Forget what you think you know about success
in AI, really. It's not about writing code. It's
about clarity. So many people chase the shiny
tools, you know, like collecting hammers without
a blueprint. That's not how you build a house.
This is Deep Dive. We're going to show you a
different way. Welcome to the Deep Dive. Today,
we're taking a really clear, focused look at
becoming an AI automation strategist. Our mission
here is to unpack the real skills, the ones that
deliver tangible lasting value to clients, all
without the burnout and without needing a single
line of code. Yeah, exactly. And we're going
to break down seven foundational capabilities
for you. It's a journey. It starts with truly
understanding how AI actually thinks, you know,
all the way to packaging your expertise for real
commercial success. Think of it like architecting
outcomes, not just building bits and pieces.
It's kind of like designing a super efficient
machine instead of just, you know, soldering
a few wires together and hoping for the best.
Mm -hmm the AI automation era is definitely here.
I mean you see it everywhere and businesses They're
desperately looking for ways to cut that operational
drag find those efficiencies and that creates
this huge Honestly booming demand for a very
specific kind of expert Absolutely, and the common
myth swirling around is oh you need deep coding
skills right to get into this space But the truth
the profound truth is that the most valuable
players right now They're the ones who can architect
outcomes, not just the AI itself. They're solving
core business problems. That's the key. It feels
like so many people fall into that trap, though.
They're chasing the latest shiny tool. They're
collecting hacks. They're mistaking all that
activity for actual meaningful progress. It really
is like trying to build a house by just gathering
a pile of hammers, ignoring the blueprint entirely.
And let's be honest, who hasn't been guilty of
collecting a few too many digital hammers at
some point? I know I have. Yeah, it's easy to
get caught up in the hype. It really is. But
you honestly don't need encyclopedic tool knowledge.
What you need is a curated set of high -livered
skills. Things that really move the needle. This
deep dive. It lays out a clear playbook for success.
No burnout. No franken systems. You know, those
fragile, cobbled together tech solutions that
cause more headaches than they solve. And truly,
No coding required. OK, so it's a fundamental
shift in thinking we're talking about here. Can
you elaborate on that? What's the core change?
Yeah, it's moving from that constant tool chasing
to outcome driven strategy. Simple as that. Focusing
on results, not just the tech itself. Got it.
All right, let's dive into the first essential
skill then, generative AI fundamentals. So many
people jump straight to asking, you know, which
tool should I learn? But. That, as you just kind
of hinted at, is absolutely the wrong question
to start with. It really is. The much more powerful
question, the one that truly unlocks your potential,
is how do these systems fundamentally operate?
Think about it. Tools evolve at lightning speed,
right? They're changing constantly. But the core
principles, the underlying physics, you could
say... Those endure. And generative AI, when
you strip it down, it's a massively powerful
pattern recognition and generation engine. It's
not some all -knowing oracle. Understanding that
distinction, that fundamental operating principle
that shifts you from being just a user to being
a true architect of these systems. Precisely.
You'll master the physics. Things like how large
language models, LLMs, these powerful computer
programs that generate human -like text, how
they use probability, how they use context windows,
which is basically the limited memory the AI
has at any given moment, and tokens, those little
building blocks of text AI processes, like parts
of words, how it uses all that to build responses.
You'll also grasp the crucial difference between
generative AI, which creates new stuff, and traditional
predictive AI, which is more about forecasting.
Plus, you get core concepts like transformers,
that's the revolutionary architecture behind
most modern AI, letting it understand relationships
in long text, and embeddings, which are these
numerical ways of representing meaning, making
words understandable to AI for really smart searching
and comparison. Okay, so this foundational knowledge,
then, it isn't just academic theory. It actually
helps you spot what we call cognitive automation
opportunities, which means automating repetitive
thinking, not just simple clicks or data entry.
We're talking automating the mental work. Exactly.
Think about the possibilities. Automating parts
of client onboarding, streamlining content strategy
creation, optimizing sales funnels, even seriously
enhancing customer support. It's about taking
that routine mental load off your human employees
so they can do higher value stuff. And for learning
this, you might check out Vanderbilt's Generative
AI Automation on Coursera, or even quicker overviews
from places like Google or deeplearning .ai.
This knowledge really is your operating system.
It's fundamental. So how does understanding this
physics of AI specifically empower us to direct
AI systems more effectively? It lets us direct
AI as a systematic tool, really building on its
core physics. Understanding its essence lets
you guide its actions. OK. Next up advanced prompt
engineering treating this like a simple Google
search That's a huge mistake because really quality
output directly reflects quality input. It's
like that old saying garbage in garbage out still
holds true Well, absolutely in this new AI powered
economy prompt engineering isn't just some tech
trick. It's a foundational business skill seriously
It's all about translating complex human objectives
into precise actionable machine instructions
This is where the real leverage is And it's how
you scale high quality content creation, develop
powerful internal tools, and reliably bridge
what a client asks for to what the system actually
produces. It's the language of leverage, really.
Exactly. Real prompt engineering. It involves
precision. We're talking things like structured
prompting, using formats like XML or maybe JSON
to define roles and constraints very clearly.
Then there's chain of thought prompting, where
you actually guide the AI through logical step
-by -step reasoning. You show it how to think.
Output priming ensures you get the exact formats
you need, say a perfect JSON object every time,
and finally, systematic debugging. Because prompts,
just like code, they need refinement. They don't
always work perfectly the first time. Let's make
that concrete. A bad prompt, a really basic one,
might be something super generic, like, write
a social media post about our new project management
tool. Just kind of vague. Right. Now compare
that to a professional structured prompt. That
prompt defines the AI's role. Maybe it's acting
as a senior social media manager. It sets the
context, the desired tone, the key benefits you
need to highlight, and then strict constraints.
Character limits, specific hashtags to include,
a clear call to action, and yeah, even the exact
output format. like requesting a JSON object,
it leaves almost nothing to chance. You're guiding
it very specifically. That structured prompt
example perfectly illustrates the power, doesn't
it? One really well -crafted prompt can potentially
replace an entire multi -step manual content
workflow. You're not just vaguely asking the
AI for something. You're actively architecting
its thought process. But beyond just getting
better output, what's the fundamental shift in
thinking required to go from basic prompting
to truly advanced prompt engineering? It's moving
from simple commands just telling it what to
do, to precise, structured, collaborative reasoning
with the AI. From making a wish to giving it
a precise recipe. I like that. Okay, our third
skill, design thinking and process mapping. Now
this is where I see a lot of agencies... stumble.
They rush straight into building. But automating
a process without truly understanding it first?
Well, that's like trying to build a bridge without
surveying the canyon, right? You might just build
it straight into a wall. You hit the nail on
the head there. There's an old saying, you know,
if you digitize a mess, you just get a digital
mess. Faster? Maybe, but it's still a mess. True
sustainable value comes from simplifying before
you even think about automating. Don't automate
complexity. Eliminate it first. So the mindset
shift here is crucial. You need to think like
a systems architect, not just a builder hammering
things together. Ask yourself those key questions.
Zoom out, map the whole process, end to end,
identify the bottlenecks, where things getting
stuck, and most critically, question every single
step. Does this step truly add value? Can it
be eliminated entirely? Can it be done differently,
maybe better? That's exactly right. You want
to view the business as this interconnected system.
Design thinking, in this context, it's the blueprint.
It ensures that what you eventually build isn't
just functional on day one, but it's stable,
it's valuable, and it actually solves the right
problem. Not just a symptom. It's about getting
that strategic clarity before you lay down any
digital foundation. For a really good mindset
shift on this. Check out UVA's Design Thinking
for Innovation course on Coursera. It's quite
good. And for actually visualizing these processes,
maybe look into business process modeling notation
or BPMN programs. There are tools for that. This
diagnostic phase, it sounds incredibly vital
for long -term success. But what's the biggest
mistake you've seen people make by not doing
that upfront work? And how can they recover if
they already skipped it? It prevents building
brittle systems that end up solving imaginary
problems or problems that don't really matter.
Right. avoiding solutions, looking for a problem.
All right, skill number four, no code automation
platforms. In, well, pretty much now, 2025 and
beyond, you genuinely don't need a full development
team to build robust operational systems anymore.
You just need to trigger something that starts
the process and a logical path for the information
or task to follow. That's basically it. It's
truly liberating, isn't it? You're essentially
connecting pre -built services, these APIs and
apps like digital Lego blocks. If you can clearly
describe a business process logically, step by
step, you can absolutely automate it using these
no -code tools. So the smarter approach then
seems to be master one primary tool really well,
like make .com or maybe Zapier, get deep with
one, focus on the key integrations that actually
matter to your clients. Usually their CRM, their
email marketing platform, things like that, and
always, always build for results. Think about
what happens if something breaks. Yeah, think
of these platforms as the central nervous system
of your automated business. Your AI skills, like
prompt engineering, that's the brain. These tools,
they're the pathways, the nerves connecting everything
and making it all fire together smoothly. top
picks that come to mind for you. Well, Make .com
is fantastic for those really complex, highly
visual workflows where you need to see everything
laid out. Zapier is amazing for its simplicity
and sheer speed, plus it has over 6 ,000 integrations
now, which is just wild. It's a powerhouse for
quick connections. And then there's N8n .io,
which is a really powerful open source option.
It offers a immense flexibility if you need more
bespoke or self -hosted solutions. I have to
admit, sometimes I still catch myself wanting
to just jump straight into Zapier before I've
fully mapped out the process properly. It's just
so tempting, right, to skip that vital diagnostic
phase we just talked about. Oh, believe me, I
still wrestle with prompt drift myself sometimes,
getting the AI to stay on track, so I totally
get it. Here's a pro tip, though. Start small.
Solve one small but high impact problem for the
client first. Deliver that undeniable win. Build
their trust. Then you can confidently expand
from there. Build bigger things. That's really
great advice. Start small, deliver value, then
scale. So how do these platforms truly empower
someone who doesn't have coding skills to build
genuinely powerful systems? They allow literally
anyone to connect powerful services visually,
snapping them together like digital Lego blocks.
Building complex systems without needing to write
code. Powerful stuff. Mid -roll sponsor read.
All right, we're back. Skill number five, no
code AI agents. This is where the conversation
really shifts, I think. It goes from just doing
tasks to delegating entire outcomes. AI agents
feel like the next big frontier. Think of them
as specialized digital workers you can design
and deploy. It really is a game changer. In a
modern AI -powered business, you design an entire
system, and then you can delegate predictable,
often complex, rule -based work to an AI agent.
This frees up your human talent for the stuff
humans are best at. Higher level strategy, creativity,
building relationships. So what's the crucial
distinction here? Because people might hear agent
and think automation. How are they different?
An automation typically follows a rigid, predefined
path, right? Step A, then step B, then step C.
An agent, though, it can perceive its environment,
it can reason based on its programming and knowledge,
and then it can act intelligently within certain
constraints. So automation moves data along a
fixed track. An agent can actually... make informed
decisions along the way. Yeah, that's a great
way to put it. And we're seeing sort of three
tiers of platforms really emerging in this space.
You've got voice flow, which is fantastic for
building really human -like voice and chat experiences,
perfect for sophisticated customer service bots.
Then there's bot press, which can handle more
complex logic and backend integrations. It's
ideal for things like advanced lead qualification
or internal process handling. And then you have
tools like Flowwise or Langflow. These are essentially
visual builders for Langchain, which is a framework
They let you design agents that can chain multiple
prompts together, access external tools, interact
with databases, really sophisticated stuff visually.
Whoa. Okay, imagine scaling that. designing an
agent that could intelligently handle, say, a
million unique customer inquiries, it paints
a very different picture of what's possible,
moving way beyond just automating a single spreadsheet
task or something. It fundamentally changes how
businesses can operate at scale, doesn't it?
Absolutely. And clients. They don't buy chat
widgets. They don't buy automations, really.
They buy outcomes. They buy problem solve. When
you develop these agent building skills, you
stop being just an implementer following instructions.
You become a true product builder, someone who
designs solutions that solve major business challenges
at scale. So thinking about that shift, what's
the key mindset change required when moving from
traditional automation thinking to embracing
these more intelligent autonomous agents? It's
shifting your thinking from simple task execution
to enabling intelligent autonomous decision making
within defined boundaries. From just following
predefined steps to enabling smart actions. Got
it. Okay. Scale number six. Knowledge systems
and RH architecture. This sounds technical, but
it's crucial. No business can truly scale effectively
if its collective knowledge is trapped, right?
Stuck in silos, scattered across random documents,
or just locked away in a few key people's heads.
In the world of AI, knowledge is the essential
fuel. It's what makes these intelligent systems
actually intelligent about your business. That's
spot on. Professionals who can design efficient
knowledge flow within an organization. They're
simply outperforming those who just rely on memorization
or searching through fragmented data. Modern
business intelligence isn't just about static
reports anymore. It's about dynamic on -demand
retrieval of exactly the right information at
the right time. And this is where we need to
demystify RG. That's retrieval augmented generation.
Can you break that down? This sounds like how
you take a general purpose AI like a big LLM
that knows about everything on the internet and
give it a specialized expert brain. tailored
specifically to a single business. Is that kind
of right? Sounds incredibly powerful. That's
exactly it. The process is actually quite elegant
when you break it down. First, you store a company's
unique knowledge think internal documents, SOPs,
past client notes, product specs in a searchable
vector database. Then when a query comes in,
say from an employee or a customer via a chatbot,
the system first retrieves the highly relevant
chunks of information from this private knowledge
base. Then it augments the original query. It
adds that specific retrieved context before sending
the combined package to the general LLM for processing.
So the AI doesn't just have to guess based on
its general training. It has precise factual
grounding from the company's own data to base
its answer on. Okay, so that means a chatbot
could instantly and accurately answer a really
specific question pulled from page 257 of a 300
-page employee handbook. Or it could give a sales
rep instant access to obscure technical details
about a product from a massive internal catalog
right when they need it on a call. Precisely.
And the three pillars supporting our egg are
pretty clear. First, you need vector databases.
Things like Pinecone are popular. Think of these
as the specialized filing cabinet that stores
information based on semantic meaning the actual
concepts, not just keywords. Second, you need
embedding models. These are the indexers that
convert all that text into numerical representations,
into vectors, making it machine readable and
comparable for meaning. And finally, you have
integration layers, often handled by frameworks
like Langchain or Llama Index. These are the
queriers or orchestrators that manage the whole
process, getting the query. finding the relevant
docs, augmenting the prompt, and talking to the
LLM. You could dive deeper into this with resources
like Pinecone's vector database fundamentals
or various rag with lane chain courses out there.
So understanding that complex process, how does
Ari truly move us beyond just getting generic,
maybe plausible sounding, but ultimately unhelpful
AI responses? It gives the general AI a bespoke
expert brain filled with a company's unique proprietary
data and knowledge. Transforming general AI into
a specific valuable expert for that business
makes sense. And that brings us to our final
skill, number seven, the business layer. This
covers packaging, pricing, and positioning. Because
mastering all the technical skills we just discussed,
that's really only half the battle, isn't it?
Truly successful AI automation strategists, the
ones building real businesses, they also excel
at communicating their value. This is so often
overlooked, but it's critical. Let's start with
packaging. Stop selling hours. Just stop. Seriously.
Productize your services. Turn them into clear,
repeatable, outcome -focused solutions with defined
deliverables. Think names like Client Onboarding
Accelerator or Content Repurposing Engine or
24 -7 AI -powered Support Agent. This transforms
you from just a generic service provider trading
time for money into a specialist solving a very
specific valuable problem. It makes your value
immediately clear. OK, productizing, not selling
hours. Then pricing. Yeah. You mentioned anchoring
on value and ROI, not just the effort involved.
Can you expand on that? Like if your system saved
the client 20 hours a week or maybe boosted their
sales conversion rate by 15 percent, how do you
price based on that outcome? Exactly. You quantify
the impact. What is that saved time worth? What
is that conversion boost worth in revenue? Your
price should reflect a fraction of that created
value. It justifies a much higher price than
hourly billing ever could. And also, consider
retainers. Offering ongoing support, monitoring,
and optimization for a recurring fee provides
predictable revenue for you and ongoing value
for the client. Makes sense. Value -based pricing
and retainers. And then, positioning. Yeah, positioning.
Look, the AI automation field is growing fast.
It's getting crowded. So you need to specialize.
Become the absolute hashtag one go -to AI automation
agency for, say, boutique law firms, or for direct
to consumer e -commerce brands, or for independent
real estate brokerages. Pick a lane. This isn't
just about finding a small niche. It's about
building deep, recognized expertise in a powerful
reputation within a specific industry. That allows
you to command premium fees and generate predictable,
high -quality referrals. You become the known
expert. Okay, let's quickly recap that strategic
action plan then. It's a focused strategy built
on these compounding high leverage capabilities.
It includes generative AI fundamentals, advanced
prompt engineering, design thinking and process
mapping first, then no code automation platforms,
no code AI agents, building knowledge systems
with ARRIG architecture, and finally layering
on that crucial business layer, packaging, pricing,
and positioning. That's the stack. That's the
stack. And you really can start monetizing this
skill set surprisingly quickly, maybe even in
a matter of weeks if you focus. Clients pay for
outcomes, they pay for transformation, they pay
for problem solved. When you can reliably deliver
results that maybe once needed an entire team,
or perhaps simply weren't even possible before
AI, you become indispensable, truly valuable.
So how does adding this business layer, that
final piece, make someone truly indispensable
beyond just being a skilled technical builder?
It shifts their entire focus and the client's
perception from just building tools to delivering
transformative, measurable business outcomes
for the client. Turning technical expertise into
indispensable business value. Okay, wow. This
deep dive has really shown us, I think, that
success in AI automation isn't primarily about
writing code. That's not the barrier anymore.
It's fundamentally about strategy, about clarity,
and about understanding these core capabilities.
And that gap, that crucial divide between those
who are just kind of interested in AI, maybe
playing around with tools, and those who are
actually building and delivering real value with
it. That gap is defined precisely by mastering
these core skills we've discussed today. It's
about making that leap from being a mere implementer
to becoming a true architect of solutions. The
opportunity is immense. It feels truly boundless
right now for those who are prepared who build
these skills. Businesses out there, they desperately
need strategic partners. They don't just need
more tool jockeys, right? They need people who
can understand their challenges and craft intelligent
automation solutions that drive real measurable
results. So reflect on which of these seven skills
resonates most strongly with you right now. Where
could you start? How might you begin building
your own blueprint for success in this space?
Perhaps it's diving deeper into prompt engineering
first, or maybe it's finally sitting down to
map out that specific business process you know
is right for all. automation. The AI revolution
is happening now. It's not coming. It's here.
The question isn't really whether you should
get involved. It's whether you'll be ready, equipped
with the skills to lead. Thank you so much for
joining us on this deep dive. We'll see you next
time. Out to your music.