Integrating Change In Business Processes

If we want value from data, it’s not enough to teach people how to read charts. We also need to teach them how to read themselves. It’s a simple but counterintuitive fact: Understanding data doesn't automatically lead to better decisions. Think about it: How often have you seen someone presented with perfect data, clear insights, and compelling visualizations... only to make the same decisions they would have made without them? A lack of Data Literacy can be part of the problem. But that’s just one piece of the puzzle. An even more common—and oftentimes overlooked—issue is misunderstanding how humans actually make decisions when presented with data. A lack of Decision Literacy. The psychology of decision-making runs deeper than we think. Even with the most robust data governance, clear metrics, and advanced analytics capabilities, organizations will still fall prey to human tendencies, such as loss aversion, anchoring bias, and the sunk cost fallacy. These psychological factors don’t just interfere with our decisions once the data is there—they already shape how we collect, analyze, and interpret data in the first place. The more skilled we become with data, the more sophisticated our self-deception can become. Cognitive biases don’t disappear just because we know how to interpret bar charts and probabilities. On the contrary! The illusion of data mastery can make us even more vulnerable to confirmation bias. Our brains are excellent at finding data that supports our pre-existing beliefs while unconsciously filtering out contradictory evidence. True data mastery requires us to be as fluent in psychology as we are in programming. We must therefore expand our common definition of data-driven transformation to account for this: ▪️ Beyond teaching people how to analyze data, we must help them understand decision-making. ▪️ In addition to building databases, we must also build decision-making frameworks that account for human nature. ▪️ When asking, “What does the data tell us?” we must also ask, “What might prevent us from seeing what the data tells us?” The most successful organizations I've worked with understand that reading data and reading ourselves are equally critical skills for success. They create environments where it's safe to challenge assumptions, and decisions are reviewed not just for outcomes but for processes. So ask yourself this: When was the last time your team discussed cognitive biases, group dynamics, and decision frameworks with the same rigor as your data stack?

Sustainability of Change Beyond the Initial Wins. Change is often celebrated in its early stages, with teams rallying around fresh strategies, new technologies, or cultural shifts. However, the true test of transformation lies in its long-term sustainability. Organisations frequently pour their energy into the initial rollout but fail to address the crucial question: how do we ensure these changes last? This is where the conversation around embedding change into the very fabric of a company’s operations becomes vital. Without a deliberate plan to institutionalise new systems and behaviours, the risk of reverting to old habits becomes inevitable, undermining the progress made. To sustain change, leaders must focus on more than just quick wins—they need to build structures that support continuous evolution. This involves integrating the new practices into everyday workflows, aligning them with long-term goals, and ensuring that employees understand the broader purpose behind the changes. Too often, companies neglect to engage their workforce in a way that promotes ownership and accountability, treating transformation as a temporary project rather than a permanent shift in culture. It's essential to cultivate an environment where new behaviours are recognised and rewarded, making the desired change part of the organisation’s DNA. Sustaining transformation is about cultivating a mindset that sees change as a continuous process rather than a one-time event. Companies that succeed are those that embed flexibility and adaptability into their culture, ensuring that every layer of the organisation is committed to growth. This requires leadership to remain vigilant, consistently reinforcing new practices and fostering an environment of accountability and innovation. By making sustainability a core priority, organisations can turn initial wins into lasting success, building a resilient culture that thrives in the face of both challenges and opportunities. #SustainabilityOfChange #BusinessTransformation #Leadership

Most organisations collect data. Few actually use it. The difference? Where data sits in the organisation's centre of gravity. When data is a technical function, it reports on the past. When it becomes a strategic asset, it shapes the future. I've seen this shift happen and it's not about better tools or bigger data teams. It's about a fundamental change in how decisions get made: → Decisions become faster, more transparent, and more accountable → Data stops being owned by a function and starts being embedded in how the business operates → The question shifts from "What data do we have?" to "What decisions are we trying to improve?" That's the inflexion point. That's when data stops being operational support and starts being a genuine source of competitive advantage. Working across both industry and academia made this clear to me, the organisations that get it right aren't just data-mature. They're decision-mature. And here's the practical test: is your data leader in the room when strategy is being set? Not presenting a report after the fact, actually in the room. If not, you don't have a data strategy. You have a data function that processes information but doesn't shape decisions. That seat at the table isn't a nice-to-have. It's the whole point. #DataStrategy #DataLeadership #DecisionMaking #ChiefDataOfficer #DataDriven #DigitalTransformation #BusinessStrategy

Organizations today face relentless and unpredictable change—from shifting customer expectations to technological disruption. Executives talk about the need for balance, agility, and transformation. But what does it take, in practice, to sustain dynamic balance and thrive? Let's explore. DYNAMIC BALANCE: WHAT IS IT? Maintaining dynamic balance is an ongoing process shaped by two forces: - Path Dependency: Internal routines, traditions, and investments that anchor how a company operates. While this provides valuable stability, it can also entrench inertia—making it hard to respond when the world changes unexpectedly. - External Environment: Rapid shifts in technology, market demands, regulation, and competition. Organizations must continuously scan, interpret, and adapt to these forces or risk becoming irrelevant. Achieving balance means managing the tension between these forces: holding on to what works, but being ready to reinvent when necessary. Dynamic balance is a continuous process of adjustment—requiring organizations to both respect their history and be ready to break with it when the environment demands. DYNAMIC CAPABILITIES: WHAT ARE THEY? To understand how organizations effectively navigate change, we look to the concept of dynamic capabilities—a term coined by scholars such as David Teece, Gary Pisano, and Amy Shuen. At its core, dynamic capabilities refer to an organization’s ability to build, integrate, and reconfigure internal and external resources to address and shape rapidly changing environments. Key Features of Dynamic Capabilities >> Attraction: The ability to bring together resources—people, technology, partnerships—to create pathways to new value. >> Recombination: The capacity to configure existing and new assets to meet new challenges. >> Learning: Organizations must generating new knowledge and learn quickly through experimentation. >> Alignment: Aligning resources both internally and externally to seize emerging opportunities. The Three Focus Areas of Dynamic Capabilities: Sensing, Seizing, Transforming Dynamic capabilities are most often described in three interrelated capabilities: 1. Sensing:Scanning the environment on the edge of the organization to identify opportunities. 2. Seizing: Recombining existing and new assets to create new business models. 3. Transforming: Changing structures, culture, and processes to achieve scale. Some examples: >> Apple added consumer electronics and services to computers. >> IBM transformed its business model away from legacy hardware.  >> Netflix shifted from DVDs to streaming, then to original content. So, what does this mean for leaders? Transformation goes beyond top-down strategies: it requires breaking out of established paths and developing new skills and routines. Effective leaders frame new opportunities. They support experimentation, learning, and innovation. Strategic Doing offers a disciplined approach to developing these dynamic capabilities. 

The only constant is change, but change isn't constant; it's accelerating. Here's how change leaders can adapt. Today's change leaders face an unprecedented challenge: how do you lead transformation when change itself is accelerating so rapidly? The answer lies in embedding adaptability. Here's how: ➡️ The psychologically safe foundation By starting with psychological safety, you create an environment where teams can experiment, voice concerns, and even fail without worry. Model vulnerability and openness, demonstrating that uncertainty isn't weakness but opportunity. When colleagues know they won't be penalised for trying new approaches or raising difficult questions, they become active partners in change rather than passive resisters. ➡️ Collaborative experimentation over top-down direction Rather than dictating solutions, adaptive leaders embrace collaborative, iterative experimentation. This means co-creating change and using feedback to continuously refine approaches. Cross-functional teams become your innovation engine, breaking down silos and generating diverse perspectives that traditional hierarchies often miss. ➡️ Strategic adaptability Today's change leaders demonstrate strategic adaptability, the ability to maintain purpose while being flexible about methods. This requires systems thinking to understand interconnections, emotional intelligence to navigate human dynamics, and a mindset that prioritises learning. ➡️ Continuous learning This isn't training, it's creating personalised learning paths, encouraging growth mindsets, and establishing feedback loops for continuous improvement. When teams see learning as ongoing capability development rather than one-time events, they become naturally more adaptable. ➡️ From resistance to engagement Reframe resistance as valuable data about change readiness and design flaws. Address specific concerns through transparent communication, involve sceptics in solution design, and celebrate early wins to build momentum. Sustainable change happens through people and their ability to adapt. What are your tips for adaptability? ---- I equip leaders for turning AI anxiety into readiness and provide AI Change Leadership learning solutions. Follow me for more on human-led AI adoption.

Learnings from a Transformation - Episode 3 You can’t do Digital Transformation without doing Process Transformation. ……..And you can’t do process transformation without changing people’s behaviours and mindsets, enhancing their skills and capabilities, changing the organisational structure and changing the whole culture of a business.  To truly embed a transformation you have to take a fully holistic approach. But let’s start with Process.  Never have I used this word more than in the last 2 years. As a results-orientated General Manager I spent much of my time trying to circumnavigate many processes which felt like organisational glue trying to slow me down. So this was a new space I found myself in bestolling the virtues of standardisation and getting associates to change their behaviours to follow the new processes. Now, there’s no doubt that the digitalisation part of change is the exciting bit that we can all see and touch. And to be fair we have implemented some pretty funky new systems as part of D30 - From an AI enabled stock readjustment system that re-balances all our stocks across our network ensuring we have the right stock when we and wherever we need it most, through to a global digital asset management database for all our marketeers around the world to create personalised content at scale. However, both of these started with detailed business process mapping and design work. For our D30 transformation, We broke down the business in to 6 cross-functional end-to-end processes. We identified the big shifts we wanted to make in these areas and where we thought the most significant savings could come from. We then went deep into designing the detail of all of these processes, mapping exactly where the work should get done, who makes what decision, what interdependencies existed between them and how we could make them easier for our associates. We drove huge efficiencies, reducing cycle times dramatically, simplifying many overly complex ways of working, removing millions of hours of work and standardising that work across regions. We are now also monitoring compliance against these new ways of working to ensure we maintain these efficiencies. Increasing associates understating of how work gets done within these end-to-end processes has enabled an enterprise mindset, with associates now understanding how their work impacts not just their part of the process but what happens upstream and downstream from them as well. This has got departments talking to other departments, regions talking to other regions and driven an unprecedented spirit of collaboration and co-creation Truly addressing how and where works gets done through detailed Process Transformation has been the foundation of and key unlocker for the transformation journey we are on. #transformation #change

Augmented Reality: The Catalyst for Sustainable Innovation & Long-Term Business Growth For Visionary Leaders Who Dare to Redefine the Future In today's dynamic business landscape, AR isn’t just a trendy add-on it’s a strategic imperative for companies committed to both profitability and planet-friendly practices. With over a decade of experience in AR/VR and sustainable tech, I’ve seen firsthand how this technology transforms operations and fuels competitive advantage. The Data-Driven Case for AR: • Market Momentum: The global AR market is set to hit $88.4B by 2026—propelling a 32% increase in operational efficiency across industries (Statista, PwC). • Training Revolution: AR-powered training slashes learning curves by up to 70%; for instance, Boeing’s AR-guided assembly reduces errors by 90%, translating to multi-million-dollar savings annually (Deloitte). • Sustainability Impact: Remote assistance via AR cuts travel emissions by 40%, while maintenance tools help reduce energy waste by 25%—crucial steps toward ambitious net-zero targets (MIT, Schneider Electric). Business Benefits Beyond the Bottom Line: • Operational Efficiency: Imagine technicians repairing wind turbines using AR overlays—eliminating 80% of onsite visits. Siemens is already saving around $1.3M per year by leveraging similar solutions. • Customer Experience Transformation: AR apps empower customers to “try before they buy.” IKEA’s tool, for example, has reduced product returns by 22% through enhanced visualization. • Future-Proof Talent: Gen Z and emerging talent demand immersive, tech-driven learning environments. Companies incorporating AR into their training see up to 35% higher retention (Harvard Business Review). Scaling Sustainability for Humanity: • Circular Economy Acceleration: Porsche’s AR-guided refurbishment process diverts approximately 12K tons of materials from landfills annually. • Democratizing Expertise: AR is leveling the playing field—enabling expert guidance in remote regions, including life-saving telehealth initiatives. • Carbon-Neutral Collaboration: Tools like Microsoft’s HoloLens are reshaping global teamwork, reducing supply chain emissions by nearly 30%. Overcoming Roadblocks: Legacy systems and initial employee adoption can seem daunting. However, pilot projects have shown that even a modest AR integration can yield over $200K in annual savings (Capgemini). Integrating AR with IoT/ERP platforms is already smoothing the transition for many forward-thinking organizations. Let’s Build the Future Together: I’m curating an exclusive mastermind for executives ready to harness AR for both profit and purpose. If you’re looking to: - Slash operational costs by 20%+ within 18 months - Achieve ESG targets without sacrificing growth - Lead your industry’s green tech revolution Comment “AR Visionary” below or DM me to explore synergies. Video Anon Artist

When I joined one of the world’s largest automotive companies, I found an entire department running its core process on… Excel. Every day, analysts spent hours tweaking spreadsheets, trying to make dozens of constraints “just barely” fit. When something didn’t work, the only solution was to delete a row, fudge a number, or beg another team for an exception. We replaced the whole process with a simple LP model. Nothing fancy; just decision variables, constraints, and a clear objective. Here’s what made the real difference: • IIS (Irreducible Infeasible Set): Instead of hunting blindly for which of 50+ rules were causing a conflict, we could pinpoint the exact minimal set that made the system infeasible. • feasRelax: Instead of emailing around asking “which rule can we break?”, we let the model tell us the least painful set of constraints to relax, and by how much. The result? 📈 Production-ready plans in minutes instead of hours 📉 WIP and firefighting went down dramatically 🤝 Trust went up, because decisions were now data-driven and explainable Sometimes “transformation” doesn’t mean AI, neural nets, or a year-long ERP rollout. Sometimes it just means putting math where a spreadsheet used to be.

Managing and Evaluating in Complex Systems: What to Consider In my work with organisations in public services across sectors, it has become more common to acknowledge that we’re navigating complex, dynamic environments—and that has major implications for how we design, deliver, and evaluate change. Features of complex systems that are important in this context are: * Multiple parts of the system are interacting leading to change * Change happens in a non-linear way, changing one thing might have a tiny or massive effect * Over time systems emerge differently in different contexts * Feedback matters - the system responds to information and knowledge as well as action What does this mean for working in complex systems and understanding change? It’s less about finding definitive answers and more about supporting learning, reflection, and adaptation. *Context matters. Always start by understanding the system you’re working in. * Be clear on assumptions. Knowing what you think will happen (and why) helps test ideas and guide action. * Use different types of evidence to reflect, learn and understand the system. Lived experience, frontline insights and qualitative feedback are just as valuable as quantitative data. *Stay flexible. Outcomes won’t always match expectations, and that’s okay. Help people make sense of change. Using feedback well can support decision-making and adaptation. When done well, learning and reflection in complex systems becomes a powerful tool—not just for understanding what’s happening, but for helping interventions evolve in real time.

⚠️ Bad Business Process + AI = Bad Results Many organizations want to unlock value from AI—whether through automation or the emerging wave of Agentic AI. But here’s the catch: AI delivers transformative results only when paired with business process transformation, not when it’s simply “bolted on” to existing ways of working. Here’s a practical approach to make AI work by design, not by accident: 1️⃣ Identify the right starting points Focus on processes that are: - Effort-intensive - Costly - Painful for customers 2️⃣ Build a cross-functional design team Bring together: - Business SMEs (curious, open to new possibilities) - AI designers & engineers (tech-savvy but grounded in business context) - Finance SMEs (to shape a credible business case) - Expert facilitators (skilled in design thinking & co-creation) 3️⃣ Reimagine with “AI by Design” - Don’t just replicate the current state—sketch a future-state process where AI is embedded from the ground up. 4️⃣ Execute wisely - Adopt a mindset of: Think Big, Start Small, Run Fast, Scale Well. 5️⃣ Measure what matters - Baseline your current costs, effort, and outcomes Define success metrics upfront (efficiency, experience, revenue impact, etc.) Report and iterate relentlessly 💡 Useful resources to dive deeper: MIT Sloan: Designing AI-Powered Processes Harvard Business Review: Reimagining Work with AI Gartner: AI by Design Framework IDEO: Design Thinking Resources 👉 The future isn’t about applying AI to the past. It’s about reimagining business processes for the future—with AI at the core.