There’s a reason more leaders are rethinking their operating models right now. Volatility is up. Expectations are higher. AI is accelerating everything. The question isn’t whether to transform. It’s whether your model can keep up. Our latest KPMG Managed Services Outlook Survey, developed with IDC, explores how organizations are building for speed, resilience, and continuous change. If you’re thinking about what the next version of your business looks like, start here. https://lnkd.in/dTAjwZMp

Business transformation is getting harder. What I see across organizations today is a growing tension between cost pressure, cultural alignment, and the need to scale globally. At the same time, AI is accelerating everything, but often in fragmented ways that create more complexity down the line. The biggest risk right now is moving without a clear baseline. Too many companies are layering new tools onto existing systems without fully understanding their processes, data, or operating model. That is where transformation efforts start to break down. At the same time, AI is changing expectations. The ability to deliver more with less is quickly becoming the standard, not the exception. In a recent interview, I shared what I believe are the most important challenges organizations will face in 2026, along with practical ways to approach them more effectively. Check it out here: https://lnkd.in/gXzRJaQT

Most executives can't answer this question: "What AI maturity stage is your organization actually in?" Not what you wish you were. Not what your competitors claim to be. Where you actually stand today. Here's what I've learned after assessing 100+ mid-sized organizations: The gap between stages isn't technology → It's strategic clarity Companies waste millions trying to sprint to Stage 4 when they haven't mastered Stage 1. That's why we built the VSG AI Readiness Maturity Model. Four distinct stages. Four different investment strategies: * Stage 1: Awareness → Know what's possible before you spend a dollar * Stage 2: Experimentation → Test without betting the company * Stage 3: Integration → Scale what actually works for YOUR business * Stage 4: Optimization → AI becomes your competitive advantage The companies pulling ahead in 2025? They know exactly which stage they're in. They invest accordingly. They don't skip steps. As Bill Gates said: "We always overestimate the change that will occur in the next two years and underestimate the change that will occur in the next ten." The question isn't whether AI will transform your industry. The question is: Will you lead that transformation or react to it? Here's my challenge to you: Based on these four stages, where does your organization honestly sit today? Drop your stage number in the comments 👇 (No judgment - just honest assessment leads to smart strategy) P.S. At Visionary Solutions Group, we help mid-sized businesses move through these stages strategically — investing in the right capabilities at the right time, without wasting resources on premature scaling. Ready to assess your AI readiness and build your custom roadmap? → https://lnkd.in/d9ZCdypq #AIStrategy #BusinessTransformation #DigitalMaturity #MidSizedBusiness #LeadershipDevelopment

All smiles because... 93% of CIOs in Europe and the Middle East plan to increase AI investment this year. They're committing, no longer just experimenting. In our latest CIO Playbook, what stood out to me was not just the scale of ambition, but the shift in mindset. Nearly half of AI proof of concepts have already moved into production, with organisations projecting strong returns. The question is no longer “Should we invest in AI?” but “How do we make it work across the business?” From where I sit in Digital Workplace Solutions, the answer is rarely about one big platform decision. It's about integration, governance and adoption. Because scaling AI responsibly requires more than infrastructure. It requires clarity of purpose and a workforce that understands how to use these tools confidently and effectively. AI is becoming embedded in everyday work. Our role is to make sure it is usable, secure and genuinely valuable. How are you turning AI ambition into measurable impact this year? #CIOPlaybook #DigitalWorkplace #SmarterTechnologyForAll

As organizations navigate a convergence of shifts across AI, operating models, and business priorities, a more fundamental question is emerging: 𝗜𝗻 𝗮 𝘄𝗼𝗿𝗹𝗱 𝘄𝗵𝗲𝗿𝗲 𝗔𝗜 𝗶𝘀 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗶𝗻𝗴𝗹𝘆 𝗮𝗰𝗰𝗲𝘀𝘀𝗶𝗯𝗹𝗲, 𝘄𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗱𝗲𝗳𝗶𝗻𝗲𝘀 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁𝗶𝗮𝘁𝗶𝗼𝗻? Most large organizations today operate with scaled, globally distributed teams, with access to AI, talent, and infrastructure. Increasingly, so do their clients and partners. At the same time, while a significant majority of organizations are experimenting with or deploying AI, far fewer are translating this into meaningful, enterprise-level impact. This points to a structural shift. The constraint is no longer access. It is the ability to apply, integrate, and sustain. Differentiation is moving towards what is inherently harder to replicate. Not the insight itself, but how organizations interpret context, align across functions, and make decisions that hold under complexity. In practice, this shows up less in what organizations know, and more in how they operate: - Applying judgment in specific business and market contexts - Aligning strategy, technology, and operations coherently over time - Making trade-offs across growth, cost, risk, and long-term priorities - Translating intent into execution, consistently and at scale It also raises a broader question on how operating models need to evolve in an AI-enabled environment, particularly in globally distributed models? In that sense, the role of strategy is evolving from defining direction to shaping how organizations translate capability into outcomes, and make decisions where judgment, not just data, becomes the differentiator. You see this most clearly in sustainability decisions around low-carbon materials, packaging, supply chain resilience, and energy investments. The value case is increasingly understood. The complexity lies in when to act, how far to go, and how much risk to absorb - decisions that sit at the intersection of multiple objectives and time horizons, and do not lend themselves to straightforward optimization. 𝗢𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀 𝘁𝗵𝗮𝘁 𝗯𝘂𝗶𝗹𝗱 𝘁𝗵𝗲 𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝘁𝗼 𝗼𝗽𝗲𝗿𝗮𝘁𝗲 𝗮𝗰𝗿𝗼𝘀𝘀 𝘁𝗵𝗲𝘀𝗲 𝘁𝗿𝗮𝗱𝗲-𝗼𝗳𝗳𝘀, 𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝘁𝗹𝘆 𝗮𝗻𝗱 𝗮𝘁 𝘀𝗰𝗮𝗹𝗲, 𝘄𝗶𝗹𝗹 𝗱𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗻𝗲𝘅𝘁 𝗽𝗵𝗮𝘀𝗲 𝗼𝗳 𝗰𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲 𝗮𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲. #Strategy #AI #OperatingModel

Most organizations can tell you where AI is failing. They struggle to tell you why. McKinsey's AI Transformation Manifesto identifies the barriers clearly: slow adoption at the front lines, unclear decision rights, and workflows that haven't been redesigned around new ways of working. Leadership and talent, not technology, are the bottleneck. What the research doesn't fully name is where those barriers live. They live inside teams. Across our enterprise client work, the execution gap shows up in recognizable patterns: 1. Direction: Teams don't have enough clarity on priorities and decision rights to move at the speed AI requires. When everything is urgent, nothing accelerates. 2. Connection: Cross-functional friction slows workflows before AI ever gets a chance to improve them. Teams are operating in silos that AI can't bridge on its own. 3. Adaptability: Organizations expect teams to iterate quickly, but haven't built the conditions for teams to absorb change, adjust, and keep moving. Resistance isn't laziness, it's a structural problem. 4. Performance: Accountability systems weren't designed for the pace or complexity of AI-enabled work. Without clear ownership, execution stalls. 5. Attitude: Skepticism and fatigue quietly undermine adoption from the inside. Teams that don't believe in the direction won't sustain the behavior change required. None of these are technology problems. All of them are team performance problems. The companies closing the execution gap aren't just investing in better AI tools. They're investing in how their teams operate, and measuring it with the same rigor they apply to everything else. What does the execution gap look like inside your organization? #ResilientTeams #RallyBright #AITransformation #TeamPerformance #LeadershipDevelopment #TeamDynamics #FutureOfWork https://lnkd.in/evk4Eix9

𝗔𝗜‑𝗰𝘂𝗿𝗶𝗼𝘂𝘀 teams ask, “What can this tool do.” 𝗔𝗜‑𝗰𝗮𝗽𝗮𝗯𝗹𝗲 teams ask, “How will we learn from using it.” That shift is subtle, but it determines whether AI becomes a novelty or a force multiplier. Across the strongest organizations, AI adoption isn’t left to chance. It’s governed with the same discipline applied to finance, operations, and risk: 📋 Clear decision rights: which decisions are AI‑assisted and which remain human‑led. 🔄 Consistent review patterns: quality gates that prevent every team from reinventing its own approach. 📊 Outcome accountability: measuring quality and impact, not just speed. 📚 Institutional learning: lessons published and shared so progress compounds across functions. Many organizations avoid this work because it feels like overhead. In practice, it’s the foundation of scalable value. Without shared operating habits, AI remains fragmented and unpredictable. With shared habits, organizations reduce execution risk and accelerate capability building. This is why AI adoption is, first and foremost, an operations and governance mandate — not a tooling exercise. Tool access enables experimentation. Operating discipline delivers repeatable business value. If your organization is moving from curiosity to capability, identify the habit that’s missing — and the one change that would unlock the most value this quarter. #OperationalExcellence #ChangeManagement #Leadership #BusinessTransformation #BoardGovernance

AI can find the patterns. People make transformation stick. That was the heart of my recent conversation with Lisa Woodall on Whatever Next? Unplugged - 20 minutes on what it truly takes to make enterprise transformation work: structure, culture, and the two moving in parallel, not in sequence. If you are navigating AI adoption, operational improvement, or organizational change - this one is worth your time. 🎧 Link in comments

From Decision to Action: Closing the Last Mile Most organizations focus on improving insights and understanding their data, but far fewer truly focus on what happens once a decision has been made. Because even when the right decision has already been identified, execution often remains manual, delayed, or fragmented across multiple systems. Operators are required to validate information, escalate events, move between different systems, and trigger workflows, and along the way, valuable time is lost exactly where response speed has the greatest impact. This is the next stage of organizational transformation: From decision support, to a world where decisions are translated into execution. An advanced AI layer does not only surface insights, it also triggers actions, orchestrates responses across systems, and enables a fully closed loop in real time. Because ultimately, real value doesn’t come only from understanding, but from the ability to act immediately and with precision.

Most AI transformation strategies are missing the team layer. Across enterprise clients, we're seeing a consistent pattern: organizations are deploying AI on top of operating models that were never designed for this pace. The result isn't transformation. It's friction that moves faster. A recent Fast Company article captured this well. The constraint on AI impact isn't the technology. It's how work actually happens at the team level. We're observing three dynamics playing out simultaneously: → Decision velocity is accelerating — but teams lack the Direction clarity to make faster decisions without creating downstream misalignment → Roles and responsibilities are blurring — because AI is changing what people do, not just how fast they do it → Cross-team coordination is becoming the primary bottleneck — individual capability gains are being absorbed by team-level friction The organizations seeing real results from AI aren't just deploying tools. They're redesigning the team operating system — how decisions flow, how work moves across functions, how alignment holds as priorities shift. In the Resilient Teams 2.0 framework, this shows up most clearly in Adaptability, Direction, and Performance. High-performing teams in AI-intensive environments have built structural clarity across all three — not just process changes, but behavioral ones. AI transformation is, at its core, a team transformation. The question worth asking right now: Are your teams designed to operate at AI speed? https://lnkd.in/exEKqAdn What's the team-level constraint your organization is most focused on as AI accelerates? #ResilientTeams #RallyBright #TeamPerformance #LeadershipDevelopment #FutureOfWork #TeamDynamics #EnterpriseTeams