Consulting

In a MAJOR ruling for European copyright law, the Munich Regional Court has sided with Germany’s music rights society GEMA against OpenAI, finding that the company’s ChatGPT model unlawfully used copyrighted song lyrics in its training and responses. The decision, issued this morning, marks the first major European court judgment holding an AI company liable for using protected works without a licence. I got into AI through being Director of Legal Affairs and Regulatory Compliance in IMRO, the Irish counterpart of GEMA - and I know the people in GEMA - so this is very interesting to me. The case centred on GEMA’s allegation that OpenAI trained ChatGPT on its repertoire of German song lyrics, allowing the chatbot to reproduce works by artists such as Helene Fischer and Herbert Grönemeyer. The court agreed, concluding that the model’s ability to reproduce lyrics word for word demonstrated that the works had been used in training. It ruled that OpenAI is liable for copyright infringement and prohibited ChatGPT from reproducing lyrics from GEMA-represented artists unless a licence is obtained. The court also held that the European Union’s Text and Data Mining exceptions cannot shield generative AI systems that “memorise” and reproduce copyrighted material. This reasoning undermines one of the primary legal defences AI developers have relied upon in Europe. While damages will be determined in a separate proceeding, the court’s finding of liability alone sets a powerful precedent. OpenAI has announced plans to appeal. The 42nd Civil Chamber of the Munich Regional Court had indicated its position in September, when it observed that the model’s outputs could not be explained without training on copyrighted material. The final judgment confirmed that assessment. For the wider AI sector, the ruling suggests that AI companies operating in the European Union may need explicit licences for any copyrighted content used in model training or risk litigation. The decision also has regulatory implications. It aligns with growing momentum within the EU to enforce transparency and rights-holder protections under the AI Act and the Copyright in the Digital Single Market Directive. The GEMA v OpenAI ruling diverges sharply from Bartz v Anthropic in the United States. In Bartz, Judge Alsup found that AI training on copyrighted material could qualify as fair use, meaning no licence is required when the use is deemed transformative and non-substitutive. He viewed training as an analytical process that teaches the model general patterns rather than reproducing expression. The Munich court took the opposite view, holding that using protected works in AI training without permission constitutes reproduction requiring a licence. This illustrates the growing divide between the U.S. model, where fair use can exempt AI developers from licensing duties, and the European approach, which treats copyright as an enforceable economic right demanding prior authorisation.

One skill separates great communicators from average ones: Perspective-taking. The ability to see things from someone else’s point of view. But most people do it wrong. Here’s how to do it right, especially when you’re leading or being led: When you’re the boss, persuading down: You’re trying to convince Maria on your team to do something different. She’s pushing back. Your instinct might be to assert your authority. But that’s a mistake. Here’s why… Research shows: The more powerful you feel, the worse your perspective-taking becomes. More power = less understanding. So if you want to persuade Maria, don’t lean into your title. Do the opposite: dial your power down, just briefly. Try this: Before the next conversation, remind yourself: Maria has power too. I need her buy-in. Maybe she sees something I don’t. Lower your feelings of power to raise your perspective. From that place, ask: → What does she see that I’m missing? → What might be in her way? → What’s a win-win outcome? That shift changes the entire dynamic. Instead of steamrolling, you’re collaborating. And that’s how you earn trust and results. Now flip it. You’re the employee persuading your boss. It’s a high-stakes moment. You’re nervous. So do you appeal to emotion? No. Drop the feelings. Focus on interests. Here’s the key question: “What’s in it for them?” Not how you feel. Not your big dream. → Will it save time? → Improve performance? → Help them hit their goals? Make it about their world, not yours. Why? Because every boss has a mental shortcut: → Does this employee make my life easier or harder? Be the person who brings clarity, ideas, and upside. Not complaints, drama, or friction. In summary: → Persuading down? Dial down your power to see clearer. → Persuading up? Focus on their interests, not your emotions. Perspective-taking is a superpower, if you learn how to use it. Now practice, practice, practice.

70% of change initiatives fail. (And it's rarely because the idea was bad.) Here's what actually kills transformation: You picked the wrong change model for the job. It's like performing surgery with a hammer. Sure, you're using a tool. But it's the wrong one. I've watched brilliant CEOs tank their companies this way: Using individual coaching (ADKAR) for company-wide transformation. Result: 200 people change. 2,000 don't. Running a massive 8-step program for a simple process fix. Result: 6 months wasted. Team exhausted. Nothing changes. Forcing top-down mandates when they needed subtle nudges. Result: Rebellion. Resentment. Resignation letters. Here's what nobody tells you about change: The size of your change determines your approach. Real examples from the field: 💡 Startup pivoting product: → Used Lewin's 3-stage (unfreeze old way, change, refreeze) → 3 months. Clean transition. Team aligned. 💡 Enterprise going digital: → Used Kotter's 8-step process → Created urgency first. Built coalition. Enabled action. → 18 months later: $50M in new revenue. 💡 Sales team adopting new CRM: → Used Nudge Theory → Made old system harder to access → Put new system as browser homepage → 95% adoption in 2 weeks. Zero complaints. The expensive truth: Wrong model = wasted months + burned budgets + broken trust Right model = faster adoption + sustained results + energized teams Warning signs you're using the wrong model: • High activity, low progress • People comply but don't commit • Changes revert within weeks • Energy drops as you push harder • "This too shall pass" becomes the motto Match your medicine to your ailment: Small behavior change? Nudge it. Individual performance? ADKAR it. Cultural shift? Influence it. Full transformation? Kotter it. Enterprise overhaul? BCG it. Stop treating every change like a nail. Start choosing the right tool for the job. Your next change initiative depends on it. Your team's trust demands it. Your company's future requires it. Save this. Share it with your leadership team. Because the next time someone says "people resist change," you'll know the truth: People don't resist change. They resist the wrong approach to change. P.S. Want a PDF of my Change Management cheat sheet? Get it free: https://lnkd.in/dv7biXUs ♻️ Repost to help a leader in your network. Follow Eric Partaker for more operational insights. — 📢 Want to lead like a world-class CEO? Join my FREE TRAINING: "The 8 Qualities That Separate World-Class CEOs From Everyone Else" Thu Jul 3rd, 12 noon Eastern / 5pm UK time https://lnkd.in/dy-6w_rx 📌 The CEO Accelerator starts July 23rd. 20+ Founders & CEOs have already enrolled. Learn more and apply: https://lnkd.in/dwndXMAk

🌍 We Can’t Afford to Get Climate Policy Wrong—A Look at the Data Behind What Really Works 🌍 In the race against time to combat climate change, bold promises are everywhere. But here’s the critical question: Are the policies being implemented actually reducing emissions at the scale we need? A groundbreaking study published in Science, cuts through the noise and delivers the insights we desperately need. Evaluating 1,500 climate policies from around the world, the research identifies the 63 most effective ones—policies that have delivered tangible, significant reductions in emissions. What’s striking is that the most successful strategies often involve combinations of policies, rather than single initiatives. Think of it as the ultimate teamwork: when policies like carbon pricing, renewable energy mandates, and efficiency standards are combined thoughtfully, the impact is far greater than any one policy could achieve on its own. It’s a powerful reminder that for climate solutions the whole is indeed greater than the sum of its parts. Moreover, the study’s use of counterfactual emissions pathways is a game changer. By showing what would have happened without these policies, it provides a clear, quantifiable measure of their effectiveness. This is exactly the kind of rigorous evaluation we need to ensure that every policy counts, especially when we’re working against the clock. If we’re serious about meeting the Paris Agreement’s targets, we need to focus on what works—and this research offers a clear roadmap. Let’s champion policies that have proven to make a difference, because we don’t have time to waste on anything less. 🔗 Full study in the comments #ClimateAction #Sustainability #PolicyEffectiveness #ParisAgreement #NetZero #ClimateScience

To effectively help their clients, strategy and implementation consultants need to leverage four drivers at the same time: Content, Process, Mindset and Behavior. Master these skills and you will be amongst the best in the world. The classical strategy consultant focuses primarily on the content-aspect of consulting. They do extensive analysis and based on that analysis, they give advice. While this model has been a great source of revenues, it is not enough for real change and effective strategy implementation. To truly achieve organizational change, a strategy and implementation consultant needs to address key drivers. We can organize them along two dimensions: explicit vs. tacit and cognition vs. action. The explicit part of consulting is what we see. It concerns the mechanics of strategy and the steps it takes to develop and implement it. The tacit part is what is under the surface; what happens in people’s mind and what is needed to change their day-to-day behavior. The cognitive part of consulting concerns the mental aspect; what happens in our minds and how we think. The action part concerns what we do; the processes and behaviors required. Based on these two dimensions, these are the four drivers of strategy and implementation consulting: CONTENT The strategy itself, as well as the roadmap and action plans that follow from it. This driver focuses on what the organization should look like in the future (point B), where it stands now (point A) and how to bridge the gap between A and B. PROCESS The steps, actions and tools used to develop and implement strategy. To define points A and B and the actions to bridge the gap between them, you take certain steps and actions and use certain tools to execute them. MINDSET What happens in people’s minds; their values and beliefs; at the top and across the organization. Without the right mindset or shift therein, strategy and implementation will remain unsuccessful. BEHAVIOR In the end, it is people’s behaviors, habits and routines that need to change. Not addressing these will not bring the success you want. Therefore, also behavioral change requires dedicated attention. Unfortunately, there are not many places where you can develop all four skills. It is for this very reason that Timothy Tiryaki and I have developed the Certified Strategy & Implementation Consultant (CSIC) program. It is carefully designed around the four drivers so that you develop all the skills required to be an effective consultant. Our next cohort starts on February 7th and there are still a few spots left. If you have at least 10 years of experience, 5 of which in a facilitating, coaching or managing role, and aspire to enhance your strategy and implementation skills, this program may be for you. Visit our website strategy.inc for all information and registration. Are you ready to develop the skills to master all four drivers? #strategicleadership #changemanagement #growthmindset

𝗗𝗮𝘁𝗮 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗶𝘀 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗺𝗶𝘀𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗼𝗼𝗱 𝘁𝗼𝗽𝗶𝗰𝘀 𝗶𝗻 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲. Because most people explain it from the inside out: policies, councils, standards, stewardship. But the business does not buy any of that. The business buys outcomes: → trustworthy KPIs → vendor and partner data you can actually use → faster financial close → fewer reporting escalations → smoother M&A integration → AI you can deploy without creating risk debt Most AI programs fail for boring reasons: nobody owns the data, quality is unknown, access is messy, accountability is missing. 𝗦𝗼 𝗹𝗲𝘁’𝘀 𝘀𝗶𝗺𝗽𝗹𝗶𝗳𝘆 𝗶𝘁. 𝗗𝗮𝘁𝗮 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗶𝘀 𝗳𝗼𝘂𝗿 𝘁𝗵𝗶𝗻𝗴𝘀: → ownership → quality → access → accountability 𝗔𝗻𝗱 𝗶𝘁 𝗯𝗲𝗰𝗼𝗺𝗲𝘀 𝘃𝗲𝗿𝘆 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝘄𝗵𝗲𝗻 𝘆𝗼𝘂 𝘁𝗵𝗶𝗻𝗸 𝗶𝗻 𝟰 𝗹𝗮𝘆𝗲𝗿𝘀: 1. Data Products (what the business consumes) → a named dataset with an owner and SLA → clear definitions + metric logic → documented inputs/outputs and intended use → discoverable in a catalog → versioned so changes don’t break reporting 2. Data Management (how products stay reliable) → quality rules + monitoring (freshness, completeness, accuracy) → lineage (where it came from, where it’s used) → master/reference data alignment → metadata management (business + technical) → access controls and retention rules 3. Data Governance (who decides, who is accountable) → data ownership model (domain owners, stewards) → decision rights: who can change KPI definitions, thresholds, and sources → issue management: triage, escalation paths, resolution SLAs → policy enforcement: what’s mandatory vs optional → risk and compliance alignment (auditability, approvals) 4. Data Operating Model (how you scale across the enterprise) → domain-based setup (data mesh or not, but clear domains) → operating cadence: weekly issue review, monthly KPI governance, quarterly standards → stewardship at scale (roles, capacity, incentives) → cross-domain decision-making for shared metrics → enablement: templates, playbooks, tooling support If you want to start fast: Pick the 10 metrics that run the business. Assign an owner. Define decision rights + escalation. Then build the data products around them. ↓ 𝗜𝗳 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁 𝘁𝗼 𝘀𝘁𝗮𝘆 𝗮𝗵𝗲𝗮𝗱 𝗮𝘀 𝗔𝗜 𝗿𝗲𝘀𝗵𝗮𝗽𝗲𝘀 𝘄𝗼𝗿𝗸 𝗮𝗻𝗱 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀, 𝘆𝗼𝘂 𝘄𝗶𝗹𝗹 𝗴𝗲𝘁 𝗮 𝗹𝗼𝘁 𝗼𝗳 𝘃𝗮𝗹𝘂𝗲 𝗳𝗿𝗼𝗺 𝗺𝘆 𝗳𝗿𝗲𝗲 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿: https://lnkd.in/dbf74Y9E

Hot take: the #legalengineer is now the most critical role in the in-house legal department. Not the GC. Not the deputy. Not the head of legal ops. The person who sits at the intersection of legal process expertise, technology fluency, and change management and who can re-engineer how legal work gets done as AI reshapes what's possible is what separates the teams that will come out of this period ahead from the ones that will have a lot of expensive technology and not much to show for it. In-house legal is redesigning itself right now. What goes to outside counsel? What does AI handle? How do we staff? You can't answer those questions or execute on the answers without someone who can architect the new model. I've been in this space for over two decades. This is the role I'd prioritize above almost anything else right now. https://lnkd.in/gCy6tQr5

Stuck in an endless loop of client changes? Lost track of what revision this constitutes? Yeah. Been there. Done that. The secret? It's not about saying no. It's about saying yes to the right things upfront. Every project that goes sideways starts the same way: Vague agreements. Fuzzy boundaries. Good intentions. Six weeks later you're bleeding money and everyone's frustrated. Here's my framework after 30 years of running two 8-figure businesses: The SOW is your salvation. Not some boilerplate template. A real document that covers: • Exact deliverables (not "design work" but "3 homepage concepts, 2 rounds of revisions") • Hours of operation ("We respond M-F, 9-5 PST. Weekend requests get Monday responses") • Revision rounds spelled out ("Round 1 includes up to 5 changes. Round 2 includes 3.") • Feedback cycles defined ("48-hour turnaround for client feedback or the project may be delayed or additional fees may be incurred") But here's what most people miss— Don't work on client notes immediately. Client sends 37 pieces of feedback at 11pm Friday? Producer sends conflicting notes from the CEO? Marketing wants one thing, sales wants another? Stop. Collect everything first. Resolve the conflicts. Get on the phone and discuss it with your client to get alignment. Separate the "have to haves" from the "nice to haves". Then present unified changes. "Based on all feedback received, here are the 8 changes we'll implement. This constitutes revision round 2 of 3." Watch how fast the random requests stop. No extra work that goes unappreciated. No more feelings of being taken advantage of. Communicate before the crisis, prevents the crisis from happening. "Just so you know, we're entering round 2. You have one more included. After that, it's $X per additional round." No surprises. No awkward money conversations. No resentment. Scope creep isn't a them problem. It's a you problem. And that's good news, because that means you are in control. They're not trying to take advantage. They just don't know where the boundaries are because you never drew them. Draw the lines early. Communicate them clearly. Everyone wins. What's your most painful scope creep story? What boundary would've prevented it? Small Business Builders #projectmanagement #clientmanagement #businessgrowth

This is the most underrated way to use Claude: (and it has nothing to do with writing or coding) It's competitive intelligence. Using data that's free, public, and updated every single week. Here's my extract step by step guide: Step 1. Go to claude .ai. Step 2. Select the new Claude "Opus 4.6." Step 3. Turn on "Extended Thinking." Step 4. Pick a competitor. Go to their careers page. Step 5. Copy every open job listing into one doc. (Title. Team name. Location. Full description) Step 6. Save it as one .txt or .docx file. Step 7. Search the company at EDGAR (sec .gov) Step 8. Download its recent 10-K or 10-Q filing. (Official strategy, risks, and financials - all public.) Step 9. Upload both files to Claude Opus 4.6. Step 10. Paste this exact prompt: "You are a competitive intelligence analyst at a rival company. I've uploaded [Company]'s complete current job listings and their most recent SEC filing. Perform a strategic intelligence analysis: → Cluster these roles by what they suggest is being built. Don't use the team names they've listed. Infer the actual product initiatives from the skills, tools, and responsibilities described. → Identify capabilities or teams that appear entirely new — not mentioned anywhere in the SEC filing. These are unreleased bets. → Find roles where seniority is disproportionately high for a new team. This signals executive-level priority. → Cross-reference the SEC filing's Risk Factors and Strategy sections with hiring patterns. Where are they investing against a stated risk? Where did they flag a risk but have zero hiring to address it? → Predict 3 product launches or strategic moves this company will make in the next 6-12 months. State your confidence level and cite specific job titles and filing sections as evidence. Format this as a 1-page competitive intelligence briefing for a CMO." What you'll find: → Products that don't exist yet but will in 6 months. → Priorities that contradict what the CEO said. → Risks they told the SEC but aren't addressing. This is what consulting firms charge $200K for. It took me 10 minutes. I used the new Claude 'Opus 4.6' for a reason: ✦ It read 60 job listing & a 200-page filing together.  ✦ And connects dots across both. ✦ It is superior in thinking and context retrieval. That's why I didn't use ChatGPT for this.