I help companies unlock the real ROI of AI.

As Founder of Boom Automations, I work with small, mid-market businesses, and investment firms to streamline operations, build AI agents, and implement automations that reduce costs, increase efficiency, and create a single source of truth across teams.

Alongside implementation, I run AiAllstars-a hands-on bootcamp and community where business leaders and operators learn how to apply AI and automation frameworks directly inside their organizations. In just 7 days, participants go from ideas to building live automations that save hours each week, with the confidence and skills to keep scaling their impact.

If you’re looking to implement AI systems in your business or to train your team to do it themselves-you’re in the right place.

Automation consultants charge $15K for what Claude Code now does in 2 hours.

I know because we're the ones who used to charge it.

Here's the exact process:

Step 1: Discovery (20 min) 
→ Paste your org chart, tool stack, and top 3 bottlenecks 
→ Claude interviews you with clarifying questions 
→ Outputs a full process inventory ranked by time cost

Step 2: Workflow Mapping (15 min) 
→ Describe any department's daily operations in plain English 
→ Claude builds a complete process map 
→ Every manual handoff, redundant step, and automation trigger flagged

Step 3: Opportunity Audit (10 min) 
→ Feed it the workflow map output 
→ Returns your top 10 automation opportunities 
→ Ranked by ROI, complexity, and build time

Step 4: Architecture Design (20 min) 
→ Claude designs the full system architecture 
→ Which tools connect where, what the data flow looks like 
→ Agents for complex logic, linear flows for the repetitive stuff

Step 5: Build (ongoing) 
→ Claude writes the actual workflow JSON 
→ Self-documents everything as it builds

Step 6: The output.

A live dashboard your whole team can work from.

→ Clickable process maps for every department 
→ Automation opportunities ranked by ROI 
→ Implementation progress by phase 
→ KPIs updated in real time 
→ One link you share with clients, freelancers, or your team to execute

This is what we hand every client at the end of discovery.

The .md file is what makes all of it possible.

Without it, Claude guesses.

With it, Claude builds like a $15K consultant.

Comment "BLUEPRINT" and I'll send you the full prompt stack and the .md file we use internally. (Make sure we are connected so I can message you)

🎁 Bonus: The first 100 people get a real Precision AI Blueprint — an actual sample audit doc from a client engagement so you can see exactly what the output looks like.

Luke Pierce

I started my AI automation agency on Upwork taking $500 contracts.

No team or process, and no idea how to price, scope, or deliver without burning myself out.

3 years and 80+ companies later, Boom Automations is signing 2-3 new clients per month and running six-figure builds with a full team behind every project.

And everything I learned between those two points is in this document.

But lately I've noticed something that's been bothering me.

People who have never delivered a single AI project are selling courses on how to build an agency around it.

No clients, case studies, and no fulfillment process. Legitimately just a landing page and a price tag.

So instead of just being frustrated about it, I made this guide:

→ The discovery call structure we run on every deal
→ How we price based on ROI delivered, not hours worked
→ Our fulfillment engine from onboarding through handoff
→ The "Company Brain" architecture we build for every client
→ How we hire, vet, and manage developers daily
→ Real pricing benchmarks from small projects to six-figure builds
→ The scoping process that keeps delivery on track in 8-12 weeks

It's just the actual operating system from someone still in the trenches doing the work every day.

Like this post and drop "SYSTEM" below and I'll send it over. (IMPORTANT: make sure we are connected so I can message you)

First 100 people also get our exact proposal and scoping template, the same document we use to close and deliver six-figure projects without scope creep.

Want to know why some companies crush it with AI while others waste thousands?


It comes down to where they start.

Every company I've worked with that got real ROI from AI had one thing in common.

They started with their processes.

After working with dozens of businesses on AI implementations, I've found there's a clear stack that separates the ones who get results from the ones who waste budget.

We call it the AI Implementation Stack.

From bottom to top:

1⃣ Process Foundation 
→ Documented workflows 
→ Clear ownership at every step 
→ Optimized before any tech touches it

2⃣ Data Layer 
→ Single source of truth 
→ Clean, connected, accessible 
→ No more copy-pasting between 6 different tools

3⃣ Integration Layer 
→ Systems that actually talk to each other 
→ Automated data flow between platforms 
→ Zero manual transfers

4⃣ Automation Layer 
→ Repetitive tasks handled by workflows 
→ Triggers, rules, and logic doing the heavy lifting 
→ Your team focuses on decisions, not data entry

5⃣ AI Layer 
→ Intelligent agents solving complex problems 
→ AI that actually has context because the layers below feed it clean data 
→ This is where the magic happens

The companies trying to start at Layer 5 without the foundation? That's where budgets get burned.

The ones building from the bottom up? They're the ones turning AI into a real competitive advantage.

Every layer compounds on the one below it. Get the foundation right and the AI practically builds itself.

Make sure to save this post for your next Ai project.

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The smartest companies I work with all prioritize AI the same way.


They don't automate everything at once.

They use a simple framework to decide what gets built first.

We call it the Automation ROI Matrix.

It maps every process across 2 dimensions:

→ Impact (how much time/money does this save?) 
→ Effort (how hard is it to automate?)

That gives you 4 quadrants:

1⃣ High Impact, Low Effort → Automate NOW. 

These are your quick wins. Data entry, client onboarding, internal notifications. Fast to build, massive time savings. Start here every single time.

2⃣ High Impact, High Effort → Plan & Build. 

Custom dashboards, AI agents, full operating systems. Big ROI but requires proper scoping and development. Worth the investment, but not where you start.

3⃣ Low Impact, Low Effort → Automate Later. 

Nice-to-haves. Calendar reminders, simple email templates. Easy to do but won't move the needle. Backlog these.

4⃣ Low Impact, High Effort → Skip It. 

This is the trap. Complex automations that sound impressive but barely save any time. Most wasted budgets live here.

The best operators start in quadrant 1, build momentum, then graduate to quadrant 2.

That's how you get AI to pay for itself.

Save this matrix before your next AI project.

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If you're trying to figure out how to implement AI in your business, the best first step you can take is mapping your processes.

Most founders and team leaders skip this and go straight to the tools. 

But that's how you end up automating broken workflows and spending twice the money fixing things later.

When you sit down and map it out step by step, the opportunities practically jump off the page.

→ Redundant handoffs no one questioned. 
→ Data getting entered 3 times across 3 tools. 
→ Bottlenecks hiding in plain sight. 
→ Steps that exist "because we've always done it that way."

Process mapping isn't sexy. It's definitely not trending on Twitter/X.

But it's the single most valuable thing you can do before touching any automation or AI tool.

And once you've mapped individual processes, the next level is building what I call a "Company Brain."

It's a single document that shows how ALL of your processes connect and flow together across the entire business.

Instead of looking at things in isolation, you see the full picture. How sales feeds into onboarding, how onboarding connects to fulfillment, how fulfillment ties back to retention.

That's when you stop guessing where AI fits and start seeing it clear as day.

I just dropped a quick step-by-step tutorial on YouTube breaking down how to do both. 

Process mapping and building your Company Brain from scratch.

Watch it here: https://lnkd.in/eqPD7ciH

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Founder @ Boom Automations. We implement Ai into your biz in 6 weeks or less

Founder @ Boom Automations & AiAllstars

Eleanor K.

BREAKING: OpenClaw creator Peter Steinberger is joining OpenAI.


If you've been anywhere near X the last few weeks, you've seen OpenClaw dominate the conversation. An Austrian developer "vibe coded" a personal AI agent that went from a playground project to 180,000+ GitHub stars in record time. It managed calendars, booked flights, handled emails, not through a fancy new app, but through tools people ALREADY use like WhatsApp, Telegram, and iMessage.

Both Sam Altman and Mark Zuckerberg made offers. We're talking billion-dollar territory for what was essentially a one-man operation that was actually LOSING $10-20k/month. Let that sink in.

Steinberger chose OpenAI. He'll lead their next generation of personal agents. OpenClaw stays open source under a foundation.

What makes this interesting is that Steinberger himself predicted that OpenClaw-style agents will eliminate 80% of current apps. His reasoning is that every app is just a slow API now. Why open 5 different tools when your agent can handle it all through a single conversation?

This is similar to what I've been saying about building smarter operating systems. The future isn't adding more apps to fill specific gaps in your business. It's fewer interfaces doing more work. Agents that live inside your existing workflows and meet your team where they already work.

Now my question for you... is this just another piece of AI noise that fades in 6 months? Or does OpenAI absorbing OpenClaw actually shift the landscape for how we all interact with AI agents moving forward?

Drop your take below.

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If your AI project takes longer than 8 weeks to launch, something's wrong.


I've seen companies spend 6+ months "planning" an AI rollout. Endless meetings. Scope changes every week. A dev team building in the dark.

And at the end of it, it's something nobody uses.

The best AI projects move fast because the thinking happens upfront, not during development.

We deliver custom AI-native systems in 8-12 weeks. Discovery to production. 

And the reason it works isn't because we rush. It's because we front-load the hard stuff.

Weeks 1-2 are discovery. We map your processes, interview your team, and document everything before a single line of code gets written. Problem Statement. Current State Map. User Stories. Data Requirements. Success Metrics. All of it locked in before we build.

Weeks 3-6 are build and iterate. We develop in sprints, not waterfalls. Your team sees working features every week. Feedback happens in real time, not at the end when it's too late to change anything.

Weeks 7-8 are production and adoption. We don't just hand you a login and disappear. We deploy into your actual workflows, train your team, and make sure people are using it daily.

The projects that take 6 months skip the documentation. They start building before anyone agrees on what "done" looks like. Then they spend months fixing misalignment that should've been caught in week one.

Speed is about removing confusion before it has a chance to slow you down.

If you're sitting on an AI project that's been "in progress" for months, the problem isn't the technology. It's the process around it.

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We just wrapped another 8-week build for a client.


They went from chaos to clarity in less time than most companies spend "evaluating tools."

And it got me thinking about why speed matters so much in AI projects.

It's not about rushing. It's the opposite actually. If it needs to take more time, sure, it will take more time. But why should it?

Speed comes from doing the thinking UPFRONT instead of figuring it out mid-build.

That's the whole game, even though things do change sometimes during the demo process.

So I broke down our exact 8-week process for building custom AI-native operating systems.

Week 1-2 → We obsess over discovery. Interview the team. Map every workflow. Find where time and money are actually leaking.

Week 3 → We lock in the blueprint. Problem statement. User stories. Data requirements. Success metrics. One document that keeps everything on track.

Week 4-6 → We build the core system. Single database. AI agents. Automated workflows. All connected to the tools you already use OR replace the old tools inside the system.

Week 7-8 → We deploy WITH your team. Real data testing. Hands-on training. Feedback loops from day one.

8 weeks later the entire operation looks different.

Your team actually uses it because they helped build it. They had a direct hand in it's development.

Swipe through the carousel for the full breakdown of each phase.

Follow me Luke Pierce for more on AI implementation and scaling operations.

8 Weeks

The smartest companies I work with all have one thing in common.


They own their operating system.

Most don't. Most are running their business across 10+ SaaS tools that barely talk to each other. Slack for comms. Asana for projects. HubSpot for sales. Notion for docs. QuickBooks for finance.

And none of it is connected.

So what happens? Your team becomes the integration layer. They're copying data between apps, manually updating statuses, and spending half their day just keeping systems in sync instead of doing actual work.

When you build a custom internal operating system, everything changes. Your data lives in one place. Your workflows are connected end to end. And most importantly, you can actually measure what's happening across your entire business in real time.

Not vanity metrics from 6 different dashboards that all tell a different story. Real operational data that shows you exactly where time is being wasted, where deals are getting stuck, and where your team is hitting walls.

We built this for a client doing $8M in revenue last quarter. They had 12 different tools running their operations. Nobody could tell me how long it actually took to onboard a new client or what their real cost per project was. Not because they didn't care, but because the data was scattered across a dozen platforms that were never designed to work together.

Within 60 days we replaced 7 of those tools with one system they fully own. Now they can see client profitability in real time, track project delivery speed down to the hour, and make decisions based on actual data instead of gut feelings.

Once you own your operating system, you control the roadmap. You stop waiting on SaaS companies to build the features you need. You stop duct-taping Zapier connections between tools that were never meant to integrate. And you stop paying $4K/month in subscriptions for software that does 20% of what you actually need.

Build the system around your business. Not the other way around.

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If I walked into your company today, here's the FIRST thing I'd automate with Ai.

(Based on your revenue stage)

It depends on where you are.

Because a $500K company and a $10M company have completely different bottlenecks.

I've built AI systems across dozens of companies at every stage.

Here's the playbook:

💰 $500K - $1M

→ Client onboarding.

Your founder is still doing everything manually. Contracts, welcome emails, account setup, follow-ups.

Automate the intake. One form triggers everything.

This alone saves 20+ hrs/mo.

💰 $1M - $3M

→ Data flow between tools.

You've got a CRM, a project tool, spreadsheets, and an invoicing app. None of them talk to each other.

Your team is copy-pasting data between 4-5 tools every single day.

Connect everything. Zero manual data entry.

💰 $3M - $5M

→ Internal reporting and visibility.

At this stage you're flying blind. Revenue is growing but nobody can tell you exactly where time and money are going.

Build automated dashboards that pull real-time data from your systems. No more "can you pull that report?" Slack messages.

💰 $5M - $10M

→ Employee onboarding and SOPs.

You're hiring fast. But every new person takes 2-3 weeks to get productive because training is scattered across Google Docs nobody can find.

Systematize it. Auto-deliver training, auto-assign projects, auto-provision access. Day 1 productivity.

💰 $10M+

→ Your entire operating system.

Stop duct-taping 15 SaaS tools together. Build a custom AI-native operating system that runs your business from one place.

This is where the real competitive advantage lives.

Overall:

Early stage → Automate founder time. 
Growth stage → Automate team friction. 
Scale stage → Automate the whole machine.

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DM me for a free Ai consult.

The AI hype from this summer has finally settled, and now it's pretty clear where the real value is.


Every company I talk to right now has the same problem. Sales is in one platform. Operations is in another. Finance in a third. HR in a fourth. And leadership is manually checking multiple systems just to get a clear picture of what's actually happening across the business.

Each department picked the tool that worked best for them. Which made sense at the time. But now no one can see across the company without a bunch of exports, copy-paste, and spreadsheets duct taped together.

This is exactly why MCP became a thing. It's the new duct tape for disconnected workflows. And while it's useful, it's a band-aid on a bigger problem. You shouldn't need a protocol to connect everything after the fact. 

You should just have everything connected from the start.

The highest value work we're doing for clients right now is building custom operating systems that replace 3 to 5 disconnected platforms with one unified system. All the data for every department in one place.

And once that foundation exists, everything else becomes possible. A chatbot that can access all of your company's knowledge in real time. AI agents you can summon with one click to handle specific tasks. Documents that generate themselves from real data instead of someone spending hours putting them together manually. Reports that don't require pulling from 6 different tools and hoping nothing got missed.

But none of that works if your data is fragmented across a dozen platforms that don't talk to each other.

The order matters.

1. Get all of your data in one place.
2. Build your workflows on top of that.
3. Then layer AI on top of everything.

Most companies are trying to skip straight to step 3 and wondering why nothing sticks.

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On Sunday I posted about the AI Implementation Stack.

The 5 layers every AI-native business needs to get right.

It got a lot of engagement, so I decided to turn it into a full detailed mind map.

Every layer broken down with the exact sub-steps, tools, and principles we use with clients.

Here's the quick recap:

1⃣ Process Foundation → Map workflows, eliminate bottlenecks, optimize manually first

2⃣ Data Layer → Single source of truth, clean data, zero duplicate entry

3⃣ Integration Layer → Systems that talk to each other, automated data flow

4⃣ Automation Layer → Workflows handling the repetitive tasks, your team focuses on decisions

5⃣ AI Layer → Intelligent agents with real context, solving complex problems

The mind map goes deep into every one of these.

What to build first, what to avoid, and how to implement it in 8 weeks.

Save this post, download the mind map, and reference it before your next AI project.

Every layer compounds on the one below it. Get the foundation right and the AI practically builds itself.

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We saved a client $50K last month.


Not by building something.

By telling them NOT to build it.

They came to us wanting a fully custom operating system.

CRM, project management, client portal, automated reporting, AI-driven dashboards…

The whole nine yards.

Sounds great on paper right?

But when we started our discovery process, we found something interesting.

Their team was using 5 different SaaS tools.

None of them were connected.

And nobody had a clear process for ANYTHING.

Data was getting entered 3 times.

Tasks were falling through the cracks.

And their "project management" was a shared Google Doc with highlights.

So instead of building them a $50K+ custom platform…

We mapped every process from scratch.

Found the 3 biggest bottlenecks stealing their time.

And built 3 targeted automations that solved 80% of their problems.

Total cost was under $10K.

The best automation companies don't just build what you ask for.

They challenge what you think you need.

Because most of the time, the problem isn't a lack of technology.

It's a lack of clarity on your own processes.

We could've taken that $50K.

Built something impressive.

And they probably would've been happy… for a while.

Until nobody used it.

Until the processes it was built on top of were still broken.

And they'd be right back where they started.

Don't build on a broken foundation.

Map it. Fix it. THEN automate it.

Every time.

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The businesses that will dominate the next 5 years are rebuilding from scratch with AI as the foundation. 


There's a massive difference between adding AI to your workflow and building your workflow around AI.

There's really only one thing that determines whether your AI project actually gets used.


The level of involvement your team had while it was being built.

I've seen this play out dozens of times. The projects where the team is in the room during development, watching features come together, asking questions, shaping how things work... THOSE tools get used every single day.

The projects where someone on the leadership team signs off and the team sees the finished product for the first time on launch day? Nobody touches it.

We run demo meetings as frequently as every single day during active builds. Sometimes we're showing something brand new every 24 hours. That frequency does something powerful. It turns us from an outside vendor into an extension of the team. People start saying "can we make it do this instead" or "what if we added this." That's when you know adoption is locked in.

When your team watches something being built from scratch, they understand how it works. They trust it. And most importantly, they feel like they had a hand in creating it (which, they did).

If you're building AI tools in-house for your team, build the process around visibility. Give people a seat at the table during development. Let them ask questions, push back, and influence the outcome.

The most adopted tools are the ones the team helped build.

Follow me Luke Pierce for more on building AI systems that actually get used.

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