Most executive AI programs teach you about agents. We hand you the blueprints, and you build one yourself. EnterpriseClaw is a 6-week cohort program for senior leaders who are done watching from the sidelines. We follow the methodology of "Learn. Build. Strategize." You won't be figuring this out alone. The program comes loaded with proprietary blueprints, decision frameworks, prescriptive guides, curated skills, prompts, and knowledge packs. All built from real enterprise deployments. You'll deeply understand the architecture that makes agents work, the patterns that scale, and the pitfalls that kill adoption. Then you build it yourself, hands-on, with regular expert guidance when you get stuck. What you walk away with: > Real, working AI agents you defined and built, not a demo > 6 enterprise artifacts compiled into your org's Digital Workforce Management Playbook (security guide, governance framework, work audit, scaling blueprint…) > The credibility and gut instinct to lead your org's AI transformation Acknowledging how quickly the tools landscape changes and the enterprise reality for many: Two paths available: OpenClaw or a similar commercial path (Cursor, Claude Code, Codex, etc.) for orgs where security locked OpenClaw down. Same skills. Same principles. No lectures. No slides. No "watch this video and collect a badge." Led by Nufar Gaspar (15 years shipping AI inside enterprises, 14,000+ execs trained) and Nathaniel Whittemore (host of The AI Daily Brief, creator of ClawCamp with 5,600+ participants). Cohort 1 starts March 2026. Limited seats. Apply → https://enterpriseclaw.ai/
Learn AI Agent Development with EnterpriseClaw's 6-Week Cohort Program
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Catch Lauren from Multitudes at NZ Tech Rally 2026 in May in Wellington. Lauren is our keynote speaker this year and her talk is bound to be super interesting with all these research findings. Building AI without loosing our values: https://lnkd.in/gDTAa-vP
500+ engineers, 200+ leaders, and 15 months of data – all to get here: Our second AI impact whitepaper is now LIVE! 🎉 I don’t even want to tell you how many hours it takes a human to write a 30-page whitepaper. But not once was I tempted to have AI write this because – ironically? – using AI to write a research paper on AI’s impact feels especially wrong, if only to do justice to the 700+ people who shared their insights with us. I’ll have lots more to say about this paper later but for now, settle in with your favorite warm drink and get ready for a paper brimming with insights! To get you excited, our key takeaways were: 1️⃣ For more AI usage, build a learning organization. Learning organizations are ones that have systems that support continuous learning – something we especially need now given the pace of change of AI. 2️⃣ The desired AI outcomes are clear but measuring them is not. While organizations know what they want out of their AI tooling, many aren’t measuring outcomes – in part because it’s hard to know how to measure them. 3️⃣ Adapt processes, not just your tooling, to work with AI. Despite the importance of supporting people to get more from AI, the vast majority of the time, people focus on codebase changes rather than people or process ones. 4️⃣ Engineering roles are evolving. Engineers are expected to be more cross-functional, and with non-engineers building more software, there are additional demands on engineers to support these new builders. The most interesting part, of course, is how organizations are doing all the above, and so we’ve filled this paper with the rich examples that came up in our surveys and interviews. Also, a MASSIVE thanks to: Kelly Blincoe, Thomas Fritz, Brittany Johnson, MBA, Vivek Katial, and Nathen Harvey for the thoughtful feedback – on our draft survey and more recently on this whitepaper. You’ve made this research and whitepaper better, and any remaining issues are our own. And of course, HUGE thanks to Youxiang Lei for the analysis and writing, and to Laura Walker for the design wizardry. Whew, we did it!! Link to paper in comments – ungated, of course.
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Our second AI impact whitepaper is now live! Over the last 15 months, we've researched 500+ engineers, and 200+ leaders, all to understand the impact of AI on engineering teams. Part one of our research looked at the impact of AI on developer productivity, codebase quality, and wellbeing. Part two, which we’re sharing today, looks at how organizations are evolving their practices to keep up with the pace of change. Links in comments to whitepapers 1 and 2 – let us know what you think!
500+ engineers, 200+ leaders, and 15 months of data – all to get here: Our second AI impact whitepaper is now LIVE! 🎉 I don’t even want to tell you how many hours it takes a human to write a 30-page whitepaper. But not once was I tempted to have AI write this because – ironically? – using AI to write a research paper on AI’s impact feels especially wrong, if only to do justice to the 700+ people who shared their insights with us. I’ll have lots more to say about this paper later but for now, settle in with your favorite warm drink and get ready for a paper brimming with insights! To get you excited, our key takeaways were: 1️⃣ For more AI usage, build a learning organization. Learning organizations are ones that have systems that support continuous learning – something we especially need now given the pace of change of AI. 2️⃣ The desired AI outcomes are clear but measuring them is not. While organizations know what they want out of their AI tooling, many aren’t measuring outcomes – in part because it’s hard to know how to measure them. 3️⃣ Adapt processes, not just your tooling, to work with AI. Despite the importance of supporting people to get more from AI, the vast majority of the time, people focus on codebase changes rather than people or process ones. 4️⃣ Engineering roles are evolving. Engineers are expected to be more cross-functional, and with non-engineers building more software, there are additional demands on engineers to support these new builders. The most interesting part, of course, is how organizations are doing all the above, and so we’ve filled this paper with the rich examples that came up in our surveys and interviews. Also, a MASSIVE thanks to: Kelly Blincoe, Thomas Fritz, Brittany Johnson, MBA, Vivek Katial, and Nathen Harvey for the thoughtful feedback – on our draft survey and more recently on this whitepaper. You’ve made this research and whitepaper better, and any remaining issues are our own. And of course, HUGE thanks to Youxiang Lei for the analysis and writing, and to Laura Walker for the design wizardry. Whew, we did it!! Link to paper in comments – ungated, of course.
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The researcher in me is stoked to see this research coming out of New Zealand - well done Lauren Peate and the team at Multitudes. Agentic product development is in its emerging stage, and research like this helps us take a scientific approach to making sense of it. And if this is something that you find useful, stay tuned for the next episode of the Tech Waka Podcast, where I talk to Lauren about this research.
500+ engineers, 200+ leaders, and 15 months of data – all to get here: Our second AI impact whitepaper is now LIVE! 🎉 I don’t even want to tell you how many hours it takes a human to write a 30-page whitepaper. But not once was I tempted to have AI write this because – ironically? – using AI to write a research paper on AI’s impact feels especially wrong, if only to do justice to the 700+ people who shared their insights with us. I’ll have lots more to say about this paper later but for now, settle in with your favorite warm drink and get ready for a paper brimming with insights! To get you excited, our key takeaways were: 1️⃣ For more AI usage, build a learning organization. Learning organizations are ones that have systems that support continuous learning – something we especially need now given the pace of change of AI. 2️⃣ The desired AI outcomes are clear but measuring them is not. While organizations know what they want out of their AI tooling, many aren’t measuring outcomes – in part because it’s hard to know how to measure them. 3️⃣ Adapt processes, not just your tooling, to work with AI. Despite the importance of supporting people to get more from AI, the vast majority of the time, people focus on codebase changes rather than people or process ones. 4️⃣ Engineering roles are evolving. Engineers are expected to be more cross-functional, and with non-engineers building more software, there are additional demands on engineers to support these new builders. The most interesting part, of course, is how organizations are doing all the above, and so we’ve filled this paper with the rich examples that came up in our surveys and interviews. Also, a MASSIVE thanks to: Kelly Blincoe, Thomas Fritz, Brittany Johnson, MBA, Vivek Katial, and Nathen Harvey for the thoughtful feedback – on our draft survey and more recently on this whitepaper. You’ve made this research and whitepaper better, and any remaining issues are our own. And of course, HUGE thanks to Youxiang Lei for the analysis and writing, and to Laura Walker for the design wizardry. Whew, we did it!! Link to paper in comments – ungated, of course.
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Multitudes have published their follow-up agentic coding research paper, that Lauren Peate alluded to in the Waves Of Innovation podcast episode that has also gone live today! If you want to hear Lauren deep dive on her team's research (and you probably should), check out the links in the comments.
500+ engineers, 200+ leaders, and 15 months of data – all to get here: Our second AI impact whitepaper is now LIVE! 🎉 I don’t even want to tell you how many hours it takes a human to write a 30-page whitepaper. But not once was I tempted to have AI write this because – ironically? – using AI to write a research paper on AI’s impact feels especially wrong, if only to do justice to the 700+ people who shared their insights with us. I’ll have lots more to say about this paper later but for now, settle in with your favorite warm drink and get ready for a paper brimming with insights! To get you excited, our key takeaways were: 1️⃣ For more AI usage, build a learning organization. Learning organizations are ones that have systems that support continuous learning – something we especially need now given the pace of change of AI. 2️⃣ The desired AI outcomes are clear but measuring them is not. While organizations know what they want out of their AI tooling, many aren’t measuring outcomes – in part because it’s hard to know how to measure them. 3️⃣ Adapt processes, not just your tooling, to work with AI. Despite the importance of supporting people to get more from AI, the vast majority of the time, people focus on codebase changes rather than people or process ones. 4️⃣ Engineering roles are evolving. Engineers are expected to be more cross-functional, and with non-engineers building more software, there are additional demands on engineers to support these new builders. The most interesting part, of course, is how organizations are doing all the above, and so we’ve filled this paper with the rich examples that came up in our surveys and interviews. Also, a MASSIVE thanks to: Kelly Blincoe, Thomas Fritz, Brittany Johnson, MBA, Vivek Katial, and Nathen Harvey for the thoughtful feedback – on our draft survey and more recently on this whitepaper. You’ve made this research and whitepaper better, and any remaining issues are our own. And of course, HUGE thanks to Youxiang Lei for the analysis and writing, and to Laura Walker for the design wizardry. Whew, we did it!! Link to paper in comments – ungated, of course.
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What I read about Business AI Failures to leaders (CTO, CEO, CAIO) must know. (DATA TALKS) Enterprise AI initiatives rarely fail because of limitations in machine learning algorithms themselves. Instead, failures usually stem from deeper enterprise architecture and operational maturity gaps. One of the most common causes is data readiness issues. Many organizations attempt to deploy AI models without establishing robust data pipelines, data governance policies, and feature engineering frameworks. Inconsistent datasets, poor labeling standards, and fragmented data lakes significantly reduce model accuracy and reliability in production environments. Another major factor is the absence of strong AI governance and risk management frameworks. Without clearly defined model validation protocols, ethical AI guidelines, and regulatory compliance mechanisms, organizations expose themselves to issues such as biased model outputs, privacy violations, and uncontrolled decision automation. Effective AI programs require structured model monitoring, audit trails, and lifecycle management systems to ensure accountability and transparency. Finally, AI projects frequently struggle due to legacy system integration challenges and organizational capability gaps. Many enterprises operate on complex infrastructures where integrating AI models with ERP platforms, API ecosystems, cloud data warehouses, and real-time analytics systems becomes technically difficult. At the same time, organizations often lack sufficient AI engineering expertise, MLOps capabilities, and cross-functional collaboration, which are essential for maintaining scalable and production-grade AI solutions. Next post will explain the practical failures based on above.
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Most AI projects don’t fail during training - they fail before training even starts. Over the past few years, I’ve worked with teams building AI systems - from computer vision models to production pipelines. And I’ve noticed a pattern: The biggest problems rarely come from the model. They come from the problem definition. Many projects begin like this: 🗨️ “Let’s add AI” 💬 But very few start with the harder question: 🗨️“What decision should this system actually improve?”💬 Before the first dataset is collected, before the first model is trained, three critical things often go wrong: 1️⃣ The problem isn’t clearly defined Teams jump straight to model selection instead of defining the outcome. Is the system supposed to: ❓ reduce manual work? ❓ improve prediction accuracy? ❓ automate decisions? If the success metric isn’t clear, the model has nothing concrete to optimize for. 2️⃣ The data strategy is missing In practice, model architecture matters far less than people expect. What matters more: data quality, data labeling, consistency, bias control, feedback loops. In most AI projects, data preparation takes 70–80% of the effort. Yet it’s usually treated as an afterthought. 3️⃣ The system thinking is missing An AI model is not a product. It’s just one component in a larger system: data pipeline ➡️ model ➡️ evaluation ➡️ monitoring ➡️ retraining. Without this infrastructure, even a great model will quietly degrade over time. One of the biggest lessons from building AI systems in production is this: AI success isn’t about building smarter models. It’s about designing better systems around them. Before training anything, the real questions are: ❓ What decision will the model improve? ❓ How will success be measured? ❓ What data will continuously feed the system? If those answers are unclear, training a model won’t fix the problem. It will just make it more expensive. 🫰 💵 If you’ve worked on AI projects in production: ❓What do you think is the most underestimated risk in AI development today? ❓Data quality? Evaluation? Infrastructure? Or product definition? #ArtificialIntelligence #MachineLearning #AIEngineering #MLOps #DataStrategy #TechLeadership
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We noticed a recurring problem across AI teams. → 80% of project time lost to data preparation - not model building → 60% of ML engineers say poor data quality is their #1 challenge → Most enterprises juggling annotation across 3–5 disconnected tools → And yet, data infrastructure is still the last thing enterprises invest in The problem was never the model. It was everything that happens before the model. Fragmented workflows. No quality control at scale. Annotation treated as an afterthought - handed off, rushed, and inconsistent. Then teams wonder why models that passed every internal test fail in production. That's not a model problem. That's a data problem. So we built Flexibench. A single enterprise annotation platform for text, image, video, and audio - designed for teams that can't afford to cut corners on training data. Here's what that looks like in practice: → AI-assisted pre-labeling that cuts annotation time without sacrificing accuracy → Configurable workflows tailored to your task - not a rigid template → Multi-tier quality gates so bad labels don't make it into your training set → Dynamic taxonomy support so ontologies stay consistent across projects → Real-time collaboration so annotation teams, reviewers, and stakeholders stay aligned Because the gap between a good AI team and a great one isn't the algorithm. It's the quality, consistency, and governance of the data they train on. Enterprise AI deserves enterprise-grade data infrastructure. If your team is spending more time fixing data than building models - that's the problem we solve. #DataAnnotation #MLOps #EnterpriseAI #TrainingData #ArtificialIntelligence #DataScience #AIStrategy #DataAnnotation #DataLabeling #TrainingData #DataQuality
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I got this message last week, and it captures a pain I've watched build for almost a decade. "The only problem with taking {this AI solution} to executive leadership is that they will either take it and give it to an offshore/external team to build (who don't know how), and I'll have no visibility into it. Or they will completely bog it down with politics. Or they will not understand it and leave it on the shelf. I want this to be published and used. And I want my name on it." This is bigger than one person's frustration. It is the shared experience of thousands of professionals sitting on viable AI solutions right now. I have taught AI strategy to thousands of students for almost a decade. The term only went mainstream in the last 12 months, but the pain has been there all along. The barrier used to be technical. People couldn't build AI solutions and agents. Now the barrier is organizational. People can't articulate the strategic value to get buy-in or navigate the business politics to move it forward. They can identify the use case and implement a version 1 solution. They are missing the strategic language to protect the idea through the gauntlet of executive review, vendor politics, and offshore handoffs. So the idea dies, or worse, it gets built badly by someone three layers removed from the problem, sometimes a team that is entirely removed from the business and its customers. The professionals who break through have one thing in common: they do more than pitch an AI solution. They own the strategy and value creation. They speak in risk, revenue, and operational leverage. Those capabilities make it impossible to separate the idea from them, so they maintain ownership and reap the rewards. AI strategic literacy is a capability, not a slide deck or buzzword-filled presentation. It is the reason I built my AI Strategist and AI Product Management certification programs. I cover what AI can do, but more importantly, I teach how to make AI happen inside organizations that resist it. If these challenges sound familiar, the courses were built for exactly where you are. https://lnkd.in/gdx442in
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We don’t have an AI innovation problem, we have an adoption problem. Great ideas are everywhere, but most never survive the jump from concept to real operational use because they’re too complex to implement, scale, or own internally. Even when they do get approved, they rarely get used to their full extent. That creates the exact scenario Vin Vashishta is describing: ideas get handed off, over-engineered, or lost in translation, and the original value disappears. What’s missing isn’t just strategy, it’s practical, operational access to data and systems. That’s where platforms like NEQTO.ai shift the equation. Instead of requiring layers of engineering or external teams, it enables operators to have direct access to their operational data within their existing workflows. That’s what drives real buy-in and sustained use. #AI #Strategy #NEQTOai #OperationalIntelligence
Training The AI Talent That Enterprises Demand | CEO @ V Squared AI | Author, ‘From Data to Profit’
I got this message last week, and it captures a pain I've watched build for almost a decade. "The only problem with taking {this AI solution} to executive leadership is that they will either take it and give it to an offshore/external team to build (who don't know how), and I'll have no visibility into it. Or they will completely bog it down with politics. Or they will not understand it and leave it on the shelf. I want this to be published and used. And I want my name on it." This is bigger than one person's frustration. It is the shared experience of thousands of professionals sitting on viable AI solutions right now. I have taught AI strategy to thousands of students for almost a decade. The term only went mainstream in the last 12 months, but the pain has been there all along. The barrier used to be technical. People couldn't build AI solutions and agents. Now the barrier is organizational. People can't articulate the strategic value to get buy-in or navigate the business politics to move it forward. They can identify the use case and implement a version 1 solution. They are missing the strategic language to protect the idea through the gauntlet of executive review, vendor politics, and offshore handoffs. So the idea dies, or worse, it gets built badly by someone three layers removed from the problem, sometimes a team that is entirely removed from the business and its customers. The professionals who break through have one thing in common: they do more than pitch an AI solution. They own the strategy and value creation. They speak in risk, revenue, and operational leverage. Those capabilities make it impossible to separate the idea from them, so they maintain ownership and reap the rewards. AI strategic literacy is a capability, not a slide deck or buzzword-filled presentation. It is the reason I built my AI Strategist and AI Product Management certification programs. I cover what AI can do, but more importantly, I teach how to make AI happen inside organizations that resist it. If these challenges sound familiar, the courses were built for exactly where you are. https://lnkd.in/gdx442in
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Teammates, here’s the academic side to my post on PMI’s CPMAI course. One concept really stood out. Data. We’ve all heard the phrase garbage in, garbage out. The same applies to AI. CPMAI highlights that a large percentage of AI project failures stem from poor data. Missing fields, poor access, redundancy, bias, inconsistent labeling. Bad data will destroy a project before the model even starts. That made me pause and ask what the intel defense industry thinks about this challenge. Are they focused mainly on algorithms and models, or do they truly understand the value of good data? A quick search gave me the answer. CACI discusses how AI is valuable only when built within a trusted, responsible data framework: https://www.caci.com/ai MITRE highlights how data integrity directly impacts model performance and trust in AI systems: https://lnkd.in/eiK9f-rP ManTech emphasizes that strong data management and governance are foundational to AI success: https://lnkd.in/ewgTYxuR None of this was surprising. Industry often moves faster than the DoW in adopting new approaches. If we want AI to deliver mission value, we must invest in understanding data, managing it properly, and preparing our DoW workforce to embrace and use AI responsibly. Last but not least, we need to remember this…..Models don’t fail first. Data fails first. Make The WE Bigger.
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Koby Ohayon