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In Warren Buffett's final shareholder letter, he gave the kind of sage advice for life that you would expect from this truly great man.You can read it here:<a data-mce-href="https://www.berkshirehathaway.com/news/nov1025.pdf" href="https://www.berkshirehathaway.com/news/nov1025.pdf" target="_blank" rel="noopener">https://www.berkshirehathaway.com/news/nov1025.pdf</a><h3>It speaks to me because wellness is a major focus in my life.</h3>Like most people, my life is a mixture of incredible blessings and some difficult challenges. I have my health, two wonderful, healthy daughters, an engaging and successful career, great friends and rewarding hobbies.Personnally I have also known the pain of a broken marriage, betrayal, and divorce. Professionally, (like many other entrepreneurs) I have stared into the void with bankruptcy looming just hours away, taking stock of my options and pondering how I might navigate the worst.But for me, the good has far outweighed the bad, and those difficult seasons are when I've learned the most about myself and the world.Buffett inspired this post with his mentions of luck and the golden rule in his letter.<h3>Luck is an important component that we can often forget to be grateful for.</h3>Every good thing that's happened in my life is probably a combination of good luck plus my actions and reactions in those moments. Every bad thing that's happened is likely a combination of bad luck coupled with my actions and reactions in those moments.In the good times, I've learned to double down on what is work working and maintain momentum.I've also learned that during the bad times, it's important to work hard to maintain physical health in the face of emotional strain and to learn from what's happening.<h3>As Buffett notes, kindness and empathy along the way are so very important.</h3>I've also learned one very important twist on the golden rule. This twist acts as a multiplier and has dramatically improved my personal and professional relationships.<h3>Instead of "do unto others as you'd like them to do to you,"...</h3><h3>Do things to help others FEEL the way you would like to FEEL.</h3>This is important for the following reasons:<ul><li style="list-style-type: none;" data-mce-style="list-style-type: none;"><ul><li>Because of our individual life experiences, each of us feels different in response to different things. Different things are important to us. There are all kinds of personality tests and "love languages" to help expose these differences in our professional and personal interactions. Understanding the communication and interaction style of the other person—and doing the things that make them feel good—pays dividends. Communicating what makes you feel good helps them reciprocate.</li></ul></li><li style="list-style-type: none;" data-mce-style="list-style-type: none;">&nbsp;<ul><li>To do this, you have to understand the other person more deeply. You need to understand their needs, desires, and preferences. Doing the work to figure this out leads to deeper relationships.</li></ul></li><li style="list-style-type: none;" data-mce-style="list-style-type: none;">&nbsp;<ul><li>You have to understand yourself—what you want, what you need, and how you react to things—to communicate your preferences to others. Possibly even more important, you can recognize when you might be trying to give someone else what YOU want, not necessarily what THEY want. Bonus: Being honest and introspective with yourself always leads to growth, sometimes in ways you might not expect.</li></ul></li></ul>When you can cultivate relationships where both people do this well, the harmony and collaboration are incredible. When you can do this with a cohort of people whose talents complement yours and your talents complement theirs, the probability and velocity of success go way up!<h3>Thank you, Warren Buffett, for all you've taught us.</h3><h3>Here's to everyone's success and wellness!</h3>

Success: Wellness, Luck and the *Revised* Golden Rule

There is a grand experiment happening in AI right now, and it’s not just in large language models (LLMs). It’s on our roads. What Tesla’s FSD 12 is showing us tells us everything we need to know about the state of AI and, more importantly, what your company's strategy should be for the next two years. The Two Eras of Computing: Rules vs. Statistics For the vast majority of the information age, computer software has run on "expert" or rules-based systems. This is “if this, then that” (IFTTT) programming. It's entirely deterministic: given the same input, you get the exact same output, every single time. This is fantastic for predictability, but it’s brittle. If the world changes, the code must be manually updated to reflect those changes .We are now in the era of machine learning (ML), which is a completely different paradigm. Instead of hard-coded logic, these are probabilistic systems built on statistics. We feed them large volumes of data, and they "learn" how to respond.This new approach has benefits and drawbacks : Benefit: They are adaptive. As the data from the real world changes, the system can automatically retrain itself to react to new scenarios. Drawback: They are not deterministic. Under the hood, they operate on probabilities. At their core, these systems are designed to perform best in the "middle of the bell curve" of data. They are, by their very nature, terrible at handling edge cases because those cases are statistically rare. The Tesla Experiment: A Pure ML System in a Safety-Critical World This brings us to Tesla. For years, self-driving systems (including Tesla's pre-FSD 12 and competitors like Waymo) have used a hybrid model: a mix of ML and old-school, rules-based logic.With FSD 12, Tesla made the bold—and risky—move to go to an entirely machine-learning system. This is a "grand experiment" in a safety-critical system&nbsp;Tesla is arguably the only company that could even attempt this. They have more driver data and more sensor-equipped cars on the road than anyone on the planet. If anyone could make a fully ML system work, it would be Tesla.And yet, we're seeing the problems you'd expect. The system is struggling with the exact thing that probabilistic models always struggle with: edge cases. In driving, a statistically rare event—a deer jumping out, a sudden construction zone, an unusual piece of road debris—is the only thing that matters for safety. The middle of the bell curve is easy. The edges are life and death. The AI Plateau is Here This challenge with FSD 12 perfectly mirrors what we are seeing in the generative AI and LLM space. We are starting to see a plateau. The massive, easy gains are behind us. We have exhausted the quality data and the algorithmic innovation that gave us the huge leaps of the past few years. The next large step-function improvement will require more than just incrementally improving what we have now .For the past two years, the smartest strategic move for many businesses was simple: wait. We had use cases we knew AI could solve, but the models weren't quite good enough. We correctly decided to wait for the next release, which often did fix our problems.That era is now over.Your New AI Strategy: Stop Waiting, Start Building For the next 12-24 months, you must operate under a new assumption: the quality of the base models we have right now is nominally what we will have two years from now.Waiting for a magic new model to be released by a hyperscaler is no longer a viable strategy. So, what does that mean? It means the incremental value is no longer in the model itself, but in the systems you build around it .This is where you should be focusing your AI strategy. If the models are great at the "bell curve" but fail at the "edges," then the solution is to build systems that:<ol><li>Automate the vast majority of repetitive tasks. </li><li>Intelligently detect when the model is likely to make a mistake (i.e., encounter an edge case). </li><li>Loop in your human experts at exactly the right moment to handle that edge case. This "human-in-the-loop" approach is how you drive hallucinations down to zero. It’s a system that can’t be overtaken by a simple model update, because it involves your in-house experts and your unique processes.</li></ol>Where to Find the Highest ROI The best opportunities aren't always the flashiest. The highest and most immediate ROI we see with our clients is in managing internal processes: policy, procedure, quality control, and compliance.If you have repetitive tasks where you have good data defining what "right" looks like, but you struggle to get consistent implementation from staff, you have a perfect opportunity. We can help you build a tool into your process flow that provides immediate productivity gains.My advice is simple: identify those high-ROI, high-value implementations and put them in place now. Don't get overtaken by competitors who are stuck waiting for a "next big thing" that may not be coming.

The Grand Experiment

Large Language Models LLM and AI Artificial Intelligence Concept

<h3>What That Means for Your Enterprise Productivity</h3>Large Language Models (LLMs) have felt like a non-stop rocket ship since ChatGPT first launched, delivering exponential improvements every few months. For the last couple of years, the right strategy was often to wait and see—after all, a groundbreaking update was usually just around the corner.But that rapid pace of progress may be starting to plateau.Here at Nearly Human AI we specialize in high-accuracy, high-ROI enterprise AI solutions. And in the last three to six months, we've started seeing indicators that the frenetic pace of language model improvement is slowing down.The Plateau is Here (For Now)While we’ve seen major new releases, including the most recent versions, their underlying quality hasn't shown the same fundamental leap we saw between earlier generations. The core technology of LLMs appears to be reaching an optimal level of quality—one that still has a noticeable "error gap" we all recognize when using these tools.This is a natural part of machine learning's evolution. What we're observing now is a shift: companies are focusing their efforts on building applications around the existing LLMs rather than overhauling the underlying models themselves.For the enterprise, this marks a critical inflection point in your AI strategy.&nbsp; The New Strategy: Stop Waiting, Start IntegratingFor two years, the substantial, game-changing releases made waiting worthwhile. That benefit is largely gone. Over the next year, until we see another true technological breakthrough, the LLM capabilities we have right now are essentially the ones you'll be working with.This means now is the time to act.While the current technology has a quality gap, it also has immense, untapped utility. The key to extracting value is changing your approach from waiting for better models to mastering implementation and application design around the models you have.Here's how to do it: 1. Shrink the Problem and Narrow the ScopeTo achieve high ROI, you can't just throw a general-purpose LLM at a massive, complicated problem. Focus on:Specific Use Cases: Identify high-volume, repetitive tasks that can be clearly defined. Data Curation and Quality: Invest cycles in ensuring the data fed into the system for those specific use cases is clean, relevant, and high-quality. This is how you drive accurate outputs.2. Bring Human Experts Seamlessly into the LoopThe reality is that LLMs will hallucinate or make errors. The most effective way to address this is by designing systems that integrate human expertise to manage risk:Drive Hallucinations to Zero: We specialize in bringing human experts "into the loop" to review model outputs specifically when there's a risk of error. Massive Workload Reduction: Our policy and procedure bots, for example, can see 80-85% automation on sophisticated problems. This doesn't replace the expert; it frees them up to focus on the crucial 15-20% of issues that truly require advanced knowledge and experience.3. Shift Your Investment from "Improvement" to "Integration"If you're already working on LLM projects, stop spending excessive cycles trying to wring out tiny improvements from the base model. Instead, focus on integrating the current level of quality into your enterprise systems in a way that generates real value.And if you haven't started yet—the time is now. You won't get a "late start advantage" anymore by waiting for the next huge, labor-saving model to drop.&nbsp; The Future is Bolt-OnBy building your core systems now, you're not locking yourself in. When new breakthrough technologies eventually emerge, they will be able to bolt onto your existing, production-ready systems, expanding the set of use cases that have a positive ROI.Don't wait any longer. Move these projects into production, use the current state of the technology, and start extracting massive value today.Need help defining your highest ROI automation projects?&nbsp; Schedule a consultation. We’d be happy to help you with your enterprise AI strategy.

Are LLMs about as good as they are going to get for now?

Businessman using mobile online banking and payments, Online shopping and customer network connection.

Recently, I discussed an MIT study revealing that a staggering 95% of AI projects yield no measurable ROI. This is a stark contrast to what we're seeing: our AI implementations are delivering over 400% ROI. This discrepancy is unsettling, but I believe I've identified the core reason.Here's the bottom line upfront: If you've been working on an AI project for two months or more and haven't yet achieved a measurable 200% ROI, something is fundamentally wrong. The number one reason we're observing this failure in the banking sector is simple: going it alone.<h3>Pitfall #1: The Perils of Going It Alone</h3>Companies struggling to realize ROI from their AI systems are typically attempting in-house solutions. This often involves either existing internal teams or expensive consultants who aren't deeply invested in the long-term success of the organization. The result? Projects drag on endlessly, delivering negative ROI.Think about it: you wouldn't develop your core banking system or CRM in-house, and for good reason. Enterprise-level AI isn't much different. While using tools like Copilot or ChatGPT feels easy, it's akin to having your team manage customer relationships on a shared Excel spreadsheet – it simply doesn't scale.Why does this "go it alone" approach consistently fail?<ul><li>Overlooking Critical Aspects: Initial in-house implementations often miss essential components like proper software tooling, seamless integrations, robust data pipelines, and the long-term maintenance required as data and processes evolve. The initial "zero to one" might seem straightforward, but achieving the necessary quality for ROI quickly becomes challenging.</li><li>Slow Progress: Internal teams often work on AI projects part-time. They're solving complex problems for the first time, and the learning curve is steep. This inevitably leads to sluggish progress.</li><li>Compromised Quality: If you're using the wrong tools or approaches (and a pro tip: if you're currently using Copilot for enterprise-level AI, you likely are), the quality of your solution will suffer.</li><li>Lagging Adoption: A significant hurdle is adoption. If AI tools aren't seamlessly built into your existing workflows, they're often side-chained, difficult to use, and consequently, go unused. Without widespread adoption, you won't see any benefit.</li><li>Missing Empirical Reporting: Crucially, empirical data and reporting are frequently absent. This leaves you in the dark, unable to determine if your AI initiatives are actually working or delivering value.</li></ul><h3>The Rule of 70: Your AI Goalpost</h3>So, how do you know if you're on the right track? I use what I call the "Rule of 70": 70% adoption and 70% automation across the entire process you're targeting, with verifiable data to prove it.We've seen this rule in action with incredible results. For instance, our first customer literally doubled the size of their bank while simultaneously shrinking their compliance and operations departments. During this transformation, the customer and employee experience improved dramatically. This level of success is not only possible but happening right now.<h3>The Hard Truth and Your Next Step</h3>Here's the hard truth: if nothing changes, the 5% of companies successfully leveraging AI are positioning themselves to either acquire or bankrupt the 95% that aren't getting results. I don't want to see that happen to you.Time is money. AI implementations can be quick, delivering value rapidly. Waiting for results is costing you hundreds of thousands, if not millions, of dollars per year, not to mention lost ground to competitors.If you're reading this and thinking, "I don't believe that's true," consider which category you might be falling into. But if you resonate with these challenges, reach out. Visit our website or message me on LinkedIn, and let's book a call. I want to help you. I can either assist you in fixing your current project or provide an out-of-the-box solution that will generate immediate ROI.Don't wait. Be realistic about your AI investments and ensure you're getting those significant returns. <a data-mce-href="https://calendly.com/jamison-rotz/discovery" href="https://calendly.com/jamison-rotz/discovery" title="Book a call">Book a call</a> if you need any help whatsoever. We can help you fix a flagging existing project or offer one of our AI products that can get you going within weeks.

The Rule of 70 - Your AI Goalpost

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Earlier this summer, I had the privilege of joining the American Society for AI at the MIT Media Lab. It was an incredible experience to see some of the brightest minds working on cutting-edge projects. This week the MIT Media Lab released a study with a striking finding: 95% of enterprise AI pilots are failing.By “failing,” the study means either never making it into production or never delivering measurable business value.As someone who works daily with organizations to implement AI in the enterprise, I wasn’t surprised. I’ve seen these challenges firsthand. But here’s the good news: while 95% may not be getting it right, the 5% who do are generating massive returns—in as little as a 3 months.<h3>So what separates the 95% from the 5%?</h3>Why Most Enterprise AI Pilots FailThere are a few common reasons AI pilots stall or collapse before generating real value:Bottom-Up and Shadow ITMany AI projects start small—an experimental tool here, a shadow IT project there. It’s like when Excel first showed up in the enterprise: useful and valuable on an individual basis, but not scalable across the entire organization. Seat licenses are expensive, adoption is uneven, and value is hard to measure when everyone uses the tool differently.Internal Projects That Stall OutAI pilots often live as “side projects” for technical teams. They’re exciting, but they don’t always reach the quality bar needed for company-wide adoption. Without seamless integration into workflows, usage remains low, and value never materializes.Poor Adoption StrategiesEven when the technology is solid, adoption can make or break success. If only a handful of staff use the system, the ROI just isn’t there.<h3>What Successful AI Adoption Looks Like</h3>At Nearly Human, we recently rolled out an AI system that reached 70% adoption in the first week—a number I’d never seen before with any enterprise software. Even better, adoption grew over time instead of falling off after launch day.How did that happen? Two critical factors:High-Quality User Experience: We invested upfront in getting the data, implementation, and linguistics right so the AI was genuinely easy to use. The first impression mattered—and it was excellent.AI Embedded Into Workflows: Instead of building a side tool with a separate user experience, we put the AI directly into the heart of the enterprise process. Employees interacted with it because it was part of their natural communication and job flow—not an optional add-on.The result was less friction, faster problem-solving, and more consistency across processes. Staff wanted to return to the system because it made their work easier and better.The ROI of Enterprise AI Done Right When AI systems are implemented correctly—with the right tools, workflows, and adoption strategies—the ROI is staggering. We’re talking 300–600% within a single quarter.But to get there, organizations need to:Work with experienced partners who know which AI tools succeed and which don’t. Choosing the wrong platform can mean building a “bridge to nowhere.”Avoid building everything in-house. Development and maintenance take time and specialized expertise. Updates can improve one area while breaking another if not handled carefully.Factor in maintenance costs. Internal projects often fall into sunk-cost bias, limping along because teams don’t want to admit the investment isn’t paying off. A holistic cost-benefit analysis is essential.How to Move From the 95% to the 5% If your organization is struggling with AI pilots, you’re not alone. But you don’t have to stay in the 95%.<h3>To succeed with enterprise AI adoption:</h3><ol><li>Partner with experts who have deployed successful AI systems.</li><li>Focus on quality and user experience, not just technology.</li><li>Embed AI directly into workflows instead of bolting it on.</li><li>Look beyond the pilot phase to long-term maintenance and ROI.</li></ol>The organizations that follow these steps are seeing transformational outcomes in both customer and employee experience.<h3>Final Thoughts: Making AI Work in the Enterprise</h3>AI in the enterprise isn’t about flashy demos or hype—it’s about embedding high-quality, well-maintained systems into real workflows to deliver measurable business value.If you’re struggling with your AI pilots, let’s talk. You can <a data-mce-href="https://calendly.com/jamison-rotz/discovery" href="https://calendly.com/jamison-rotz/discovery" data-mce-selected="inline-boundary">book a call</a>.&nbsp; For community banks, we already have a product that can be deployed in just one week, offering a low-cost, low-risk way to improve customer and employee experience.New verticals are coming soon, so stay tuned.Until then—keep pushing toward that 5%. The ROI is real, and it’s worth it.

Why 95% of AI Pilots Fail—and How to Achieve Enterprise AI ROI

Small language model, SLM with chipset concept. Abstract stream information, Generative AI.

OpenAI recently unveiled GPT 5, and for everyday use, it appears to be somewhat unremarkable compared to the 4 series models. That said, initial reviews are touting GPT 5 as an absolute game-changer for coding. This comes as a bit of a surprise, as OpenAI has frequently discussed a strategy of leveraging AI to write the code that will further expand AI capabilities, yet a coding-focused model wasn't what I had anticipated from this particular release based on the chatter leading up to it. This shift alters my perspective on the future trajectory of AI. We've been involved in developing "AI" automations for the past 12 years, long before we collectively began referring to machine learning systems as "AI." The statistics-based methods inherent in machine learning have consistently struggled with what I call the "quality gap"—the persistent 10-20% of the time that models tend to get things wrong. This gap is particularly stubborn due to edge cases—rare circumstances that are crucial to handle correctly but are, by their very nature, on the edges of the statistical bell curve. These are inherently difficult to address using traditional machine learning approaches. Attempting to balance data to account for these outliers often poisons the "golden middle" of the bell curve, thereby degrading the model's performance on typical operations. We've typically mitigated this by implementing rules-based backstops around machine learning systems and/or through human-in-the-loop methods. Both approaches are costly and not particularly scalable. I always viewed this as a significant, potentially insurmountable hurdle on the path toward Artificial General Intelligence (AGI). It's a foundational problem for machine learning that seemed unlikely to be resolved simply by adding "more data." With the release of GPT 5, I'm beginning to envision a world where we could establish a fairly scalable ecosystem. In this system, machine learning models could handle the bulk of our tasks, while specialized expert systems could be GENERATED to address the specific edge cases associated with various use cases. While this isn't the fully scalable solution we often imagine when contemplating AGI, it could dramatically accelerate the deployment of highly reliable AI implementations. This aligns perfectly with the Nearly Human and Manada Technology Enterprise AI model, which is incredibly exciting to me. What remains to be seen, is how this development will enable OpenAI and other hyperscalers to advance agent software. This is an area where it feels like there is still a considerable distance between our current capabilities and the envisioned potential of AI agents. It's truly an exciting time to be innovating in the technology space!<a data-mce-href="https://calendly.com/jamison-rotz/discovery" href="https://calendly.com/jamison-rotz/discovery" data-mce-selected="inline-boundary">Book a call with us!</a>

Blog

Success: Wellness, Luck and the Revised Golden Rule

The Grand Experiment

Are LLMs about as good as they are going to get for now?

The Rule of 70 - Your AI Goalpost

Why 95% of AI Pilots Fail—and How to Achieve Enterprise A...

GPT 5 and what it means for the future of AI