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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://outlook.office.com/bookwithme/user/89cd34eb739a43d88c015c3778ed5740@nearlyhuman.ai/meetingtype/ITbD6eimmkuuRqfaW8kUdw2?anonymous&amp;ep=mCardFromTile" href="https://outlook.office.com/bookwithme/user/89cd34eb739a43d88c015c3778ed5740@nearlyhuman.ai/meetingtype/ITbD6eimmkuuRqfaW8kUdw2?anonymous&amp;ep=mCardFromTile">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

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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!

GPT 5 and what it means for the future of AI

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<h3>Let me be direct: </h3>I believe most AI experts get it wrong when it comes to community banks — because they misunderstand what community banking is really about. Community banking isn’t about technology first. It’s about relationships. It’s about trust built over time. It’s about knowing your customer’s name — and their story. It’s about a real human being on the other end of the line who cares. That human connection is not a cost center. It’s the very core of your brand. And when AI experts push an “AI-first” strategy, they miss the point entirely.<h3>The AI-First Strategy Won’t Work in Community Banking</h3>It might work for tech giants or call center factories — but not here. Why? Because your value isn’t in scale. It’s in relationship capital. And that capital erodes the moment you replace a caring conversation with a cold script or a chatbot. At Nearly Human, we believe something very different: AI should never be the star of the show. If this were a movie, the customer would be the main character. The human banker would be the guide. And AI? It’s the quiet, loyal supporting actor — making everyone else better.<h3>Nearly Human OpsPilot Was Built for a Human-First World</h3>We don’t put bots between your people and your customers. We put AI behind your people — like a co-pilot who never sleeps. That means: <ul><li>Context is ready the moment the call begins</li><li>Reps can focus on listening instead of multitasking</li><li>Emotional intelligence stays front and center</li><li>The human touch gets amplified, not replaced</li></ul>This is the human-first AI model — and it’s the only one that aligns with what community banks actually stand for.<h3>Relationships Are the Differentiator — Don’t Give Them Away</h3>Let’s be honest: Local relationships and high-touch service are the key differentiators for community banks. It’s what sets you apart in a world of impersonal, algorithm-driven interactions. Without that, you’re just a smaller version of today’s highly automated megabanks — and customers will treat you as such. Why would they choose a small bank with fewer features if it feels just as impersonal? The only path forward is doubling down on what makes you unique — the human connection — and using AI to reinforce it, not erase it.<h3>Because You Don’t Just Serve Accounts — You Serve Relationships</h3>Your customers aren’t looking for automation. They’re looking for someone who knows them. That’s why community banks thrive — because you show up when it matters. Our job at Nearly Human is to help your people show up stronger, more focused, and more supported — without compromising who they are or how they serve.<h3>Pro-Relationship. Pro-Performance. Pro-Human.</h3>We believe AI can enhance performance without sacrificing empathy. Community banks using Nearly Human are seeing: <ul><li>25–40% faster resolutions</li><li>Stronger customer satisfaction</li><li>Less burnout on the front lines</li><li>And most importantly — deeper relationships at scale</li></ul>Because when you support your people, they deliver the magic that keeps customers for life.<h3>The Bottom Line</h3>You don’t need an AI-first strategy. You need a human-first strategy — powered by the right kind of AI. Nearly Human was built for that. To protect what makes you special, and scale it smartly — without losing your soul.<a data-mce-href="mailto:rona.melchor@nearlyhuman.ai" href="mailto:rona.melchor@nearlyhuman.ai" title="Book a free consultation" target="_blank" rel="noopener">Book a free consultation</a> to learn how we help community banks stay personal, powerful, and unforgettable — even in an AI-powered world.

Why I Disagree with 99% of AI Experts When It Comes to AI and Community Banks

Remember those days? The Wild West of spreadsheets. Everyone, from the intern to the CEO, had their own personal Excel masterpiece. Budgets, trackers, project plans – all meticulously crafted, often with a hidden formula that only made sense to the creator (and sometimes not even to them a week later).<h3>The Glorious Chaos of Personal Tools </h3>Excel, in its prime, was the ultimate "bottom-up" tool. Need to track your tasks? Excel. Generate a massive SQL insert script? Excel. Create the executive dashboard? Excel. The beauty was its accessibility. Anyone could jump in, hack something together, and instantly feel like a data wizard.Sales teams loved using 17 different Excel sheets to track leads. Each rep had their own, with their own unique color-coding system and a "secret" column for "gut feeling." It was brilliant for them individually, a nightmare for the sales manager trying to get a unified forecast. "Just consolidate them!" The response was always a blank stare, followed by "But mine works for me!"<h3>Enter ChatGPT: The New Kid on the Block (with a Familiar Vibe) </h3>Fast forward to today, and we're seeing a similar phenomenon with ChatGPT. Everyone's tinkering. Need a quick email follow up? ChatGPT. Brainstorming blog post ideas? ChatGPT. Analyzing reports? ChatGPTIt's empowering, right? Suddenly, you have a personal AI assistant, ready to churn out text, code, or even terrible poetry on demand. It's the ultimate "bottom-up" AI tool.<h3>The Inevitable Scaling Headache </h3>But here's where the comparison gets a bit more… Excel-like. Just as those individual spreadsheets started to buckle under the weight of team collaboration and enterprise demands, so too will our personal ChatGPT hacks.Imagine this: A marketing team, each member using their own ChatGPT prompts and methods to generate campaign copy. Some are getting amazing results, others… not so much. The brand voice is fragmented, the messaging inconsistent. The marketing director throws their hands up and says, "Can't we just have one approved AI for this?"Or consider a support team. One person uses ChatGPT for quick answers, another for empathetic replies, a third for comedic relief. While individually helpful, the lack of a standardized, scalable approach means inconsistent customer experiences and a mountain of unmaintainable, idiosyncratic AI "workflows." Add the demands of corporate policy or worse yet compliance constraints and it all falls apart pretty quickly.<h3>The Future: From Personal Hacks to Enterprise Solutions </h3>Just as we learned to embrace proper databases, CRM systems, and collaborative tools over countless individual spreadsheets, we'll need to do the same with AI. The individual experimentation with ChatGPT is invaluable – it's how we discover what works and what doesn't. But for real business impact, we need to move beyond isolated, unscalable hacks.I’ve been working in the enterprise AI space for 12 years - starting way back when we were calling it “predictive analytics!” My companies have developed platforms to become great at picking up those diamonds in the rough and creating enterprise agents. That means setting up data governance, software maintenance process, responsibility and safety controls and putting experts seamlessly in the loop to close the AI quality gap.&nbsp;We’re seeing a lot of companies who are starting to wonder why they aren’t getting the productivity juice from their AI squeeze, and a few who are ahead of the Enterprise AI Curve seeing 400%+ ROI in just a few months.If you’re in the rarified air, congratulations! You’ll likely dominate within the next 2-3 years. If you need some help getting there, reach out to me.<a data-mce-href="mailto:rona.melchor@nearlyhuman.ai" href="mailto:rona.melchor@nearlyhuman.ai" title="Schedule a Demo!" target="_blank" rel="noopener" data-mce-selected="inline-boundary">Would you like to see for yourself? Click here to schedule a demo!﻿</a>

ChatGPT is becoming the new Excel

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<h3>&nbsp; Customers Are Telling Us What They Want—But Few Banks Are Listening</h3>In study after study, the message is loud and clear: banking customers are frustrated.● Nearly 70% of customers say they’ve had at least one negative&nbsp; &nbsp; &nbsp; service experience with their bank in the past year.● Over 50% say they feel like “just a number.”● 64% of customers say they would leave their bank for better service—even if it meant giving up perks or slightly better rates.Despite billions spent on automation, chatbots, and self-service tools, the experience gap is growing. Why? Because most banks have misunderstood the real issue. It’s not about speed alone—it’s about trust, feeling valued and being well taken care of.Automation Isn’t the Enemy—But It Can’t Be the Hero EitherThe AI movement in banking has largely missed the mark. By putting bots in front of customers and pushing humans to the background, many banks have unintentionally made their service feel colder, not smarter.Community banks now have a unique chance to flip that script—by doing what they’ve always done best: lead with humanity. And with the right technology working quietly in the background, that high-touch service can become even stronger.<h3>This is the Nearly Human approach:</h3>● Keep people front and center● Use AI to support, not replace● Scale service—not just efficiencyHigh Touch Meets High TechAt Nearly Human AI, we believe the future of banking belongs to institutions that can deliver the best of both worlds:✅ The empathy and connection of a community banker✅ The speed and precision of modern AIOur platform works behind the scenes to learn from every interaction—allowing your bankers to know more, respond faster, and serve better. The result? A better experience for the customer and a more empowered team.<h3>The Hidden Cost of the Status Quo</h3>Community banks that delay action face growing risks—not just to customer satisfaction, but to long-term viability. Ignoring this shift can come at a steep cost:● Loss of customer trust and loyalty as expectations continue to rise● Increased churn to fintechs and digital-first competitors● Higher operational costs from inefficient, manual processes● Staff burnout due to overwhelming workloads and repetitive requests●&nbsp; Reputational damage from poor reviews and customer complaints● Costly compliance errors and increased regulatory scrutiny<h3>The Win for Customers</h3>A human-first, AI-supported model doesn’t just fix frustrations—it transforms the entire banking experience. Here’s what customers stand to gain:● Complex issues solved the first time—without the wait● Empathetic interactions, even during stressful financial situations● Consistent and proactive communication tailored to their needs● Human interaction<h3>The Win for the Bank</h3>And the benefits for community banks are just as compelling. Adopting a human-first, AI-supported approach delivers:● Stronger customer loyalty and higher retention● Reduced compliance costs● Increased cross-sell and up-sell opportunities through smarter engagement● Improved employee satisfaction by automating repetitive tasks● A modernized brand reputation as a tech-forward, human-first institution<h3>Use Cases That Matter</h3>The possibilities for community banks are enormous. Here are just a few areas where a human-first, AI-enabled strategy is already showing results:● Proactively supporting customers facing financial stress● Freeing bankers from low-value tasks so they can focus on real conversations● Turning routine customer service into loyalty-building moments<h3>The Bottom Line</h3>You don’t need to be a megabank with a billion-dollar tech budget to innovate.You just need to choose a different path—one that values people over process, relationships over tickets.Nearly Human AI is a human-first, AI-enabled customer experience platform built for community banks. We help you deliver warmth, empathy, and intelligence at scale—without losing your personal touch.<a title="Schedule a Demo!" href="mailto:rona.melchor@nearlyhuman.ai" target="_blank" rel="noopener" data-mce-href="mailto:rona.melchor@nearlyhuman.ai">Would you like to see for yourself? Click here to schedule a demo!</a>

Blog

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

GPT 5 and what it means for the future of AI

Why I Disagree with 99% of AI Experts When It Comes to AI...

ChatGPT is becoming the new Excel

Why Banking Customers Are Still Complaining—and How a Hum...