Strategic Alignment Across Teams

In luxury hospitality, everyone obsesses over design, architecture and capex. But the difference between a nightly rate of $1,200 and $2,500 is the people running the place. Let’s start with one of the most overlooked parts of that group: the investors. The wrong capital partners drag a property down because they optimize for the wrong things, cut corners that poison the guest experience, and replace strong leadership with people who can’t carry the weight. The right partners protect standards, invest in talent, and back decisions that strengthen rate instead of weakening it. When you choose your capital partners well, every part of the operation has the conditions it needs to deliver. Now let’s talk about the people on the ground. You can build the most beautiful property in the world, but it won’t matter if the team can’t make guests feel understood without theatrics or noise. People pay more when they sense the property’s paying attention, and that attention shows up in small decisions that add up to pricing power. I’ve watched a front-desk manager remember a child’s name from a stay two years earlier, and you could see the rate right there in the parent’s face. One of the most talented hoteliers I know builds everything around a simple premise: guest delight. There’s nothing sentimental about this ethic. Guest delight produces repeat visits, referral behavior, and rate integrity. It’s the foundation of demand elasticity at the top of the market, and it’s the reason two properties with similar costs and similar amenities can end up worlds apart in both revenue performance and valuation. I’ve seen a GM step out from a back corridor at 6 a.m. just to hand a departing guest a warm pastry for their early flight because he remembered a comment they’d made in passing. That’s why people come back, and that’s why they’ll pay for it. The properties that reach the top of the market are the ones where the investors, the leadership, and the people who greet guests at the door are aligned. A guest might remember the pastry, the kindness you showed to their child, or the way you made them feel. An investor remembers the ADR. Alignment is what makes all of those things the same event. That alignment determines whether a property stays at $1,200 or reaches $2,500. If you care about where this sector’s going, read Unspoken Hospitality https://lnkd.in/gRc4FKKA

Data or Gut Feelings. Whenever I’ve made strategic decisions while neglecting my gut feelings, I have felt a tinge of regret. Leaders are often urged to make data-driven decisions in this age of abundant data. Data is significant; it offers valuable insights by revealing past trends and providing predictive analytics, yet I believe it has limitations. Data alone will not always account for individual circumstances, unexpected challenges, or the essential human elements crucial to effective leadership. On the other hand, intuition - rooted in experience, judgment, and the ability to recognise patterns - can be incredibly powerful, especially in uncertain or quickly changing environments. Still, we must acknowledge that biases and narrow perspectives can sway intuition. Today’s leaders face the interesting challenge of blending analytical skills with intuitive wisdom rather than choosing one over the other. For example, while data may highlight an emerging market trend, intuition empowers leaders to assess whether the timing, cultural relevance, or team readiness aligns with taking action. A potent way to bridge this gap is by asking lots of critical questions during decision-making: Cultivating a habit of evaluating choices from numerical and descriptive angles ensures a more robust approach. The essence of future leadership lies in mastering the art of merging analytics with intuition. We can achieve this by fostering critical thinking to evaluate data accuracy, employing scenario planning, evaluating multiple alternatives to juxtapose gut feelings with measurable insights, and building diverse team thinking to challenge assumptions. Practical steps, such as conducting post-mortems to reflect on decision-making processes, help bring this balance to life. When data and intuition unite, leaders can make much more impactful decisions. So, I vote for a harmonious combination.

Many operational problems do not originate in manufacturing, quality, or supply chain. They arise when functions fail to see themselves as part of the same system. Over time, teams excel at optimizing their own agendas: - Quality ensures oversight and protection - Procurement negotiates best prices - Manufacturing prioritises efficiency and throughput - Supply chain manages risk and focuses on customer interests - etc While these efforts are not wrong, when they are not integrated, the customer—and ultimately the patient—pays the price long before we do. Patrick Lencioni often reminds leaders that clarity and alignment triumph over complexity. Integrated operations are not about structure or organizational charts; they involve shared responsibility for creating positive impacts and outcomes that matter. When operations are integrated: - Attitudes and decisions change - Trade-offs become visible - Problems surface early - Teams stop defending gaps and start protecting outcomes This is why our extended roadmap under the 45° approach is significant. It compels us to deliver today while building the capabilities that enhance performance tomorrow. Integration is not merely a structural exercise; it is fundamentally a matter of leadership.

If you don't have a Champion, then you can't qualify the Decision Process. If you can't qualify the Decision Process then you can't qualify the deal. And if you can't qualify the deal, then you can't justify investing your time and resources into it (let alone putting it anywhere near a forecast 😬). This is just one of the reasons why we say #NAMIE (Not All MEDDIC Is Equal). So many sellers consider the Decision Process in isolation from other letters. 🚨 𝗧𝗵𝗶𝘀 𝗶𝘀 𝗮 𝗺𝗶𝘀𝘁𝗮𝗸𝗲 🚨 The Decision Process 𝗜𝗦𝗡'𝗧 the process of 𝗧𝗛𝗘 decision to buy your solution or not. It's the customers' process of making 𝗠𝗨𝗟𝗧𝗜𝗣𝗟𝗘 decisions throughout an engagement: • Whether to evaluate a solution in the first place? • Which vendors to involve? • Which stakeholders to involve? • What should be in the Decision Criteria? (𝘛𝘦𝘤𝘩𝘯𝘪𝘤𝘢𝘭, 𝘌𝘤𝘰𝘯𝘰𝘮𝘪𝘤𝘢𝘭, 𝘙𝘦𝘭𝘢𝘵𝘪𝘰𝘯𝘴𝘩𝘪𝘱) • How will the vendors be evaluated?  (𝘋𝘦𝘮𝘰? 𝘙𝘍𝘐/𝘗? 𝘗𝘖𝘊? 𝘗𝘖𝘝? 𝘙𝘦𝘧𝘦𝘳𝘦𝘯𝘤𝘦𝘴?) • What are the steps for technical approval? • What are the steps for business approval? • How will the business case be constructed?  (𝘝𝘦𝘯𝘥𝘰𝘳? 3𝘳𝘥 𝘱𝘢𝘳𝘵𝘺? 𝘐𝘯𝘵𝘦𝘳𝘯𝘢𝘭 𝘰𝘯𝘭𝘺?) • What are the formal steps?  (𝘚𝘦𝘤𝘶𝘳𝘪𝘵𝘺, 𝘗𝘳𝘰𝘤𝘶𝘳𝘦𝘮𝘦𝘯𝘵, 𝘓𝘦𝘨𝘢𝘭, 𝘦𝘵𝘤.) • What are the steps from Business and Technical approval to signature? And so so many more... And every company and engagement will be different... 𝗛𝗲𝗻𝗰𝗲 𝘄𝗵𝘆: The Decision Process needs a 𝗖𝗵𝗮𝗺𝗽𝗶𝗼𝗻 to advise, confirm, and support its progress. The Decision Process must connect to the value elements of 𝗜𝗺𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀, 𝗠𝗲𝘁𝗿𝗶𝗰𝘀, and 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗖𝗿𝗶𝘁𝗲𝗿𝗶𝗮 to ensure consensus on the unique value your solution can bring to EVERY stakeholder and ensure urgency remains. The Decision Process must connect to the 𝗖𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝗼𝗻. Not just the rival solutions but competing initiatives, so your deal doesn't get de-prioritized. The Decision Process must include the 𝗘𝗰𝗼𝗻𝗼𝗺𝗶𝗰 𝗕𝘂𝘆𝗲𝗿's sponsorship. The Decision Process must align with the 𝗰𝗼𝗺𝗽𝗲𝗹𝗹𝗶𝗻𝗴 𝗲𝘃𝗲𝗻𝘁, ideally the customer's or, at worst, an event you have created based upon customer value (𝘯𝘰𝘵 𝘢 𝘥𝘪𝘴𝘤𝘰𝘶𝘯𝘵 𝘧𝘰𝘳 𝘢 𝘥𝘪𝘴𝘤𝘰𝘶𝘯𝘵'𝘴 𝘴𝘢𝘬𝘦). And, of course, the Decision Process and 𝗣𝗮𝗽𝗲𝗿 𝗣𝗿𝗼𝗰𝗲𝘀𝘀 are like two best friends that should go everywhere together in step 🤝. The best bit of all? Your Champion is highly unlikely to be a professional buyer. They won't know the best approach to buying a solution like yours. This creates an opportunity for professional sellers to position themselves as trusted advisors by helping curate and map out the Decision Process with their customers. Did I miss anything? 🤔 What are your tips for building a solid and collaborative Decision Process? #MEDDIC #MEDDICC #MEDDPICC #DecisionProcess #Sales  

What’s the best way to document what AI decided—and why? Most people think AI documentation is about logging data — capturing outputs, timestamps, and probabilities. But I think that’s missing the real point. When humans make decisions, we can explain our intent. When AI makes a decision, it can’t. Because AI doesn’t have reasons — it has optimizations. And that, to me, changes everything. We don’t just need to record what the AI did. We need to explain what we taught it to optimize for. Right now, most documentation serves auditors and regulators. But the real value lies elsewhere — in understanding the alignment gap between human intent and machine logic. That gap is where trust, accountability, and learning live. Here’s how I think we can fix it: ✅ Document the goal — what was the AI trying to achieve? ✅ Note the key factors — what influenced the outcome most? ✅ Capture human intent — what assumptions shaped the model? ✅ Acknowledge uncertainty — what could still be wrong? This way, documentation becomes more than proof — it becomes memory. A shared record of how humans and machines reason together. So, what do you think — should AI documentation serve compliance, or should it serve understanding and accountability first? #AITransparency #AITrust #ResponsibleAI #AIEthics #HumanCenteredAI #AIGovernance

Not all cyber threats are equal…. It is crucial for the Board & CXOs to ensure that investments in security are aligned with the organization's risk profile. This requires regular risk assessments & aligning the cyber security strategy with the organization's business goals. Simply put, far too many boards & CEOs see cybersecurity as a set of technical initiatives & edicts that are the domain of CIO, CISO, & other technical practitioners. In doing so, they overlook the perils of corporate complexity & the power of simplicity when it comes to cyber risk. In fact leaders who are serious about cybersecurity, need to translate simplicity & complexity reduction into business priorities that enter into the strategic dialogue of the board, the CEO, & the rest of the C-suite. Questions such as the following can help catalyze this conversation: • How does a full accounting of cyber risk affect our business model’s attractiveness, & does that suggest the need for a “simplification agenda”? • How transparent are the cyber risks and trade-offs associated with our external digital partnerships, & what would be the pros & cons of simplifying our ecosystem to make them more manageable? • How risky are our IT-enabled legacy processes, and how should we prioritize investments to secure, simplify, & transform them to achieve competitive advantage? Leadership teams which grapple with questions like these and embrace simplicity boost their odds of making the entire enterprise securable. Breakneck digitization in the smartphone era has exacerbated matters, as companies have increasingly created ecosystems with a variety of new partners to help expand their reach and capture new, profitable growth. They range from supply chain relationships across goods & services to partnerships for data, distribution, marketing, & innovation. Even more recently, the business challenges of COVID-19 pandemic have spurred faster adoption of digital solutions that rely on data, digital networks and devices that are often operated by companies outside the organization’s borders. Leaders seeking to strike a better balance can start with some basic principles. One is ensuring that strategic moves won’t increase complexity risk & make the current situation worse. Another is understanding that simplification of company, may require more than minor rewiring of systems, & instead may demand more fundamental & often longer-term modification to IT structures, to make them fit for growth. The challenges & opportunities fall into 3 areas. 1. Business models 2. External Partners 3. Internal Systems Reducing complexity while establishing a framework for governance & shared responsibility demands deliberate action, over the long & the short term. It also demands attention & energy of the CEOs & the boards who understand its value and are ready to invest in changing mindsets. Leaders who are ready to step up and set the tone will create a better blueprint for a securable enterprise.

“PRDs are dead,” they said. I disagree. They’re more important than ever. Let me explain. I was coaching a product team at a major enterprise last week. They were following the "Prototype fast. Ship fast. Be AI-first" mantra and I know you see this on your timeline as well. Above than that, they had board pressure. They wanted to look like the Anthropic of their industry. So out of desperation, they stopped writing them and went straight to shipping. Here's what happened (and reason they onboarded us): •⁠ ⁠Three teams claimed ownership of the same AI initiative •⁠ ⁠Design built prototypes that didn't match the vision •⁠ ⁠Engineering shipped features that solved the wrong problem •⁠ ⁠No source of truth because everyone had opinions The same time they spent managing chaos was the same time their competitors spent shipping solutions that actually solved user problems. Here’s the thing nobody tells you about AI-era product development: AI makes PRDs more important, not less. Not the old 50-page waterfall PRDs. Those are dead, and good riddance. I’m talking about living alignment artifacts — documents that evolve alongside your prototypes. Here’s the framework — The Living Artifact Method: 1. Start with the prototype — yes, build first. Get your hands dirty. AI makes this fast. 2. Then write the document — use it as a checklist. “Am I solving the right problem? For the right user?” 3. Iterate both together — the prototype and the document should tell the same story. 4. Make it everyone’s input, one owner’s decision — product, design, and engineering all contribute. But the product leader owns the final prioritization. 5. Let the artifact lead — when teams disagree, point to the document. That’s your tiebreaker. The teams shipping real AI products aren’t the ones who abandoned structure. They’re the ones who evolved it. AI doesn’t kill structure — it demands better structure. We teach product teams how to evolve their processes for the AI era → Product Faculty

I'm biased but hear me out - in-house creative teams are an unlock for any company.  The power and advantage of in-house creative teams lies in their adaptability, responsiveness, and understanding strategic nuance.  This allows them to drive conceptualization and development - since they aren’t playing catchup or having to read a doc to understand the vision.  They know it and live it, giving them more time to do what they are good at - being creative!   They also understand the voice of your internal stakeholders making content that naturally fits with who they are and meeting their needs - saving rounds of back and forth. I’m looking for three things in building a powerhouse internal team:  🎥Creative range — Can they stretch across formats, tones, and timelines without losing quality? 📊Strategic fluency — Can they connect the creative dots back to business outcomes? 🔀Adaptability under pressure — When the campaign changes (and it always changes), do they flex or freeze? The best in-house creatives aren’t just executors. They’re partners who happen to sit on your side of the table. #NABShow Irad Eyal

Six months ago, a client almost pulled the plug on an AI implementation we were running. Three weeks in. Leadership was aligned. The use case was clear. The tools were live. And yet adoption had started to stall. Usage dropped. Teams quietly slipped back into old workflows. Moments like this define whether an AI project succeeds or dies. At ALTRD, our instinct isn’t to defend the system we built. Our instinct is to investigate the system we missed. So we paused the rollout and audited what was actually happening inside the workflow. What we found was instructive. The training had landed well. But the implementation had been designed around how leadership thought the team worked. Not how they actually worked. Two things were quietly breaking adoption. First, we had optimized the visible workflow but missed an invisible step. There was a key handoff happening informally between two people over WhatsApp. It wasn’t documented anywhere. It never showed up in process charts. But it was where the real decision-making happened. Our redesigned workflow skipped that moment completely. Second, there was a quiet skeptic in the system. The team lead everyone naturally looked to before trying something new had concerns she hadn’t voiced in any meeting. Not because she was resistant, but because she wasn’t convinced the workflow would hold up under real pressure. Once the team sensed that hesitation, adoption slowed down. So we fixed the system. We remapped the actual workflow, not the documented one. Then we worked directly with the team lead. Not to sell the tool, but to understand the operational concerns and redesign parts of the system around them. The engagement expanded. And that project ended up becoming one of the most valuable learning moments for how we implement AI today. Two lessons we now carry into every engagement at ALTRD: Document the informal workflow, not just the official one. And find the quiet skeptic in the room early. They’re rarely the blocker. They’re usually the signal that something important hasn’t been designed properly yet. AI implementation isn’t just a technical system. It’s a human system. And if you want adoption to stick, you have to understand both.

"five building blocks — conceptual and technical infrastructure — needed to operationalize responsible AI ... 1. People: Empower your experts Responsible AI goals are best served by multidisciplinary teams that contain varied domain, technical, and social expertise. Rather than seeking "unicorn" hires with all dimensions of expertise, organizations should build interdisciplinary teams, ensure inclusive hiring practices, and strategically decide where RAI work is housed — i.e., whether it is centralized, distributed, or a hybrid. Embedding RAI into the organizational fabric and ensuring practitioners are sufficiently supported and influential is critical to developing stable team structures and fostering strong engagement among internal and external stakeholders. 2. Priorities: Thoughtfully triage work For responsible AI practices to be implemented effectively, teams need to clearly define the scope of this work, which can be anchored in both regulatory obligations and ethical commitments. Teams will need to prioritize across factors like risk severity, stakeholder concerns, internal capacity, and long-term impact. As technological and business pressures evolve, ensuring strategic alignment with leadership, organizational culture, and team incentives is crucial to sustaining investment in responsible practices over time. 3. Processes: Establish structures for governance Organizations need structured governance mechanisms that move beyond ad-hoc efforts to tackle emerging issues posed in the development or adoption of AI. These include standardized risk management approaches, clear internal decision-making guidance, and checks and balances to align incentives across disparate business functions. 4. Platforms: Invest in responsibility infrastructure To scale responsible practices, organizations will be well-served by investing in foundational technical and procedural infrastructure, including centralized documentation management systems, AI evaluation tools, off-the-shelf mitigation methods for common harms and failure modes, and post-deployment monitoring platforms. Shared taxonomies and consistent definitions can support cross-team alignment, while functional documentation systems make responsible AI work internally discoverable, accessible, and actionable. 5. Progress: Track efforts holistically Sustaining support for and improving responsible AI practices requires teams to diligently measure and communicate the impact of related efforts. Tailored metrics and indicators can be used to help justify resources and promote internal accountability. Organizational and topical maturity models can also guide incremental improvement and institutionalization of responsible practices; meaningful transparency initiatives can help foster stakeholder trust and democratic engagement in AI governance." Miranda Bogen, Kevin Bankston, Ruchika Joshi, Beba Cibralic, PhD, Center for Democracy & Technology, Leverhulme Centre for the Future of Intelligence