augmented Marketing Specialist” and “Automation Supervisor” to show how traditional roles (marketing specialist, operations supervisor) would evolve with AI assistance. Present these to the team members well before the AI systems went live, along with personal development plans to reach those roles. By providing a roadmap, you replace panic with purpose. People see a future for themselves in the AI-enabled organisation, which drastically reduces resistance.
• Highlight new roles created by AI. It’s important for everyone to realise that AI adoption isn’t just a destroyer of jobs – it’s also a creator of jobs and opportunities. Make a point to highlight examples in your industry: the rise of roles like AI ethicist, data curator, AI product manager, chatbot designer, and so on. These didn’t exist a few years ago and are direct outcomes of AI initiatives. Open internal vacancies for “AI champions” in each department – a part-time role where an employee spends a fraction of their week guiding colleagues on using AI tools (more on that later). This signals that embracing AI can actually open new career avenues. Some of your staff who were in stagnant positions now have a chance to become go-to experts in exciting new domains. By framing AI as a source of growth, we turn the fear on its head – the challenge becomes an opportunity.
• Foster open dialogue and listen to concerns. Finally, never underestimate the power of simply letting employees voice their fears and truly listening. Schedule town halls and informal Q&A sessions whenever we roll out a significant AI project. In these forums, I invite anyone to ask the tough questions – “Are we automating my department?”, “How will my goals be set if AI does X part of my work?”, etc. Answer candidly, and if you don’t have an answer yet, commit to finding one. Sometimes the best outcome of such a session is not that fears are fully resolved (that can take time), but that employees feel heard and involved. It builds trust when you acknowledge their feelings: “I understand you’re worried – that’s natural. Here’s what we’re doing to ensure everyone finds their place in the new setup….” This culture of open communication makes the organisation more resilient, because people are less likely to resist in silence or spread rumours; instead, they bring issues to the table where we can address them rationally.

By taking these steps, we exercise rational control over the narrative of AI in our workplace. Rather than letting “AI will take our jobs” fear spiral out of control, we provide a balanced, factual perspective and a plan. We help our teams build resilience by equipping them for change, and encourage mastery by developing their skills. The result is a workforce that doesn’t just accept AI, but is eager to leverage it. In one of our divisions, after extensive training and job redesign, a survey showed a significant attitude shift – employees overwhelmingly agreed that “AI will improve our work” instead of “AI will replace our work.” That’s when you know resistance is giving way to enthusiasm.

Addressing Data Privacy and Security Concerns Through Transparency

The second major source of AI pushback in the boardroom is data privacy. This concern often comes up from multiple angles: board members worry about compliance and reputational risk, IT teams raise flags about cybersecurity, and employees themselves wonder how their personal or customer data will be used by AI. In fact, data privacy has rapidly climbed to the top of the list of ethical worries surrounding AI. According to a 2024 Deloitte survey, nearly 75% of business and technology professionals ranked data privacy among their top three concerns with enterprise AI – and 40% cited it as the number one concern, up from 25% the year before. Clearly, if we don’t address this issue, our AI efforts might never leave the launchpad.

When discussing an AI project with your peers and team, make it clear that privacy is a priority, not an afterthought. This is where the “Educate” component extends beyond just employees – it’s about educating everyone, including leadership, on how we will safeguard data in the age of AI. Here’s how you turn privacy concerns from a show-stopper into a chance to build trust:

• Demystify how AI uses data. A lot of privacy fear comes from the unknown. People imagine AI as a black box hoovering up unlimited data. Counter this by explaining in plain language what data your AI will actually use, and for what purpose. For example, if you’re deploying an AI customer service chatbot, outline: It will access our customer query database and knowledge base articles, nothing else. It will not read personal emails or browse unrelated files. By spelling out the scope, we draw clear boundaries that make the technology less ominous. Often hold internal workshops with your data scientists or IT security officers co-presenting, so they can answer technical questions about encryption, access controls, etc. When people understand the specifics, their vague fears often subside. This is a form of proactive education that pays huge dividends in trust.
• Implement strong data governance and shout it from the rooftops. It’s not enough to have good data protection measures – you need to publicise them internally. Your AI policy (which I’ll touch on shortly), has strict guidelines aligned with GDPR and other regulations. For instance, ensure any personal data used to train AI is anonymised and that you have consent where required. Also built in automated data logging to track how AI systems access sensitive info. These safeguards are effective, but their effect on reducing resistance comes when we communicate them clearly to the organisation. Send company-wide memos detailing your privacy-by-design approach for AI projects, and include a briefing on data protection in every AI training session. Knowing that specific measures are in place to comply with laws and protect information helps skeptics feel that we’re exercising responsible, rational control over AI, rather than unleashing a wild system that might leak data.
• Engage compliance and legal early. One practical tip for executives: bring your compliance officers and legal counsel into the AI planning process from day one. Make this a standard practice. Their input helps identify potential privacy issues before they become problems, and it also gives them confidence in the project. When board members or employees ask, “Have we considered privacy X or Y?”, I can answer: “Yes, our compliance team has signed off, and we have protocols A, B, C in place.” This not only educates the concerned parties on what’s being done, but it signals that we are in control and accountable. It transforms a challenge (regulatory compliance) into an opportunity to strengthen our processes – for example, by updating our data handling procedures company-wide, not just for the AI project. One colleague in the banking sector shared that their early collaboration with compliance on AI led to improvements in how all data (not just AI-related) was managed in the firm – a net positive outcome.
• Be transparent with customers (and employees) about data use. Trust is the currency of both our workforce and our customer base. Advocate for transparency externally as well: if you’re introducing AI that touches customer data, we issue clear communications to your clients about what you’re doing and how their information is protected. This might seem like a PR or customer service move, but it feeds back into internal morale too. Employees take pride knowing you operate honestly. It creates a culture where privacy isn’t a secret burden, but rather a value we champion. In the long run, this transparency can become a competitive advantage – customers prefer companies that use AI responsibly. As one commentary noted, stringent privacy regulation like GDPR, while sometimes seen as a hurdle, can “help create the trust that is necessary for AI acceptance” among the public. You find that when your team sees you taking the high road on data ethics, it galvanises their support for AI projects (instead of them feeling they have to apologise for or hide what the company is doing).
• Develop a robust AI use policy. To address privacy (and other risks) systematically, create a formal AI Policy document. This policy, approved by your board, outlines how you select AI vendors (including requiring certain security standards), how you handle data (e.g. retention, anonymisation, access logs), and who is accountable for monitoring AI systems. It also covers ethical use guidelines. Require all departments to abide by it when implementing AI. Now, drafting a policy might sound dry, but it’s been incredibly helpful as an educational tool. Roll it out with briefing sessions, so everyone understands the do’s and don’ts of AI usage. For example, one rule is that no one is allowed to plug sensitive company data into an external AI service without clearance – this addresses the fear that someone might, say, feed a private document to ChatGPT and create a leak. By clearly stating what is allowed and what isn’t, and training staff on these points, you remove a lot of the “wild west” feeling that sparks privacy anxieties. Employees know there’s a framework keeping our AI use in check.

An employee interacts with an AI chatbot on his screen. Educating your team about how AI systems work and what data they use builds trust. When people see that safeguards and policies are in place, they become more comfortable engaging with AI tools in their daily work.

• Reinforce cybersecurity hygiene alongside AI rollouts. Privacy and security go hand in hand. A concern I often hear is “Will introducing AI make us more vulnerable to hacks or breaches?” To counter this, ensure that every AI project has a security review and that you upgrade your cybersecurity measures as needed. For instance, when deploying cloud-based AI services, tighten your network access controls and conduct penetration testing. Just as crucially, remind all staff of security best practices during AI training. AI doesn’t exist in a vacuum – employees still need to beware of phishing, use strong passwords, etc., especially as AI tools may have access to sensitive data. By linking your AI adoption to a refresh on data security training, show that AI isn’t weakening your stance, it’s prompting you to become even more robust and resilient. This proactive stance turns a challenge into an opportunity: our organisation becomes safer overall.

Addressing data privacy concerns is ultimately about building trust. When people trust that AI is being implemented carefully and ethically, their resistance melts away. Initially, sceptical executives became strong AI advocates after they saw the thorough privacy safeguards established – they moved from “I’m not sure about this” to “We can proceed, provided we maintain these standards.” By educating everyone about the responsible AI practices you follow, you not only overcome objections but also set a tone of rational control and integrity. The message is clear: we use AI, but we remain firmly in control of our data and destiny.

Bridging the AI Literacy Gap at All Levels

The third barrier – and perhaps the most fundamental – is the lack of AI literacy. Simply put, if people don’t understand something, they’re likely to resist it. I’ve encountered highly intelligent, capable colleagues who are experts in finance, law, or operations, but who have only a cursory idea of what artificial intelligence actually means for their work. This gap in understanding breeds misconceptions, which in turn breed fear or dismissal. Many fears and anxieties about using AI are due to a lack of understanding and education, as one industry blog noted, and I agree completely. The antidote is straightforward: education, education, education.

When we talk about AI literacy, we mean empowering everyone – from the C-suite to the front line – with a working knowledge of AI’s capabilities and limitations. It’s not about turning everyone into data scientists, but ensuring they grasp the basics and can comfortably engage with AI in their context. As one MIT Sloan article highlighted, organisations that unlock AI’s potential tend to have leaders who possess deeper knowledge of AI’s functionality. That ethos needs to cascade down as well. Here’s how you approach building AI literacy in your organisation:

• Lead by example – educate yourself and your leadership team. I’ll admit that a few years ago, I myself had only a high-level understanding of concepts like machine learning, neural networks, or generative AI. I made it a priority to change that. I took online courses and continuously pushed myself out of my comfort zone. It paid off. When leaders can talk the talk – even just the basics – it builds credibility with the rest of the organisation. Employees will tell you they feel more at ease knowing that “our CEO actually understands this stuff.” So, step one is to not shy away from the technical details. Encourage your fellow executives to do the same. Make technology-related learning a habit for leadership; this attitude will trickle down and make continuous learning a norm for everyone.
• Offer accessible AI education for employees. Launch an internal “AI 101” program as part of the Educate phase. These are regular sessions (virtual and in-person) open to all employees, where you cover fundamental questions: What is AI? How does it work? What can’t it do? Deliberately keep the tone friendly and non-technical. For instance, use analogies (“training an AI is like teaching an intern with hundreds of examples”) and interactive demos. The goal is to replace the mystery with a basic familiarity. Also address common myths head-on (e.g., “AI is infallible” or “AI will take over the world”) with facts and discussion. Over time, see these sessions boost confidence: people start using AI terms correctly, and they stop attributing almost magical qualities to it. Instead, they see it as another tool – powerful, yes, but ultimately created and guided by humans. That mindset shift is crucial for adoption.
• Tailor education to roles and departments. Different teams have different training needs. Your salespeople, for example, want to know how AI could help them sell better and what it meant for customer relations. Your HR staff are more interested in how AI might assist in recruitment or performance analysis, and what biases to watch out for. Tailored workshops for each department focusing on relevant use cases. This makes the learning immediately applicable. It answers the “what’s in it for me?” question and turns sceptics into curious participants. Imagine a finance team workshop where their eyes light up upon learning how AI could automate parts of financial reporting – a task they loathe. They go from lukewarm to enthusiastic in one afternoon. The key was making the training contextual. One size does not fit all in education, so invest the time to customise.
• Create AI champions and peer learning networks. One of the best strategies is establishing a network of AI Champions across the company. These are tech-savvy volunteers from different departments who receive more advanced training and serve as liaisons. As Dataiku’s AI literacy framework suggests, “executives can create AI champions in each department, develop interdisciplinary AI project teams, encourage knowledge-sharing platforms, and run dedicated AI workshops”. Your AI champions can meet biweekly to share what they’re learning and to harmonise strategies. Importantly, they can act as go-to support for their peers. If someone in marketing has a question about using our new AI analytics tool, there’s a friendly colleague right there to help, rather than having to call IT. This peer-to-peer model greatly accelerates learning. It also decentralises the effort – it’s not just top-down preaching about AI; it’s colleagues helping colleagues, which normalises AI usage in everyday work. The champions have also become an informal feedback channel, bringing front-line insights back to the AI development team or leadership, which helps us tweak our approach continuously.
• Encourage hands-on experimentation (with guardrails). Nothing builds literacy faster than doing. Set up “AI Sandboxes” – safe environments where employees can play with AI tools on non-sensitive data or dummy scenarios. For instance, provide a sandbox version of a chatbot builder for anyone interested to try creating a bot that answers common questions about their department. This came from the principle that “employees should feel empowered to explore AI tools and integrate them into their work… offer sandboxes and low-stakes experimentation zones”. Not only do people learn by doing, but you also get a flurry of creative ideas as a bonus! For example, one staff member outside of IT builds a prototype FAQ bot for your intranet that you likely would not develop centrally. That kind of grassroots innovation only happens when you remove the fear of breaking things. Of course, we set some guardrails – e.g., the sandbox is in a controlled cloud space, not on live systems, and caution not to put real customer data in there. With those precautions, the sandbox becomes a playground for skill-building. It fosters a sense of mastery and confidence. After all, once you’ve built a simple AI model yourself, using one that the company provides feels far less intimidating.
• Integrate AI into everyday language and business processes. Make AI a recurring topic in your internal communications and meetings. For example, in weekly team meetings, managers share any AI tool tips or successes (“This week, our content team used an AI tool to draft social media posts, saving 5 hours”). Adjusted your project templates to include a section: “AI/Automation Opportunities” so that any new initiative considers if AI could help. By weaving AI into the fabric of daily work life, it ceases to be this special, scary thing and becomes part of the norm. Essentially, we’re cultivating a culture of continuous learning and curiosity. Teams are now comfortable enough that they themselves suggest ideas for AI pilots. That’s a huge leap from the early days when I had to push and persuade teams to even consider using AI. It underscores that raising literacy transforms mindsets – people move from resistance to proactively seeking ways to leverage AI.
• Showcase quick wins and celebrate learners. When individuals or teams make strides in AI adoption, we publicise it. For instance, if the customer support team successfully implements an AI-driven ticket triage system, share that story in the company newsletter: “Kudos to the support team for embracing AI to speed up response times by 30%! Here’s what they did….” Also highlight personal development, like announcing certifications or courses employees completed in AI. Recognising these efforts rewards the learners and sends a message company-wide that this knowledge is valued. It motivates others to jump on board the learning journey. Over time, this contributes to a resilient, innovation-friendly culture. When people see their peers being celebrated for adapting and growing, they are more inclined to see change as opportunity rather than threat.

All these actions feed into a virtuous cycle: greater understanding leads to greater acceptance, which leads to more successful AI outcomes, which further reinforces understanding. Your workforce becomes not just receptive to AI, but capable with it. One could say we’re collectively achieving a level of mastery over the technology appropriate to our business needs. We’ve moved from fear of the unknown to confidence in our capabilities.

To put it in perspective, not long ago an employee might have said, “I don’t get this AI thing, I’ll just avoid it.” Now you hear, “I think AI could help in this task – can we try it?” That transformation is the direct result of prioritising education and literacy. It is deeply linked to the “Educate” phase of our Fast Implementation Track, proving that when you invest time up front to teach and learn, you earn back dividends in smoother, faster implementation later.

Practical Steps to Educate and Engage Your Organisation

Throughout the above sections, we’ve touched on many actionable ideas. Let’s summarise some practical steps you as a boardroom executive can take right away to turn AI resistance into enthusiastic adoption:

1. Start with Honest Conversations: Begin by acknowledging the fears in your organisation. Host an open forum or send a personal note inviting concerns about AI. Simply listening will earn trust and give you a clear map of what needs to be addressed.
2. Craft and Communicate a Vision: Clearly articulate why your organisation is adopting AI and how it benefits everyone – not just the bottom line, but employees, customers, and even society if applicable. When people see a purpose, they are more willing to get on board.
3. Develop an AI Education Program: Don’t leave AI understanding to chance. Set up a structured program to educate employees at all levels. This can include AI 101 sessions, role-specific workshops, newsletters with AI tips, and an internal wiki or resource hub answering common questions.
4. Empower AI Champions: Identify tech-savvy and enthusiastic individuals in various departments and make them “AI Champions” or ambassadors. Provide them extra training and let them spearhead AI initiatives in their domains. They will be your grassroots change agents spreading knowledge peer-to-peer.
5. Implement Governance Early: Work with compliance, IT, and legal to establish an AI governance framework (policies on data use, ethics, security). Educate the organisation about these guidelines so everyone knows the rules of the road. This addresses concerns proactively and avoids painful mistakes.
6. Show Quick Wins: Pilot a small AI project in a receptive team to generate a success story. For example, automate a simple report or implement a tiny AI feature on the intranet. When you showcase a successful use case (especially one that made employees’ work easier), it builds positive momentum and silences some critics.
7. Invest in Training & Reskilling: Allocate budget and time for continuous learning. This might mean online courses, workshops with external experts, or certification programs. Make it easy for your people to acquire the skills to work alongside AI. Notably, also train managers on how to lead teams augmented by AI – their role will shift towards more coaching and strategic thinking.
8. Foster a Culture of Experimentation: Encourage teams to experiment with AI in a low-risk manner. Perhaps run an innovation challenge: “Come up with an AI idea for your department, we’ll support the best ones.” This turns passive observers into active participants. Remember to give them sandbox environments and IT support so they can tinker safely.
9. Communicate Regularly and Authentically: Keep the dialogue going. Provide updates on AI projects, address new concerns that arise, and be transparent about setbacks or changes. Consistent communication prevents the rumour mill from taking over and reinforces that leadership has a steady hand on the implementation.
10. Celebrate Adaptation: Finally, acknowledge and reward teams and individuals who embrace AI in their work. Whether through formal recognition or simple shout-outs, show that you value the growth and adaptability of your people. This positive reinforcement inspires others and solidifies the mindset that learning new things is part of who we are as an organisation.

These steps, rooted in education and engagement, embody the spirit of resilience, rational control, and mastery. We prepare our people to adapt (resilience), we manage the AI journey deliberately and thoughtfully (rational control), and we enable everyone to gain proficiency in new tools (mastery). In doing so, we transform AI from a scary unknown into a lever for empowerment.

Embracing the Future: From Resistance to Resilience

Every AI adoption hurdle is also an opportunity in disguise. Fear of job loss nudges us to invest in our people’s growth so they become more skilled and versatile than ever. Privacy concerns compel us to strengthen our data practices and build a reputation for trust that sets us apart. Lack of understanding prompts us to create a learning organisation where curiosity is valued and knowledge is shared freely. By facing these challenges head on, we end up not only implementing AI, but also becoming a better organisation in the process.

The boardroom executives I admire most are those who lead with both head and heart in this AI journey. The head ensures we approach adoption rationally – with strategies, policies, and evidence-based decisions. The heart ensures we bring our people along – with empathy, transparency, and empowerment. When you combine the two, resistance doesn’t stand a chance.

Our Fast Implementation Track’s Educate component has proven to be the linchpin for success time and again. Educating broadly and deeply creates a foundation of understanding that makes the later phases (the actual technology implementation, process integration, etc.) so much faster and smoother. In one case, an AI tool deployment that might normally take a year of back-and-forth only took a few months – largely because the users were prepared, even excited, to adopt it. They pulled the solution in, rather than us having to push it on them.

As board leaders, set this tone from the top. Champion education and address concerns with action, model how to be resilient in the face of change. Show that you are not at the mercy of technological trends – you master how technology is woven into your organisational fabric. And importantly, affirm that your people are your greatest asset in any transformation, not a cost to be minimized.

I encourage you to take these insights and tailor them to your own context. Every company has its own culture and challenges, but the theme of Educate-before-Execute is universally applicable. If you’ve read this far, you’re clearly invested in guiding your organisation through change with wisdom and care. Keep that spirit – it will serve you well.

Let me leave you with a question to ponder and perhaps discuss with your team: How can you further educate and inspire your workforce to see AI not as a threat, but as an opportunity to reach new heights? I’m curious to hear your thoughts and experiences. After all, we’re all learning on this journey, and by sharing, we only grow stronger. So, how are you turning AI resistance into resilience in your organisation? Let’s continue the conversation – the future is ours to shape.