Imagine a world where truly groundbreaking technology like AI promises to transform, well, everything, every industry, every process. Now, pick your almost all of those big efforts just not delivering. It's kind of this quiet crisis happening. Yeah, totally. It feels a lot like, you know, bolting a jet engine onto an old horse cart, doesn't it? Exactly. The idea sounds cool, maybe, but the reality, that ride is going to get messy,
spectacularly messy fast. So today we're going to peel back the layers, try and understand why that like 95 % failure rate is so common. And crucially, how you can be among the successful 5%. Right. Welcome to the Deep Dive. Our mission today is pretty straightforward. Give you a clear, actionable way to think about AI implementation. Exactly. We're diving deep into this critical issue, the staggering failure rate of automation
projects in the AI space. We'll unpack the core reasons, drawing on some recent analyses, including work by Max Anne. But hey, it's not all like doom and gloom here. Actually, it's quite the opposite when you look at it, right? We're then going to reveal this proven three -phase framework. It's called the Morningside Method. This is apparently what the successful few are using. Okay. And the key. It's all about focusing on the process first. not the technology, turning that internal
chaos into actual growth. So you're basically getting a strategic roadmap to make AI work for you. Okay, let's really dig into this. You see the headlines about AI everywhere, right? But the reality for... Most businesses, it's pretty stark. Research, and this includes sources like MIT, consistently shows that a shocking 95 % of enterprise AI initiatives. 95? Yeah, those big expensive projects, they just... failed to deliver any real measurable return on investment.
It really is that jet engine on a horse cart problem. It is. Companies are trying to attach this incredibly powerful, like cutting edge AI onto their old, often kind of broken internal systems. Right. The foundation isn't there. Exactly. And, you know, a huge piece of that is what this analysis calls organized chaos. Most businesses, they're living in it. Organized chaos. I like that. Yeah, me too. They're data. It's like.
scattered everywhere, disconnected spreadsheets, old legacy apps, their processes, often just a tangle of how we've always done it, usually undocumented, held together by like duct tape in someone's memory. Tribal knowledge. Totally. So plugging powerful AI into that, it isn't going to speed things up in a good way. It just amplifies the mess. Magnifies the existing process. Exponentially. You get garbage in, garbage out, but like faster
than ever before. It's more like a spectacular explosion than... acceleration honestly mm -hmm and beyond just that internal mess another major issue is the copycat strategy ah yes you see companies chasing the hype right just mimicking what maybe a competitor is doing or some trendy startup without really thinking if it fits them exactly No deep strategic look at their own unique needs. They're not asking what specific problem are we trying to solve here or how does this
actually align with our business goals. So they end up with these expensive totally irrelevant solutions. Precisely. It's a recipe for failure. But you know the deepest root and this is what's really fascinating because it gets overlooked so often is the human problem. OK. Tell me more. Well. Companies pour millions into the tech. Right. And then they completely forget about their employees. The, you know, emotional, often fearful people who actually have to use this
stuff. Right. Change is hard. It is. You've got people resisting change. Maybe they lack proper training or they're just plain uncomfortable with new ways of working. Ignoring these human barriers, fear of job loss, the like a mental load of prompt engineering, the discomfort of shifting roles, it almost guarantees things won't stick. It's easy to focus on the shiny tech. So easy. But the human element, it's incredibly powerful. I mean, honestly, I still wrestle with
prompt drift myself sometimes. Prompt drift. Explain that quickly. Yeah, it's basically when the AI's output kind of subtly changes over time, even with small shifts in how you ask it things. Seeing how even tiny changes like that can just throw a whole team off their rhythm. It's tricky. So when we boil it all down, why do most AI projects
fail? What's the fundamental issue? It really seems to consistently be focusing on the flashy technology before truly understanding and, you know, streamlining the messy internal processes and the people involved. Okay. So this huge 95 % failure rate, it sounds daunting, but the perspective here is that it's not just a problem. No, not at all. For those who approach AI smartly, it's
framed as an enormous opportunity. Exactly. It's like everyone else is digging for gold over there in the wrong spot and you've just found the map to the actual mother load. Interesting analogy. So for business owners. For business owners, this is how you get what they call an unfair competitive advantage. Unfair advantage. Okay. Yeah. While your competitors are like wasting time and money on these chaotic failed projects, you can quietly build a really durable AI powered
advantage. Durable meaning sustainable. Right. This isn't just like a small improvement. This could genuinely help you. dominate your industry for years, you're building on a solid foundation. What about for entrepreneurs, agency owners? Oh, for them, it's framed as nothing short of a gold rush opportunity. Seriously. A gold rush. Think about it. Millions of businesses desperately want the power of AI. They see the potential buzzwords, but they have absolutely no clue how
to get a real measurable return. They need help navigating it. Totally. The market is wide open for people who can step in and become that expert guide, the ones who can actually deliver results. So what kind of expertise wins big in this AI gold rush? It's really being that expert guide who brings AI power effectively and profitably to businesses that genuinely need it. Okay, this is where it gets really practical. The framework highlighted, the Morningside Method. It starts
with three distinct phases. Phase one is education and alignment. And this is the one almost everyone skips. Right. Why is it so important? Think of it like the mission briefing before big operation. You got to get all the leaders, all the generals on the same page first. Show them the map, the objective. Exactly. The main goal here is to establish a really clear strategic vision for how AI is actually going to fit into this specific
company. So alignment is key. Totally key. It means getting the entire leadership team completely on board. They need to understand the strategy, the terminology, what are we even talking about here, and the real opportunities, usually through focused AI leadership workshops. Makes sense. Because without that shared understanding, any talk about transformation is just, well, it's just noise. You can't build something coherent if everyone's speaking a different language about
it. And there's this interesting tactic, Pinchin, the inception tactic. Yeah, what the? Explain that. It's about doing the education before specific recommendations. Precisely. This vital education process, getting them up to speed, happens before you even start pitching specific AI tools or projects. Because it ensures the leadership is already bought into your logic and terminology first. They understand the why behind your approach before you present the what. Ah, so it smooths
the path for approvals later. Massively smoother. It's actually a really powerful psychological trick for managing change. Get them nodding along with the principles first. And then there's this visual tool. the AI -first org chart. That sounds potentially scary. It sounds scary, but it's not about who gets fired or replaced. That's not the point here. It's about showing how AI can augment the team, how it can create new efficiencies, maybe even new roles, new capabilities they didn't
have before. So visualizing the future state. Exactly. It helps leadership really visualize that final destination of the transformation, helps them see the potential of their people working with AI not being replaced by it. So let's recap phase one. Before any building starts, why is educating that leadership team so profoundly crucial? Fundamentally, it establishes that shared strategic vision and it solidifies their buy -in for everything that comes next. Got it. Okay.
So leadership is aligned, clear vision in place. Phase two then shifts gears to a deep business investigation. Right. And the goal here is ambitious. Understand the company better than they understand themselves. Yeah. Become the absolute expert on how their business actually functions day to day, including all those messy, inefficient bits they've just, you know, learned to live with over the years. So how do you do that? Step
one. Step one is comprehensive interviews. Think of it like taking the patient history when diagnosing an illness. Talking to people. Talking to everyone. Yes, department heads, sure. But crucially, also the frontline staff. The people doing the actual work. On the ground insights. Exactly. You really dig into their daily challenges, their frustrations, what takes up all their time. You're trying to uncover those deep insights that, you know, spreadsheets
and reports will just never show you. Okay. Interviews first. Then step two. Step two is developing the process map. This is like giving the business an MRI scan. A visual map of workflows. Yeah. Using tools, maybe like Figma or similar, to visually map out all the core workflows end -to -end, how does information really flow? Where are the handoffs? I bet for many clients that's
eye -opening itself. Oh, absolutely. For many, this visual map is the very first objective look they've ever had at how their business actually operates. It can be a huge aha moment. Suddenly, bottlenecks just jump off the page. Okay, interviews, process math, step three. Step three is use case identification. And this, they say, is where the amateurs get separated from the experts.
How so? You take that detailed process map you just built and you compare it against a carefully curated database of proven real world AI systems. Ah, so not just brainstorming AI ideas, but matching problems to existing solutions. Exactly. Pinpointing the exact bottlenecks like manual data entry, endless report writing, super repetitive. customer queries, and matching them to AI tools already working out there in the real world. Proven solutions. Yes. Focus on solutions already in production,
not like experimental moonshots. We're talking about leveraging specialized AI services for specific repetitive tasks, like, say, using a transcription API to turn meeting notes into text for automated summaries. Not trying to build a brand new AI model from scratch to write a novel. Got it. Practical application. And the final step. Step four. Finally, step four is opportunity grading and validation. Okay. What
does that involve? You take all those potential opportunities you identified and plot them on a simple matrix. Usually it's value versus difficulty. Value versus difficulty. Makes sense. Yeah. It helps you prioritize. You want a mix of quick wins, high value, low difficulty for that immediate ROI, and maybe some big swings, high value, higher difficulty for longer term advantage. And validation.
Crucially, this involves dual validation. You need buy -in from both the employees who are actually doing the work, do they agree this is a real problem, and from leadership for the strategic fit. So bottom -up and top -down validation. Exactly, the end result of all this. A comprehensive AI strategy roadmap, often like 50, 100 pages detailing the plan. Okay, so focusing on phase
two again. How do you make sure those AI opportunities you identify aren't just theoretical, but are actually practical and will be accepted by the team? It comes down to validating the problems directly with the employees experiencing them and then securing that leadership buy -in for the strategic alignment. Dual validation. Right. Okay. Makes sense. We'll take a quick break here. Sounds good. Sponsor read. All right. We're back. We've got leadership aligned from phase one and
a clear validated roadmap from phase two. Now, phase three is where we actually turn that plan
into growth. development and implementation yeah this is where the rubber meets the road as they say where the planning turns into tangible results and there's a key rule here to start with oh yeah absolutely paramount the quick win rule which is always always start with the quick win not the big flashy project never never ever begin with some massive six -month multi -million dollar moonshot project that is just a guaranteed way to lose trust lose momentum really fast so start
small Get runs on the board. Exactly. You build credibility. You build enthusiasm by tackling projects that blend high impact with low difficulty. Get that immediate, tangible ROI first. That makes sense. Build momentum. And whoa, just imagine taking one of those maybe kind of boring, quick win solutions, proving its value in one department and then scaling it across a whole global enterprise. Yeah. The cumulative effect could be. Absolutely staggering. It's actually fascinating how much
power is in those, quote, boring systems. Isn't it? For most businesses, the biggest, most profitable quick wins, they often solve those just soul -crushing manual tasks. Like what? Things like, you know, endless manual data entry, copying from one system to another, writing the same report format over and over, or answering the same five customer service questions 80 times a day. Stuff nobody wants to do. Exactly. These aren't flashy, maybe. But their impact on productivity
and morale, it's profound. So give me some examples of these boring but high -impact tools. Yeah, sure. Simple things, really, like smart voice agents to route calls correctly the first time, intelligent transcription services for meetings or notes, or maybe internal document query tools so employees can find info without asking someone. Things that save time and reduce frustration. Precisely. These aren't like super complex algorithms
needing a team of THDs. They can save hundreds of hours a month and easily deliver a six -figure ROI. And often from a relatively small investor. Often. Think maybe $20 ,000 to $50 ,000 sometimes to get started on a specific problem. That kind of return. That's a total game changer for most businesses. It's about smart, targeted application, not just raw AI power. And this whole approach, starting with deep understanding and quick wins, it also fosters something else. Yeah. Long term
partnerships. How so? Because you've done that deep investigation in phase two, you understand their business deeply. You're not just a vendor selling a tool. You become a strategic ally. A trusted advisor. Exactly. It naturally leads to ongoing work, continuous improvement cycles. You know where the next opportunity is. And there's another angle to the AI upgrade opportunity. Right. This is built in every time a new, more powerful AI model comes out, which, let's be
honest, is happening constantly now. All the time. It creates a new business opportunity. for you if you're the partner. You can go back to your existing happy clients and say, hey, we can upgrade that system we built for you, make it even better, maybe even cheaper to run now. Increasing the lifetime value of that client. Hugely. It becomes this ongoing positive cycle.
Okay, so wrapping up phase three. When it comes to that initial implementation of AI solutions, what's the single most crucial rule to follow? Always, always start with those quick wins that deliver immediate, tangible ROI, not the massive long -term moonshots. Build trust first. Got it. So bringing this all together, what does this three -phase method mean for you, the listener? whether you're maybe building an AI business or you're a leader looking to implement AI in
your company. Well, this playbook offers a pretty clear path, regardless of which side you're on. Okay, let's take the first group, AI agency owners and entrepreneurs. For them, it's really presented as a journey, like leveling up over time. How does that look? Maybe you start as a no -code agency, using those visual drag -and -drop tools to automate simpler stuff, learn the ropes, understand business processes. Get some experience. Right.
Then maybe you evolve into a full stack agency, bring in developers, build more custom, complex solutions for clients. OK. And eventually, after years of doing this, really being in the trenches, you can become a true transformation partner, selling that whole strategic process we've been talking about from education through implementation. So a growth path for the service providers. Yeah. Now, what about for business owners, people inside companies wanting to use AI? For them, there
are really two clear options presented. Option one. The DIY AI sounds tough. It is challenging, no doubt. But the idea is you take this playbook, these phases, start internal conversations, maybe run a small audit on just one department. Find a pilot project. Build some internal momentum. Possible but requires commitment. Definitely achievable with persistence and real commitment from leadership. And option two. Option two is the work with experts path. Hire an agency or
consultancy. But with a caveat, I assume. A big one. You have to choose carefully. Look for partners who are genuinely focused on long -term strategic relationships, not just trying to make a quick sale on some software. And crucially. Crucially, make sure they follow a process -first methodology. They shouldn't just walk in and immediately start recommending flashy tech. They need to do that deep dive first. Yes. That distinction, prioritizing understanding your existing processes first,
is absolutely vital for success. Ask them about their process. Okay, so for a business owner choosing that AI partner, what's the single key consideration they need to keep front of mind? Just ensure they prioritize that process -first methodology over simply trying to sell you the latest flashy technology process -first. Okay, so let's try to distill the big idea from this deep dive. What's the core message? I think it's
incredibly clear. We're in the middle of a process -first revolution when it comes to AI actually working. Process -first. Meaning the most powerful AI in the world won't help you if your underlying business processes, your house, is basically on fire. Exactly. The opportunity with AI is truly immense, no question. But most people, most companies, they're approaching it completely backward. How so? They skip those vital first steps, the education, the deep identification
of problems and processes. They jump straight to development, thinking the tech itself is the answer. It's like trying to paint a house before you've even laid the foundation. Perfect analogy. It simply doesn't work long term. You might get a demo that looks cool, but not sustainable results. So real sustainable success, the kind that actually drives measurable ROI and gives you a competitive edge, comes from following that complete three -phase process. Right. First, education for alignment.
Get everyone on board. Second, identification to deeply understand the business reality. And only then. Only then. Third, development, implementing the right solutions for measurable growth. Starting with those quick wins. It really seems the winners of this whole AI revolution. won't necessarily be the ones with the fanciest algorithms or the biggest R &D budgets. Probably not. They'll be the ones who fundamentally understand that true, lasting transformation always, always starts
with people and processes. Technology is the enabler, not the starting point. So a final thought for everyone listening. Maybe reflect on your own organization. Is your business experiencing its own version of that organized chaos? Yeah. Does it maybe need that strategic process first approach before you try and bolt on that expensive AI jet engine? What stands out to you about starting with the process, not just the technology? Something
to think about. Definitely. Thank you for joining us for this deep dive into making AI automation actually deliver results. Yeah. Thanks for listening. Until next time, keep digging deeper.