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.