Latest news with #organizationalChange
Fast Company
12 hours ago
- Business
- Fast Company
How AI agents and copilots are rewriting the rules of consulting
AI agents and copilots are more than productivity tools—they are transforming the consulting model itself. From solutioning to delivery, talent development to client interaction, they're redefining the skills, structures, and strategies needed to succeed. As a practice leader at the intersection of business solutions, AI, and organizational change, I've seen firsthand how this shift is playing out and where it's going next. HUMAN + MACHINE: A NEW MODEL FOR DELIVERY AI agents are fundamentally reshaping how we think about delivering and consulting. The old model—staffing a team and executing tasks step by step—is evolving. Now, when we build teams, we don't just think about human talent. We think about the tools we'll use to help us make smarter decisions, automate repetitive processes, and surface insights in real time. This kind of human-machine collaboration means we can spot risks earlier and optimize delivery more efficiently. We're also using automation for tasks like running test cases, which used to require significant manual effort. Now we're cutting down on that overhead, which means faster timelines, fewer errors, and, ultimately, better prices for our clients. THE EVOLVING ROLE OF CONSULTANTS One of the biggest shifts is in what entry-level consulting looks like. There's a lot of talk—and some fear—about copilots replacing people. This is understandable. But what I'm seeing isn't replacement, but transformation. The nature of entry-level work is changing. Rather than doing the manual tasks that lead up to analysis, new consultants are stepping in at the 'what do we do next?' stage. Instead of generating the data, they're interpreting it. They need to be ready to assess project plans, spot risks, and make decisions based on the information AI provides. That requires rethinking what early career experience should look like. Graduates and newcomers need strong analytical skills and a mindset for continuous learning. Education and certifications still matter, but they have to be paired with curiosity and critical thinking. THE EXPECTATIONS GAP Client expectations around AI are skyrocketing. They want faster results, lower prices, and fewer people involved, and they want it all immediately. But while expectations are high, the full value of AI will still take time to realize. This is where some education has to happen. Some clients are very fluent in AI. Others are just starting to understand what's possible and what isn't. It's important for us to help them see the opportunity while also being honest about what AI can (and can't) do right now. The pace of change is stunning, but that doesn't mean we can instantly deliver everything AI promises. There has to be a learning curve on both sides. DON'T WAIT FOR PERFECT If there's one piece of advice I offer to clients and teams when it comes to AI, it's this: Don't wait. Don't wait for perfect tools. Don't wait for someone else to figure it all out first. The companies embracing AI now, speed bumps and all, are the ones that will lead their industries tomorrow. Will the first AI tool you try be perfect? Probably not. But that's okay. We all need to iterate. Try, assess, adapt. The wins won't all come at once, but they'll come faster than if you sit on the sidelines. We need to design consulting engagements with outcomes in mind, not just tasks. Because the tasks are being handled differently now, with value delivered through human-machine collaboration. Clients should expect that kind of delivery model, and they should align their objectives accordingly. Having subject matter experts embedded in this environment alongside fast-moving AI tools is where we'll see the most value in the short term. THE FUTURE IS AGILE Looking ahead, I'm incredibly optimistic when I think about the tools we don't even have yet: what they could unlock, how much faster we could deliver, and how much higher the quality could be. Maybe we'll even start breaking up large projects into smaller, more value-driven phases because the technology makes it possible. But I'm also realistic. The pace of AI development means the skills required in consulting are going to keep changing. If you want to be in this business, you have to be willing to adapt—not just every year or every few years, but continuously, and faster than before. Because here's the truth: AI isn't just rewriting the rules of consulting. It's writing a whole new playbook. And if we want to stay in the game, we've got to keep turning the pages right along with it.
Harvard Business Review
3 days ago
- Business
- Harvard Business Review
Leading Through Continuous Change
The amount and pace of organizational change today is staggering. Gartner research found that in 2022, the average employee experienced 10 change initiatives—up from two in 2016. And this comes against a backdrop of rapid technological development and geopolitical shifts. Change is no longer episodic; it is continuous—and people are tired.
Forbes
13-05-2025
- Business
- Forbes
AI adoption in organizations: Will it hit the buffers?
AI is developing at breakneck speed. Every day new developments and breakthroughs are announced. The big players in the west like Google, Microsoft, Amazon, Tesla, Nvidia are making big bets on commercial success and in the east China is in the mix too with DeepSeek and government backed programs. The AI 'space race' is fully in play. But whilst the technology is advancing rapidly, the critical question for me remains: are organisations really ready to harness AI's full potential to achieve the promised 15%-30% productivity improvements being predicted by the World Economic Forum? Or will corporate AI adoption hit a wall. I recently led a study group working with AI industry experts involving a number of organisations from different sectors that examined the implications of AI on jobs, people, and organisational structures. The experience has been illuminating, leading me to conclude that successful AI adoption is predominantly an organizational and behavioural change challenge and less of a technological one. Yet paradoxically, many companies are treating AI as a technology deployment, handing the reigns over to their CIOs. As part of our research, we developed a simple questionnaire designed to identify AI's impact on specific jobs by examining several key factors. Participating organisations were asked to invite employees to complete this survey, but few actually did. When pressed for reasons, we discovered that they were uncomfortable even raising the subject of AI with their employees for fear of the questions that might arise. Consider the dilemma: what happens after an employee completes the questionnaire revealing their jobs could change radically? This triggers awkward questions: Is my job secure? Will AI reduce our headcount? What will happen to me? Will I be retrained? Making matters worse, "AI", for many remains a fuzzily defined concept encompassing various technologies from Large Language Models (LLMs) like Co-pilot, Chat GPT, Co-Pilot, Gemini and Claude to sophisticated machine learning algorithms. This ambiguity compounds the challenge – leaders are rightly uncomfortable about engaging employees on topics they themselves don't fully understand making AI adoption tough. These conversations become particularly fraught in organisations facing competitive pressures and declining margins, where leadership may view AI primarily as a cost-reduction tool. Companies experiencing growth might frame AI more positively as enabling increased work capacity without additional hiring. However, this narrative doesn't resonate in organisations whose leaders are explicitly seeking efficiency gains through cost base reduction. Successful AI adoption requires, at minimum, a clearly articulated narrative and comprehensive education programme to build leadership and employee AI competence and confidence. Organisations must develop honest, coherent communication and change programs and prepare for challenging questions before initiating broader AI conversations. Cultural trust matters enormously too in AI adoption. Organizations with high levels of trust between leadership and employees can have difficult conversations much more easily and honestly than those that don't. Where leadership has demonstrated a consistent record of transparency, care and ethical change management, employees approach AI conversations with greater openness, less immediate suspicion and less 'resistance'. Conversely, in low-trust cultures where leadership credibility has eroded through the historical breaking of promises, contradictory actions and statements, AI initiatives are likely suffer from resistance from a justifiably sceptical workforce limiting the ability to achieve the productivity benefits of higher trust organisations. Beyond cultural challenges, there are numerous technical hurdles. Many IT departments have restricted external AI tool deployment due to legitimate concerns about: These technical concerns intersect with growing ethical and legal considerations surrounding AI adoption, including: A comprehensive global study by Deloitte involving over 2,700 executives found that identified governance, ethics, and regulatory compliance as significant barriers to AI implementation, outranking even technical and skills challenges (Deloitte, "State of AI in the Enterprise", 2024). Without comprehensive change programs addressing education, skills, communication, employee engagement, technology, processes, governance, and regulation, widespread AI adoption will be slow. Organisations will go up blind alleys hitting adoption buffers and the promised productivity gains will be hard to achieve. Leaders that treat AI as an opportunity for organisational renewal, systematically evolving their organizations for a new world will reap the much promised productivity gains and associated competitive advantage. Those that don't may well be left behind.
