How to Avoid AI Initiative Hype

"We need an AI strategy!" 𝘙𝘦𝘤𝘰𝘳𝘥 𝘴𝘤𝘳𝘢𝘵𝘤𝘩 Hold up. That's the wrong question. The right question? "What business problem are we actually trying to solve?" I've sat in countless board meetings where executives demand AI initiatives – not because they've identified a problem AI can solve, but because they're afraid of being left behind. This FOMO-driven approach is precisely how companies end up in what I call "perpetual POC purgatory" – running endless proofs of concept that never see production. Here's the uncomfortable truth: Your goal isn't to use AI for the sake of AI. Your goal is to solve real business problems. Sometimes the best solution is a regular hammer, not a sledgehammer. So when leadership pushes AI without purpose, redirect the conversation: → "What business outcome are we trying to drive?” → “What’s the actual problem we’re solving?” → “Is AI the most effective tool for that — or just the most exciting one?” Next, how do you determine if AI is the right solution? I recommend this straightforward approach that keeps business problems at the center: 1. 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗽𝗿𝗲𝗰𝗶𝘀𝗲𝗹𝘆 - What specifically are you trying to solve? The more precisely you can articulate the problem, the easier it becomes to evaluate whether AI is appropriate. 2. 𝗖𝗼𝗻𝘀𝗶𝗱𝗲𝗿 𝘁𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 𝗳𝗶𝗿𝘀𝘁 - Could existing technology or processes handle this faster, cheaper, and more reliably? 3. 𝗟𝗲𝗮𝗻 𝗼𝗻 𝗲𝘅𝗽𝗲𝗿𝘁𝘀 - If the problem seems AI-suitable, validate it with people who’ve delivered outcomes — not just hype. 4. Be brutally realistic about your organization's maturity - Do you have the data infrastructure, talent, and risk tolerance necessary for an AI implementation? Remember this fundamental truth: AI is not a silver bullet. Even seemingly simple AI projects require time, focus, alignment, and resilience to implement successfully. The companies winning with AI aren't the ones with the flashiest technology. They're the ones methodically solving pressing business challenges with the most appropriate tools—AI or otherwise. 𝗜’𝗱 𝗹𝗼𝘃𝗲 𝘁𝗼 𝗵𝗲𝗮𝗿 𝗳𝗿𝗼𝗺 𝘆𝗼𝘂: What business problem are you trying to solve that might (or might not) actually need AI?

AI doesn’t need hype. It needs hygiene. Up top, the dream is glossy: GenAI, agentic AI, digital twins, robotic workers. But below the surface? Data silos. Technical debt. Legacy systems. Manual processes. App sprawl. Weak governance. No wonder AI pilots stall. Right now, AI feels like a race, with everyone sprinting toward automation glory. But most “AI problems” aren’t really AI problems. They’re data, integration and process problems. If your data is messy, your systems don’t talk, and your processes are outdated, then no algorithm will save you. It’s like dropping a turbo engine into a car that’s never had an oil change. You’ll go fast, but only for a few seconds. Before automation, fix the basics: → Understand your processes → Build a robust data architecture → Establish clear governance → Create smooth integrations Then start small. Pick one domain. Prove value. Learn fast. In parallel, tackle technical debt, strengthen governance and modernise integrations. And make sure your cybersecurity is as advanced as your AI ambitions. Keep the hype in check. Not every “agentic” demo is enterprise-ready. What’s the first foundation you’d fix to make AI actually deliver value in your organisation? ♻️ Repost to help someone take a look at their AI foundations. 🔔 Follow Clare Kitching for insights on unlocking value with data & AI. H/T to Gordon T. for the inspiration and image.

Right now, I’m watching a dangerous trend unfold. One that’s being marketed really well — and leading people down the wrong path. Someone builds a chatbot. They start a prompt course. And suddenly, they’re calling themselves an AI strategist. But building with AI isn’t the same as building strategy for AI. 💬 Because your organisation doesn’t need another shiny tool. It needs a system. A roadmap. A governance structure. A values-aligned, risk-aware, people-centered approach to transformation. That’s not something you get from a prompt template. That’s not something you figure out by cloning your voice. And it’s definitely not something you want to delegate to someone who doesn’t understand data structures, AI governance, or the actual impact this technology is having — on equity, operations, culture, and safety. ⸻ ⚠️ I’ve had to go into organisations and unwind messes: • No documentation • No clear ownership • No ethical considerations • No alignment with the broader team or business strategy And it’s almost always because someone jumped in too fast — or handed the wheel to someone who knew how to build a tool, but not how to lead change. ⸻ So how do you know who to trust? Here’s what to look for in real AI strategy: ✅ A systems lens (not just tools) ✅ Governance knowledge (not just prompt tips) ✅ Ethical fluency (especially re: bias, privacy, safety) ✅ Cross-functional thinking (not silos) ✅ Measurable ROI and risk mitigation (not hype) Because this isn’t about being first to post your bot. It’s about building something that lasts. Something your team can use. Something that reflects your mission — not just your ambition. ⸻ You deserve more than duct-taped automation. You deserve aligned systems. Clear strategy. Ethical leadership. 🎯 Don’t confuse a chatbot with a vision. And don’t confuse prompt fluency with organisational foresight. Your future deserves better. #EthicalAI #AIHerWay #AIStrategy #AIConsulting #EquiAI #AIForGood #FeministAI #AutomationWithIntention #GovernanceMatters #ValuesLedTech #WomenInAI #ResponsibleAI #AITransformation #DigitalLeadership #HumanFirstAI #ChatbotIsNotStrategy

How to Be Pragmatic in the Age of Agentic AI Agentic AI is taking center stage in our industry. Every day I see new headlines, vendor pitches, and analyst hot takes proclaiming this as the dawn of a new era. The hype is understandable—agentic AI is powerful and disruptive. Yet amidst this sea of optimism, I believe the job of technology leaders is to remain focused on what actually works. After decades in enterprise tech, here’s what I’ve learned: Hype cycles come and go. What matters is your ability to separate genuine value from marketing enthusiasm. Here’s how to be pragmatic in the age of agentic AI: - Start with Your Pain Points: Agentic AI should solve real business problems, not just be a trophy implementation. Don’t start with the technology—start with the issue that needs fixing. - Pilot, Measure, Adjust: Instead of launching into full-scale adoption, run small pilots aimed at high-impact areas. Measure outcomes against defined KPIs. Double down on successes, and don’t be afraid to pull the plug on projects that stall. - Beware the Cheerleaders: There are a lot of consultants and providers promising the moon right now. Ask for evidence, case studies, and honest post-mortems—not just success stories with cherry-picked metrics. - Invest in Skills + Change Management: Agentic AI is as much a people issue as it is a technology issue. Make sure your teams are ready, your processes are mature, and you have the right guardrails in place. - Stay Curious, Stay Skeptical: Be open-minded about the potential, but scrutinize every claim. Pragmatism means questioning the status quo, even when everyone else seems on board. - Bottom line: The best way forward is to focus on outcomes, not hype. In the age of agentic AI, the winners will be those who keep their heads, learn from real-world deployments, and never stop asking: “Is this delivering measurable value?” Let’s work together to ensure agentic AI delivers on its promise—by keeping our eyes on what works and what doesn’t. That means not attacking me just because I'm looking at the actual capabilities of the technology. 🙏 #AgenticAI #Pragmatism #EnterpriseIT #DigitalTransformation

AI is already everywhere in most orgs - just not often in a way that creates consistent, measurable value. The patterns are familiar: scattered experiments, tool bloat, unclear ROI and teams already at capacity. Leaders I talk to daily feel pressure to “do something with AI,” yet worry about time-to-value, vendor/model confidence and change fatigue. What’s getting in the way? 💣 Fragmented usage and no shared operating model 💣 Burnout risk from “one more tool” without workflow integration 💣 Budget scrutiny and skepticism from managers and frontline teams 💣 Sluggish decision cycles because governance is undefined Don't boil the ocean. Pick a spot and drive some momentum and credibility. Keep it tight with a sense of urgency and measurable impact. Here's a practical framework to make traction in the next 90 days: 💡 Align on outcomes, not tools. Pick 3–5 metrics that matter (speed to execution, collaboration drag reduction, campaign throughput, knowledge findability). 💡 Run impact pilots where work already happens. Prioritize low-friction use cases in campaign ops, asset creation, and knowledge management—then standardize what works. 💡 Publish role-specific playbooks. Make it concrete for PMM, demand gen, ops, and BDRs: inputs, prompts, guardrails, and handoffs baked into existing workflows. 💡 Stand up lightweight governance. Define what “good” looks like (accuracy, privacy, auditability), who approves changes, and how exceptions get resolved quickly. 💡 Equip champions and communicate. Give internal advocates a “first 30 days” kit, FAQs, and a simple scorecard to show quick wins and build momentum. 💡 Instrument adoption and ROI. Track efficiency gains, cycle-time reductions, and usage by workflow—not just licenses provisioned. Leaders who treat AI as an execution system—not a side project—move from experimentation to repeatable value, faster. If your team is feeling the strain of tool sprawl and unclear impact, start with clarity, pilots, playbooks, and scorecards—then scale what proves out.

🔁 We’ve seen this movie before... 🧠 AI Won’t Fix That! I remember when test automation took off in the mid-2000s. Everyone was excited. Manual testers started worrying about their jobs. Tool vendors promised 80% automation coverage. Leadership teams jumped in headfirst, buying licenses and starting pilots. But here’s what really happened: A handful of companies got it right. The rest? Ended up with expensive tools, half-baked scripts, and very little to show for it. Why? Because they skipped the basics. They didn’t ask, “What are we trying to solve?” They just assumed automation was the answer. Back then, nobody really knew what they were automating. Today we are in 2025? We’re buying AI before even writing the problem statement. And it’s happening all over again. Just swap “automation” with “AI.” Everyone wants in. Teams are experimenting with AI tools, launching initiatives, and setting big expectations. But I rarely hear anyone step back and ask: What's the actual pain point? Is AI even the right solution for it? How will we measure impact? What does success look like 6 months from now? AI isn’t a silver bullet. It’s a tool. And like any tool, it works only if you use it for the right job. So before chasing the hype, pause and reflect. Make decisions that fit your context, not someone else’s trend. Don’t let history repeat itself. Start with the problem. Then find the solution : AI or otherwise. 👀 Seen this play out in your org or with a client? Tag a teammate or a testing leader who’s been there. #TestAutomation #SoftwareTesting #QualityAssurance #TestMetry

The new Gartner Hype Cycle for AI is out, and it’s no surprise what’s landed in the trough of disillusionment… Generative AI. What felt like yesterday’s darling is now facing a reality check. Sky-high expectations around GenAI’s transformational capabilities, which for many companies, the actual business value has been underwhelming. Here’s why.… Without solid technical, data, and organizational foundations, guided by a focused enterprise-wide strategy, GenAI remains little more than an expensive content creation tool. This year’s Gartner report makes one thing clear... scaling AI isn’t about chasing the next AI model or breakthrough. It’s about building the right foundation first. ☑️ AI Governance and Risk Management: Covers Responsible AI and TRiSM, ensuring systems are ethical, transparent, secure, and compliant. It’s about building trust in AI, managing risks, and protecting sensitive data across the lifecycle. ☑️ AI-Ready Data: Structured, high-quality, context-rich data that AI systems can understand and use. This goes beyond “clean data”, we’re talking ontologies, knowledge graphs, etc. that enable understanding. “Most organizations lack the data, analytics and software foundations to move individual AI projects to production at scale.” – Gartner These aren’t nice-to-haves. They’re mandatory. Only then should organizations explore the technologies shaping the next wave: 🔷 AI Agents: Autonomous systems beyond simple chatbots. True autonomy remains a major hurdle for most organizations. 🔷 Multimodal AI: Systems that process text, image, audio, and video simultaneously, unlocking richer, contextual understanding. 🔷 TRiSM: Frameworks ensuring AI systems are secure, compliant, and trustworthy. Critical for enterprise adoption. These technologies are advancing rapidly, but they’re surrounded by hype (sound familiar?). The key is approaching them like an innovator...  start with specific, targeted use cases and a clear hypothesis, adjusting as you go. That’s how you turn speculative promise into practical value. So where should companies focus their energy today? Not on chasing trends, but on building the capacity to drive purposeful innovation at scale: 1️⃣ Enterprise-wide AI strategy: Align teams, tech, and priorities under a unified vision 2️⃣ Targeted strategic use cases: Focus on 2–3 high-impact processes where data is central and cross-functional collaboration is essential. 3️⃣ Supportive ecosystems: Build not just the tech stack, but the enablement layer, training, tooling, and community, to scale use cases horizontally. 4️⃣ Continuous innovation: Stay curious. Experiment with emerging trends and identify paths of least resistance to adoption. AI adoption wasn’t simple before ChatGPT, and its launch didn’t change that. The fundamentals still matter. The hype cycle just reminds us where to look. Gartner Report:  https://lnkd.in/g7vKc9Vr #AI #Gartner #HypeCycle #Innovation

Don’t dive into AI without knowing what you’re trying to achieve. Skipping strategy first is the fastest way to drain resources and stall momentum. This is the framework I bring to leadership teams to turn AI ambition into measurable growth: 1️⃣ Identify key processes Spot the moments where AI can remove friction or create new value. Think supply chain delays, customer onboarding, content velocity—not “AI everywhere” for the sake of it. 2️⃣ Start small Launch a pilot that matters but won’t break the business. One well-designed agent that saves 30% of a team’s time beats 10 half-baked experiments. 3️⃣ Collaborate AI isn’t a solo sport. Pull in data teams, operators, designers, and frontline staff. Innovation sticks when everyone owns it. 4️⃣ Measure, learn, iterate Track what’s real: cost per insight, time-to-decision, customer response. Cut what fails. Double down on what compounds. 5️⃣ Keep learning Models evolve daily. So must your playbook and leadership mindset. AI isn’t just tech. It’s the new substrate of how the world works. But only if you approach it with intentional design and relentless learning. How are you making sure your AI investments are driven by purpose, not hype? Follow Emma Shad for more.

CEOs don’t realize that when they ask for AI agents, what they’re really asking for is: ✅ Data To Information Transformation ✅ Infrastructure Upgrades For Model Training & Inference Serving ✅ Comprehensive Information & AI Governance Guardrails ✅ Organizational Transformation ✅ Internal Training & Upskilling ✅ Process, Workflow, & Product Reinvention I have worked with large enterprise clients for 13 years, and I haven’t had one start AI-ready yet, but they all think they are. When internal voices try to tell C-level leaders the truth, it doesn’t go well, and here’s why. Data platform vendors have been selling AI-ready for years, but in reality, they’ve been getting customers BI-ready. BI-first platforms have made cosmetic changes and rebranded themselves as AI-ready. Consultants have been transforming businesses to be BI-first with an AI-first price tag. It’s the same playbook, except every mention of data and analytics is replaced with AI. It’s all sales and marketing without the functional substance to support a completely new paradigm. Enterprises truly believe they are AI-ready and can’t understand why their technical teams can’t make AI magic happen the same way they see it work in the demos or customer use cases presented at conferences. Dear CEOs, When you see a ‘simple drag and drop’ agentic interface, ask about the foundational pieces required to make it look easy. When internal technical voices raise concerns or discuss the need for foundational components, trust them. When you see dancing robots on stage or video demos of them folding laundry, be skeptical. Read the fine print and have technical voices in the room asking tough questions about what’s really going on. Opportunities hide in the hype. AI, agents, robots, and all our new tech waves don’t work perfectly and don’t deploy easily. But there’s still massive value to be had, both short-term and long-term. Be aware of what they don’t do so you can refocus on what they are capable of today. Value doesn’t have to take years to materialize. Foundational steps can deliver immediate value while setting up for the future. Be a pragmatic futurist. Identify the disruption to reveal the opportunities and customers that help define high-value products. Foundational work amplifies the value of long-term initiatives & those bigger bets amplify the returns of foundational investments. Think in terms of flywheels, not get-rich-quick schemes, and you’ll be far more successful with AI.

AI makes it easier to turn ideas into real products, and that’s exciting! The flip side is that it's just as easy to spin up convincing look‑alikes. I wish I didn't have to post this, but it turns out someone is impersonating me to promote a fake "AI Protocol" project. This includes using my name and photo, along with a surprisingly polished GitBook site to make it look legit. Unfortunately, it seems this kind of impersonation is becoming more common. With today's tools, it takes just minutes to create a site that looks credible, especially when paired with a recognizable name or photo. With this disclaimer, I wanted to share a few general tips that I try to follow myself if I get approached by suspicious projects: 1) Check for a personal announcement. If people are involved in a project that is supposed to be public, you'd expect they also shared information about it on their own profile and website (LinkedIn and sebastianraschka.com in my case) 2) Look at domain names carefully. Scammers often use lookalike domains or subdomains to appear official. (Coincidentally, I also just read about the Google phishing issue involving their sites.google.com subdomain, so this alone is not enough.) 3) Watch for urgency or secrecy. For example, "limited slots," or requests to keep the offer quiet are red flags that you are being rushed past due diligence. 4) Search for outside footprints. I.e., real projects leave traces. This includes GitHub commits, conference talks, press releases, etc. If you cannot find any independent mention, be skeptical. 5) Verify. If you are asked for payments or investments, don't hesitate to reach out via trusted channels (their verified social accounts or contact email listed on their websites; in my case, LinkedIn messages or my email addresses listed at https://lnkd.in/gb9Q7xbn)