AI Amplifies Whatever System You Have, Are Your Systems Ready ?

Here's the uncomfortable truth: Your developers feel 20% more productive with AI. They're actually 19% slower.

That's not speculation, it's from METR's 2025 study of experienced developers using Claude and Cursor on real open-source projects. Developers were convinced AI accelerated their work. Screen recordings proved otherwise. They spent more time reviewing AI-generated code, prompting multiple times, and eventually writing it themselves anyway.

Carnegie Mellon tracked 807 GitHub projects using Cursor and found initial code output jumped 281%. Within three months, it crashed back to normal. What stuck? A 9% increase in bugs per developer and pull requests 154% larger. Faros AI's analysis of 10,000+ developers confirmed it: 75% use AI tools, zero measurable organizational improvement.

But here's what should terrify you: DORA's 2025 research of 5,000 technology professionals found that without the right foundations, AI doesn't just fail to help—it actively hurts team performance. Not "less benefit." Actual harm.

Why Your Productivity Numbers Are Lying

AI is an amplifier. It magnifies whatever system you already have. Strong development process? AI makes it stronger. Broken workflow with downstream bottlenecks? AI floods that bottleneck with 281% more half-working code, overwhelming your review process and QA team.

Individuals generate more code and feel productive. Your organization sees increased instability, more bugs, and delivery friction that didn't decrease at all. The gap between what people claim and what actually ships is where your money disappears.

The One Thing That Actually Works

Before you accelerate anything with AI, you need to know where work gets stuck. XYZ Company, a telecom firm, mapped their software development lifecycle and found their delivery time ballooning to 210.5 days. Value stream mapping revealed the bottlenecks weren't where leadership assumed—they were in handoffs, waiting for approvals, and rework loops.

After redesigning the process based on what the map revealed, delivery time dropped to 137.5 days. That's 34.7% faster without any new tools. Just fixing the plumbing before turning on the fire hose.

DORA's research identified seven capabilities that determine whether AI helps or hurts. The difference maker? Quality internal platforms. With strong platforms, AI amplified benefits. Without them, it amplified chaos. Organizations seeing actual ROI from AI didn't start with better prompts—they started with better systems.

What This Means Monday Morning

Stop buying more AI licenses. Start with a two-hour workshop. Pick one workflow, feature development, bug fixes, customer onboarding, and map it. Every handoff. Every approval. Every place work waits.

You'll find your constraint within an hour. It's probably not where you think. Then ask: if we 3x the input to this step with AI, what breaks downstream? That's your answer.

Organizations waste money optimizing local productivity while organizational performance stays flat. DORA proved it with 5,000 developers. Your metrics prove it too. Your production metrics are probably proving it right now.

Fix your system first. Then amplify it with AI. The alternative is paying for productivity theater while delivery metrics get worse.

The Secret to Agents That Actually Work? It's Not the AI

Here's why most agent projects crater: nobody mapped the real workflow first. A team decides to automate customer onboarding. They brief a developer, buy a platform, spend three months building. The agent handles maybe 30% of cases, breaks constantly, creates more cleanup work than it saves. Everyone blames the technology.

Wrong diagnosis. The problem was simpler, nobody actually documented what happens during onboarding before trying to automate it.

The Two-Hour Map

Successful implementations start with a room and the people who do the actual work. Not their managers. The folks in the trenches.

Ask them to walk through one real example from last week. Not the textbook case, a messy Tuesday example with all the annoying bits included.

Write every step. "Customer emails request" isn't a step. The real steps: Email hits shared inbox. Sarah checks if they're existing or new. Existing? She pulls their file from the shared drive, reviews history. New? Welcome template gets sent, folder gets created.

Capture the decisions: Why does Sarah route this one to John? What information does she need that's not in the email? When does she escalate?

Get the exceptions: Incomplete customer file? Urgent request? Email arrives Friday at 4:58 PM?

This is where the magic happens. "Customer onboarding" isn't one thing—it's 47 micro-decisions, information scattered across five systems, and judgment calls Sarah makes from experience nobody's written down.

Why This Changes Everything

Now you can actually design something that works. You know which steps are rule-based (agent territory) versus judgment calls (human territory). You know which systems need connecting. You see the edge cases before they break production.

Better: you can scope intelligently. Maybe the agent handles steps 1-4, Sarah makes the judgment call at step 5, agent finishes steps 6-10. That's still massive value—and it's something you can actually ship.

The Real Discovery

Sometimes the mapping session reveals the process itself is broken. No agent will fix a fundamentally flawed workflow.

I've seen this pattern repeatedly: teams start mapping their process for an agent, realize halfway through that their current workflow makes no sense, and end up redesigning the process before building anything. The agent project becomes a forcing function for fixing broken operations.

That's the actual value. The best agent implementations don't automate your current process—they automate the process you discover you should have been running all along.

The two-hour mapping session isn't prep work. It's the work. Everything else is just execution.

The Action Item

Pick one workflow causing pain right now. Get the people who actually run it in a room for two hours this week. Map what really happens, not what the procedure manual says. You'll either find your agent blueprint or discover you need to fix the process first.

Either way, you're further ahead than the teams building agents blind.

The Three Reality Checks for Building an AI-First Organization

A mid-sized insurance company spent six months building an AI-powered claims processing system. Engineering prototyped it in three weeks. Production deployment? Four months late, $2M over budget, and it processed 30% of claims before requiring human review—twice the error rate of their legacy system.

The problem wasn't the AI. It was assuming traditional organizational infrastructure could handle AI velocity.

If you're transforming your organization to build AI products or features, here are the three gaps that will cost you time, money, and market position—and how to actually address them.

Challenge 1: Speed vs. Guardrails Creates Hidden Costs

AI-assisted development lets your engineers ship features 3-5x faster than traditional coding. Your governance processes weren't built for this velocity.

Here's what happens: Teams either bypass security reviews and compliance checks (creating regulatory exposure that can cost millions in healthcare or financial services), or they slow AI development to fit quarterly approval cycles (losing competitive advantage while faster-moving competitors ship).

The real cost isn't just delayed features. It's the technical debt accumulated when teams rush to production, the security incidents that erode customer trust, or the market opportunities lost waiting for legacy processes to complete.

Who owns this? Not just engineering. Your Chief Risk Officer, Chief Product Officer, and Chief Technology Officer need to redesign governance together. You can't delegate this to IT.

What works: Automated guardrails that move at AI speed. Real-time security scanning for AI-generated code. Lightweight compliance checkpoints for regulated industries—daily reviews instead of quarterly gates. One financial services firm cut approval time from 12 weeks to 3 days by automating 80% of their security review process specifically for AI features.

Challenge 2: The Prototype-to-Production Gap Destroys Economics

Your team demos a working AI feature in two weeks. Leadership approves. Then reality: production deployment takes six months and costs 10x the prototype budget.

Why? Prototypes don't include error handling, cost modeling, monitoring, fallback systems, or load testing. That AI chatbot that costs $0.15 per conversation in testing? It costs $4.50 in production at scale. Your engineers built for "it works" not "it works reliably at 100,000 users under various failure conditions."

Organizations typically underestimate production-readiness time by 3-5x and costs by 5-10x. This gap kills ROI on AI investments and creates a backlog of half-finished features draining resources.

What works: Mandate production-readiness checklists before prototyping begins. Cost modeling, error handling strategy, monitoring plan, and fallback approaches are non-negotiable from day one. Build reusable infrastructure—authentication, rate limiting, monitoring—so teams aren't rebuilding basics for each AI feature.

Challenge 3: Your Data Infrastructure Isn't Ready (And It Takes Longer Than You Think)

Here's the hard truth: If your data is scattered across legacy systems, inconsistently formatted, or poorly documented, your AI transformation will fail. AI doesn't work around messy data—it amplifies the problems at scale.

The insurance company's claims AI struggled because 20 years of claims data had inconsistent codes, missing fields, and undocumented business rules. Cleaning that data took 8 months—longer than building the AI system itself.

For regulated industries, this is compounded by data governance requirements. HIPAA, SOC 2, GDPR compliance can't be retrofitted after you've already built AI features on problematic data foundations.

How do you know if you're ready? Ask: Can our teams access the data they need within days, not months? Is our data quality documented and monitored? Do we have governance processes that allow AI development without creating compliance risk?

If you're 3+ years behind on data infrastructure, expect 12-18 months minimum to establish AI-ready foundations. There's no shortcut.

What works: Start now. Data infrastructure investment must precede AI feature development. Assign clear ownership—usually Chief Data Officer or VP of Engineering—with executive sponsorship and budget. Track data accessibility and quality as key metrics.

Your Next Move

Tuesday morning: Assess which of these three gaps poses the biggest risk to your organization. Assign executive ownership. Set a 30-day deadline to report back with specific plans, timelines, and budget requirements.

Going AI-first requires rebuilding your organization's operating model. The question isn't whether to invest—it's whether you'll invest strategically or pay the cost in failed projects and missed opportunities.

Which AI Should YOU Actually Pay For?

I switched from ChatGPT to Claude last month. Better writing support, blog updates, and access to Claude Code for development work when I need it. Multiple reasons, one decision.

The "best" AI depends on what you actually do all day. Here's the breakdown by task, as of January 2026. These tools evolve monthly, so strengths shift, but the decision framework stays the same.

By What You Actually Do

• Sorting Data & Working with Excel/Google Sheets

Winner: OpenAI (ChatGPT)

ChatGPT writes Excel formulas, explains what's broken, and helps sort messy data. "I have 500 rows of customer data with inconsistent date formats" → ChatGPT walks you through cleanup step by step.

Beyond formulas, it excels at structured thinking—building financial models, thinking through scenarios, identifying what variables matter, working through ambiguous business problems to clear decisions. This thought partnership is where ChatGPT shines.

Gemini works directly in Google Sheets from the sidebar, but ChatGPT's formula logic and structured reasoning are superior.

• Writing Emails & Memos

Winner: Tie - OpenAI or Claude

Both excel at professional business writing. ChatGPT leans crisp and efficient. Claude is more nuanced. For most work, you won't notice a difference.

• Connecting to Your Email & Learning Your Tone

Winner: Gemini

Gemini sits in your Gmail sidebar, reads your email style, and drafts responses that sound like you. Say "draft a reply confirming the meeting" and it pulls context from the thread.

ChatGPT and Claude require copy-pasting. That gets old fast.

Trade-off: Gemini's integration is brilliant, but reasoning for complex topics is weaker.

• Analyzing Meeting Recordings & Videos

Winner: Gemini

Gemini watches and listens to videos natively—no transcript needed. Useful for catching up on meetings or analyzing public competitor demos. ChatGPT and Claude need transcripts first.

Note: Follow company policies on confidential recordings.

• Researching & Source Audit Trail

Winner: Claude (with web search)

Claude searches the web, cites specific sources you can verify, and synthesizes across multiple pages of research. Ask "what are the three main disagreements across these sources?" and it connects the dots.

What sets it apart: honesty. If sources conflict or something's uncertain, Claude tells you rather than confidently making up citations or smoothing over contradictions. For research where accuracy matters, this carefulness is the feature.

The Claude 4.5 family has significantly improved reasoning quality for complex synthesis.

• Creating Outlines for Decks

Winner: Claude (Artifacts feature)

Claude's Artifacts feature builds an actual outline you can edit in real-time and export. It becomes a working document, not just chat text.

ChatGPT gives you text to copy-paste. Claude gives you a structure you manipulate directly.

• Creating Images for Presentations & Marketing

Winner: Gemini (Nano Banana model)

Gemini's Nano Banana model keeps subjects perfectly consistent. Upload your product bottle, and it generates that exact bottle in different settings—office, outdoor, retail shelf—maintaining brand consistency.

You can edit existing images with prompts: "change the background to a sunny office." It edits pixels rather than regenerating from scratch.

For branded, consistent visuals across slides, Gemini wins. For generic illustrations, ChatGPT's DALL-E is fine.

• Working on Big Multi-Part Projects

Winner: Claude

Extended context is the game-changer. Claude handles multiple documents simultaneously—presentations, notes, research—and maintains context across all of them.

I've synthesized months of meeting notes, strategy documents, and research papers into coherent recommendations. ChatGPT loses track with that volume. Gemini struggles with complex synthesis across sources.

Important: Be mindful of what you share. Check your organization's AI usage policies for sensitive data.

So What Should You Actually Do?

What's the one task that, if it took 50% less time, would change your week?

• Spreadsheet work & structured decision-making → ChatGPT ($20/month)

• Email management in Gmail → Gemini (free with Workspace or $20/month)

• Research and multi-document projects → Claude ($20/month)

• Analyzing videos or creating branded images → Gemini ($20/month)

• Mix of everything → ChatGPT (most versatile) or Claude (complex documents)

The 30-Day Reality Check

Pick one. Use it daily for 30 days on actual work. Track: "How much time did I save on [specific task]?"

If you're not saving 3+ hours per week, you either picked wrong or haven't built the habit.

My switching decision wasn't complicated, I evaluated what filled my calendar (writing, document analysis, development work) and matched it to Claude's strengths. Your decision should be just as straightforward: what task takes up most of your time?

These tools evolve fast. What's true in January 2026 might shift by March. Reassess every few months as your needs and the platforms change.

The goal isn't picking the "best" AI. It's solving your specific time-sink problem.

What's the task that kills your productivity every week? That's your answer.

When to Orchestrate vs When to Code: A Framework from the Trenches

Last week I presented to Publicis Sapient's strategy group on my recent work with n8n workflow automation. I walked them through building AI agents, chatbots, web scrapers, RAG systems, all without writing code. The question that dominated the Q&A wasn't about the tech. It was strategic: "When do you orchestrate versus when do you code?"

That's the real question. Not whether workflow automation works (it does), but when it's the right tool versus writing actual code.

The Answer Nobody Wants to Hear

There's no universal rule. But after building GreenBookAI with LangGraph and Python, then rebuilding similar patterns in n8n, I've developed a framework that works.

The decision isn't technical. It's about constraints, control, and what you're optimizing for.

When to Orchestrate: The Three-Factor Test

Use workflow automation when you can answer yes to at least two of these:

1. Speed to market matters more than perfect control

If you need to validate an idea this week, orchestrate. I built a functional RAG agent in n8n in 20 minutes. The equivalent in LangChain would take days—API setup, vector database configuration, deployment infrastructure, error handling.

Orchestration trades precision for velocity. That's a great trade when you're testing hypotheses or need a prototype yesterday.

2. The problem has existing integrations

If your workflow connects Slack + Google Sheets + OpenAI + Salesforce, orchestration wins. These platforms have 400+ pre-built connectors. Writing custom API clients for each service is engineering overhead you don't need.

The web scraper I built? One HTTP Request tool, one AI Agent node. In code, that's BeautifulSoup configuration, rate limiting logic, error handling for failed requests, retry mechanisms. n8n handles it.

3. Non-technical stakeholders need to own it

If business users will iterate on the logic after you build it, orchestrate. Visual workflows are editable by people who can't code. Python scripts aren't.

This matters more than engineers think. Half of AI adoption is about who can modify systems when requirements change. If that answer is "only the engineering team," you've created a dependency bottleneck.

When to Code: The Control Checklist

Write code when you need:

1. Custom logic that doesn't exist in pre-built nodes

My GreenBookAI travel agent required complex state management across multiple API calls, custom scoring algorithms, and sophisticated data transformations. n8n couldn't handle it. LangGraph could.

If your workflow requires "when X happens, do Y, unless Z occurred in the last 3 interactions, then do W but only if conditions A and B are met"—you need code.

2. Performance optimization at scale

Orchestration platforms add latency. Every node execution is an API call. If you're processing 10,000 requests per second or need sub-100ms response times, code gives you control over execution.

For my web scraper running once an hour? The 200ms overhead doesn't matter. For real-time trading decisions? It's unusable.

3. Proprietary IP or algorithmic differentiation

If your competitive advantage IS the algorithm, don't build it in a visual platform. Code gives you the black box. Workflow tools are transparent by design.

4. Deep integration with existing codebases

If you're extending a Python microservices architecture, adding a workflow automation tool creates system fragmentation. Keep the tech stack coherent.

The Hybrid Reality

Here's what I actually do: prototype in n8n, productionize in code when needed.

I built a lead generation system in n8n in an afternoon. It scraped websites, enriched data with AI, scored leads, updated Google Sheets. Good enough to validate the approach.

When we needed 10x throughput and custom attribution modeling? I can rewrite it in Python. But n8n gave me the proof of concept that justified the engineering investment.

The Real Skill

The skill isn't mastering n8n or becoming a better Python developer. It's pattern recognition—knowing which tool gives you the best time-to-value for the problem at hand.

Most engineers over-index on code because it's what they know. Most business people over-index on no-code because they want autonomy. The answer is situational.

Ask yourself: what am I optimizing for? Speed, control, scalability, maintainability, stakeholder ownership? That tells you which tool to reach for.

The future of AI engineering isn't just writing better code. It's knowing when not to code at all.

A Framework for AI Decisions I Wish I'd Had Earlier

Health insurance premiums are jumping 18% for 2026—more than double last year's increase. Your margins are compressed from every direction. Your board wants to know what you're doing about AI.

Before you approve another vendor demo, ask: what specific decision are we trying to make, and what makes it correct?

If you can't answer that precisely, you'll get perfectly functional AI that solves the wrong problem. Then you'll build validation processes confirming those wrong solutions. Six months later, you're wondering why the claims team still works manually.

Why AI Projects Fail

Mid-market health insurers face brutal pressure. Premiums up 24% since 2019. Specialty drugs crushing margins. Competitors with better tech stealing members. Everyone's being told to "do something about AI."

So companies follow the playbook: hire consultants, create an AI strategy, pilot some tools, report adoption metrics. A year later, leadership claims 60% AI adoption while operations runs exactly like before.

The mistake is starting with technology instead of the problem.

Start With Specifications

Pick one painful, high-volume process. Prior authorizations for standard procedures. Claims coding for routine diagnoses. Eligibility verification.

Now describe exactly what decision needs to be made and what makes it correct. Not what you want AI to do, what business result you need.

For prior auths: "Knee MRI requests with documented conservative treatment and specific clinical indicators get approved within 2 hours with 98% accuracy against Medicare criteria."

That specificity forces clarity. You can't hide behind "improve efficiency." You've defined success before evaluating any technology.

Most companies skip this. They let vendors define the problem based on what their AI does. That's backwards. Make the AI solve your precisely-defined problem.

Build vs. Buy Comes Down to Differentiation

Build when the process creates competitive advantage. If your utilization management differs from competitors because of your network or market, buying generic AI means conforming to standard industry processes—eliminating your edge.

Buy when it's commodity work. Claims adjudication for routine procedures follows the same logic everywhere. No advantage comes from proprietary systems here.

For PE operators: companies with proprietary AI solving unique challenges command premium valuations. Companies using the same vendors as everyone else don't.

• The Questions That Matter

When evaluating vendors, skip the feature lists. Ask:

"Show me a client who deployed this in production with verified results." Not pilots. Production. If they can't provide verifiable references, walk away.

"When your system is wrong, what happens?" They should have documented failure modes and error rates. If they claim it's never wrong, they haven't deployed at scale.

"Can I audit the decision logic?" You can't defend what you can't explain, and regulators will demand explanations.

Create the Forcing Function

Here's why AI gets ignored: you implement it alongside the old process. Staff can choose. They'll pick the familiar manual approach every time.

Instead: Pick one authorization category—routine imaging for specific conditions—and route it exclusively through AI. No manual backup. The old process is gone for these cases.

Your manual process already has error rates; you're just used to them. AI with comparable accuracy that works in hours instead of days creates immediate value. More importantly, it forces teams to trust the output instead of maintaining parallel workflows that defeat the purpose.

Skip the endless pilots. If the specification is clear and the vendor has proven deployments, commit fully to one use case. You'll discover real integration problems immediately rather than after a "successful" pilot.

Measure What Matters

Don't track AI adoption. Track the operational outcome you specified initially.

For prior auth AI: measure processing time, denial rates, appeals, provider satisfaction, member complaints. These show if AI actually improves operations or just creates different problems.

If you're a PE operator evaluating portfolio companies, ignore "adoption metrics" entirely. Ask: what specific processes work measurably better because of AI? If management can't point to concrete improvements, they have theater, not value.

Steps to Try

• Pick one high-volume process that genuinely frustrates your team.

• Document what a correct decision looks like—precisely.

• Then decide: does this create competitive advantage or is it commodity work?

  Differentiated? Build it internally with a 12-18 month timeline.

  Commodity? Find vendors with proven production results. Verify independently. Commit fully to one use case.

And before you start: figure out what happens to the people currently doing this work. Without a plan for them, the AI won't get implemented regardless of how good it is.

The companies winning with AI aren't building the most sophisticated systems. They're solving precisely-defined problems with clear forcing functions and honest change management.

The $40K Vibe Coding Bug: When AI Perfectly Solves the Wrong Problem

Your developer just shipped a feature in three hours that would've taken three days. The code looks clean. Tests pass. You're thrilled about the velocity. Then users start complaining because every time someone likes their post, they get an email. And a push notification. And an in-app notification. All at once.

Nobody caught it because the AI understood "add notifications when posts get liked" and did exactly that. It also wrote tests that verified notifications worked. Everything passed. The problem? The AI didn't know users expect to control how they get notified, not just receive all notifications through every possible channel.

This is the AI coding problem nobody's talking about. It's not that AI writes bad code. It's that AI writes perfectly good code that solves the wrong problem, then writes tests that confirm it solved that wrong problem beautifully.

Your code reviews can't catch this. Your CI/CD pipeline can't catch this. Because technically, nothing is wrong. The code works. The tests pass. You just built the wrong thing.

What's Actually Happening

Pre-AI, when developers wrote code slowly, they had time to think about what they were building. Writing tests forced them to consider edge cases. Code reviews caught logical errors because reviewers understood the problem space.

Now? A prompt becomes code in seconds. Tests generate just as fast. Ship it. The thinking step disappeared. We accidentally optimized it out.

The developers who succeed with AI aren't the ones generating code fastest. They're the ones who realized they need to think more before generating code, not less.

Because AI will happily build whatever you imply you want, whether that's what you actually need or not.

For Technical Teams: The New Workflow

Stop doing: prompt → generate code → generate tests → ship

Start doing:

• Write the specification first. Before touching AI, write down the actual system behavior: "When a post gets a like, send one notification based on user preferences. Check notification_settings table for preferred channel (email, push, or in-app). If user disabled like notifications, send nothing. Batch multiple likes within 5 minutes into a single notification."

• Generate tests from that specification. Let AI create tests. Then ask: do these tests verify my specification or just validate AI output? This is where you catch that AI interpreted "send notifications" as "send all notification types" instead of "respect user preferences."

• Generate implementation only after tests match your spec. Now you're reviewing AI code against a specification you understand, not just checking if code looks syntactically correct.

• Tag AI-generated code in commits. Mark what came from AI assistants. This lets your team track which code creates maintenance burden over time.

This takes 10 minutes per feature. Your velocity is still 3x faster than pre-AI. You just stopped optimizing for generation speed and started optimizing for building the right thing.

For Leaders: What to Ask and Enforce

Most teams are reviewing AI-generated code the way they reviewed human-written code—looking for bugs, not requirement mismatches. Here's how to fix it:

Questions to ask your engineering leadership:

• What percentage of AI-generated features had written specifications before code generation?

• How are we tracking bugs in AI-generated code vs. human-written code?

• Can developers explain the system behavior their AI code implements, not just what the code does?

Policies to implement:

• Mandate specification-first development. No AI-generated code merges without a written specification of intended behavior. This isn't documentation theater—it's forcing systems thinking before implementation.

• Track AI code separately in your repos. Tag AI-assisted code and measure its bug rates, time-to-fix, and maintenance costs over 6 months. Early data from teams doing this: AI code has fewer implementation bugs but more requirement mismatches. The specification step fixes this.

• Change how code reviews work. Stop asking reviewers "is this code correct?" Start asking "does this code match the specification? What breaks if our assumptions change?"

What This Actually Costs (And Saves)

Real numbers: Teams using AI assistants with specification-first workflow maintain 3-4x velocity gains while keeping quality comparable to pre-AI development. The specification step costs about 20% of raw generation speed but catches requirement mismatches before any code exists.

Teams skipping this? Initial 5x velocity that degrades to 1x within six months as technical debt accumulates and nobody understands what their system actually does.

The company that lost $40K on bad authentication now spends 10 minutes per feature writing specifications first. They haven't had a requirement-mismatch bug in six months. Their velocity is still 3x faster than before AI. They just stopped measuring speed by how fast code generates and started measuring it by how fast correct features ship.

The Real Shift

AI coding assistants are incredible at implementation. They're terrible at understanding what you actually need. The teams figuring this out aren't fighting AI or trying to slow it down. They're recognizing that AI accelerates the wrong part of development if you let it.

The velocity gain is real. But it only compounds if you preserve the thinking that makes sure you're building the right thing. Otherwise you're just accumulating technical debt at impressive speed.

Beyond the Security Theater: What Actually Makes AI Deployments Safe

Your employees are already using AI. Right now. With company data. Through personal ChatGPT accounts, Claude on their phones, GitHub Copilot on personal machines.

Publicis Sapient's research found that while 78% of organizations claim AI adoption, fewer than 5% of employees use sanctioned tools. The rest? They're using it anyway—without your governance, security, or ability to learn from what works.

The real risk isn't controlled deployment. It's that you're already deployed, just invisibly.

What Internet Adoption Actually Taught Us

Remember 1995? Companies faced the same question: let employees browse the web or lock it down until we understand the risks.

Organizations that succeeded didn't wait for perfect security. They created boundaries, made basic access universal, and learned rapidly from mistakes. The ones that waited for comprehensive frameworks watched competitors move faster.

The pattern repeated with email, cloud storage, mobile devices. Early adopters with learning systems beat late adopters with perfect governance every time.

What Failure Actually Looks Like

Real AI incidents from the past year:

• Healthcare admin pasted patient names into Claude (contained in 3 hours, zero actual breach)

• Sales team shared pricing via ChatGPT (caught in audit, strategy already public on earnings call)

• Engineer exposed API keys in code (standard security issue, unrelated to AI)

Compare this to delayed deployment costs: competitors moving 2-3x faster on analysis, product teams stuck shipping quarterly instead of monthly, customer service response times at 24 hours while competitors hit 2 hours.

The math isn't close.

Strategies You Can Implement This Week

Sandbox Creation (Day 1-2):

• Designate 3-5 specific workflows as AI experimentation zones: draft content creation, internal process analysis, data summarization on non-sensitive datasets

• Document clear boundaries: no customer PII, no binding financial commitments, no proprietary code in public tools

• Remove approval requirements within these boundaries—make access universal

Incident Protocol (Day 3):

• Establish 24-hour response: immediate documentation, root cause analysis next day, learnings shared within 48 hours

• Create explicit protection: document that sandbox experimentation cannot result in negative performance reviews

• Designate one person as AI incident coordinator with clear escalation paths

Graduated Access Framework (Week 2):

• Tier 1 (everyone): Basic prompt interfaces for sandboxed work

• Tier 2 (demonstrated users): API access after completing 10+ successful Tier 1 tasks

• Tier 3 (proven teams): Integration capabilities after documented value creation

Monthly Learning Cycles (Ongoing):

• Collect compression moments: where did AI turn 3-day tasks into 3-hour tasks?

• Share incident learnings without blame—celebrate finding and fixing issues

• Adjust sandbox boundaries based on actual usage patterns, not imagined risks

The Actual Choice

Your competitors aren't deploying AI more safely. They're deploying it more pragmatically—making safe experimentation faster than unsafe workarounds.

Safe deployment means people actually use the tools. Risk management without usage is just expensive theater.

Vibe Coding 101: The $4 Million Decision Your Engineering Team Is Making Without You

Your senior developers are already using AI to write code. Not because they asked permission, but because it makes their lives easier. They're quietly using ChatGPT and tools like Cursor on side projects, experiencing 2-3x speed gains, while your official development processes still move like it's 2020.

I know this because I'm doing it too. I've been using Claude Code on side projects for greebookai.com, and I'm seeing those same 2-3x productivity gains. What used to take me a weekend now takes an evening. The difference is real, and it's happening right now outside your official processes.

GreenBookAI Home Page Upgraded Thanks To Vibe Coding. Please visit https://greenbookai.com/ for more

This creates an awkward situation: your engineers know there's a better way, but your company hasn't figured out how to capture it. And your 2026 budget planning is happening right now.

Let's Talk Numbers

Forget the hype for a second and think about your actual budget. If you're spending $10M on development, research from Publicis Sapient shows you could potentially deliver 40-60% more output with AI assistance. That's not $4M in savings, it's the difference between shipping 12 features next year versus 20.

But here's the honest part: you need to invest upfront. Maybe $300-500K for proper tools, some training, and building guardrails. The pitch isn't "fire developers and save money." It's "keep the same team and deliver way more value, starting about six months from now."

Companies using platforms like Sapient Slingshot report cutting engineering time by 50-70% during the actual coding phase. The catch? They're also spending 10-15% more time on reviews and security checks. Still, the net result is shipping 40-50% faster—if you set it up right.

Your Hiring Situation Just Changed - There is a new problem in town

This isn't a future problem. By next quarter, your recruiting team needs new answers.

The old interview question—"write this algorithm on the whiteboard"—doesn't work anymore. Anyone can generate decent code in seconds now. What matters is whether someone can design a system, spot when AI is leading them astray, and explain why one architectural choice beats another.

Your senior engineers are actually more valuable now, not less. They're the ones who can tell when AI-generated code looks good but will create a maintenance nightmare in three months. But you need to help them see their evolving role: less "I write all the code" and more "I architect systems and guide both junior developers and AI tools."

And when candidates ask about your AI strategy in interviews, "we're exploring it" is a red flag. It signals you're behind. Better answer: show them your framework for how you're using AI to amplify engineers, not replace them.

A Simple Way to Decide What's Safe

Not everything should be "vibe coded." Here's how to think about it:

1. Go for it on internal tools, prototypes, quick data dashboards, and one-off analysis. Anything where speed matters more than long-term maintenance.

2. Proceed carefully with new features on existing products—but have experienced engineers review everything. Same with new services that have clear boundaries.

3. Stay away from anything security-critical, payment processing, healthcare records, or systems where failure means regulatory problems or customer data exposure.

It's really about consequences. If your marketing analytics tool breaks, you make worse decisions for a day. If your payment system breaks, you lose customer trust and potentially face serious liability.

The Uncomfortable Part

Here's what worries me: in a couple years, there will be two types of companies. Some will have figured out how to use AI effectively with proper oversight. They'll deliver more while spending less. Others will be paying premium prices for a shrinking pool of developers who work the old way, watching competitors ship faster.

The winners aren't being reckless. They're being deliberate, building clear systems for when to use AI and when to rely on human expertise. That distinction is the actual competitive advantage.

As always, please leave your comments and questions. And if you're tinkering with questions like these, I'd be happy to help through my work with Publicis Sapient.

Open-Source vs Closed-Source LLMs: What Leaders Need to Consider

When it comes to deploying LLMs, leaders often default to whatever's easiest to spin up, usually a closed-source API. But that quick start can become a long-term constraint. Publicis Sapient found that 42% of enterprises abandoned most of their AI initiatives last year, and a big reason is infrastructure choices that don't match what the organization actually needs.

The Real Trade-offs

Closed-source models (GPT-4, Claude, Gemini) get you to a working solution fast. API integration takes hours, not months. You get support, predictable SLAs, and best-in-class reasoning.

Open-source models (Llama, Mistral, Falcon, DeepSeek) give you complete control, and complete responsibility. You decide where data lives and how the model behaves. But you're on the hook for infrastructure, security updates, and performance optimization.

The question isn't which is better. It's which trade-off your organization can actually execute on.

The Cost Reality

Closed-source pricing scales fast. A chatbot handling 100,000 monthly interactions might run $2,000-$5,000/month. Scale to 1 million and you're at $20,000-$50,000/month. For high-volume customer service or personalization engines, those API costs add up quickly.

Open-source eliminates API fees but needs GPU infrastructure at $3,000-$10,000/month minimum. The break-even typically hits between 500K-1M monthly interactions. Where open-source often wins: processing proprietary data you can't send externally anyway—production configurations, supply chain optimizations, equipment patterns.

When Regulation Drives the Decision

Processing protected health information? Closed-source APIs without proper Business Associate Agreements create immediate HIPAA violations.

Open-source keeps data on-premises but you need the technical chops to back it up. Many organizations go hybrid: closed-source for general communication, self-hosted for regulated data.

The Decision Framework

Choose closed-source when:

• You need to ship in weeks, not months

• You lack ML/AI engineering resources in-house

• You're handling <500K monthly interactions

Choose open-source when:

• You're in a regulated industry with data residency requirements

• You have proprietary data you legally cannot share with vendors

• You're at scale (>1M monthly interactions) where TCO favors it

Go hybrid when:

• You have both public-facing and sensitive internal use cases

• Different departments have different compliance requirements

Before committing, ask: Who on your team has deployed production ML infrastructure? Have you negotiated proper data processing agreements? At what volume do API costs become a problem?

Conclusion

The companies winning with AI aren't the ones with the fanciest models. They're the ones who've matched their technical choices to what they can actually execute.

Sources:

• a16z State of Enterprise AI: 41% plan to increase open-source LLM usage

• Publicis Sapient Guide to Next 2026: 42% abandoned most AI initiatives in 2024

• Astera: Llama models downloaded 400 million times in 2024

The Real Cost of AI Systems: What Leaders Need to Know Before Scaling

Most AI strategy conversations start with capability questions. What can the model do? How accurate is it? What problems can it solve?

But there's a more fundamental question that often gets answered too late: What does this actually cost at the scale we're planning to operate?

The Gap Between Theory and Practice

Your marketing team needs to analyze customer feedback from the past quarter and create a summary report. Someone asks GPT to do it: "Here's our customer feedback data. Summarize the key themes and create recommendations." Clean request. 50 tokens using OpenAI Tokenizer Counter.

Tokens are the units of measuring LLM inputs and outputs, roughly equivalent to 3-4 characters or about three-quarters of a word in English.

The first response comes back with general themes and basic structure. Helpful, but it's missing specific metrics. It hasn't connected feedback to the product roadmap. The tone isn't right for executives.

So they clarify. "Include sentiment scores and tie themes to our Q3 product launches." Better, but now it needs reformatting for slides. Another exchange. Then it needs to match last quarter's framework for comparison.

Input Across Multiple Prompts

Output tokens

Final tally: 92 tokens of input across multiple prompts, 3,428 tokens of output.

This pattern isn't unique. Research shows roughly two-thirds of ChatGPT users need 2+ prompts per task, with about a third requiring 5+ exchanges to get usable output. The cost isn't in the initial query, it's in the iteration required to get from "plausible answer" to "working solution."

Running the Numbers

Using the OpenAI GPT API Pricing Calculator:

• 100 API calls: ~$3

• 1 million API calls: ~$30,000

That's for GPT-5.1. But for this feedback analysis, the team needed solid insights, not perfect prose or deep statistical analysis. Switching to Gemini-2.5 could drop that million-call cost to ~$1,000.

The strategic question: Are you paying for capability you don't actually need?

The Tradeoff Matrix Leaders Should Map

1. Accuracy vs. Cost: State-of-the-art models cost significantly more. For customer-facing responses or critical decisions, that premium might be worth it. For internal summaries or draft content, probably not.

2. Speed vs. Power: Faster models sacrifice some capability. Real-time customer queries need speed. Monthly report generation doesn't.

3. Context Length vs. Efficiency: Models handling longer inputs cost more per call. Entire quarterly reports as context? You pay for that. Working section-by-section? You save considerably.

Most organizations pick a model tier—usually the flagship—and apply it uniformly. That's leaving money on the table. The strategy should be matching model capability to specific requirements.

Three Planning Questions

1. What's your realistic usage multiplier?

   Don't build your cost model on single exchanges. If your team averages 3-5 prompts per task, multiply your projected costs by that factor. Tools like the OpenAI Tokenizer let you measure actual token usage across real workflows.

2. Have you mapped requirements to model tiers?

   Create a matrix of your use cases and their requirements. Legal contract review might need GPT-4's accuracy. Meeting summaries might work fine with GPT-3.5 or Claude Haiku. Customer feedback analysis could use mid-tier models. The goal is precision matching, not blanket deployment.

3. What's your cost scaling curve?

   Model your expected costs at 10x, 100x, and 1000x your initial volume. If the curve breaks your economics, you need to know that before you scale, not after.

Why This Matters for Strategy

AI systems scale differently than traditional software. With SaaS, you pay per seat regardless of usage intensity. With AI, cost rises directly with usage.

Success means more expense. If your AI implementation works exactly as planned, high engagement, widespread usage, your costs accelerate. Unless you've structured your model strategy correctly, that success becomes unsustainable.

The organizations getting this right aren't necessarily the ones with the biggest AI budgets. They're the ones doing the systematic work upfront: measuring real prompt patterns, testing model tiers against specific requirements, understanding their cost curve before they're locked into it.

Starting Point

1. Run a pilot with actual tracking.

2. Use real tasks from your team.

3. Measure the prompt-to-solution ratio.

4. Calculate costs across different model tiers using the pricing calculators.

5. Map your findings to projected scale.

The exercise won't be glamorous. But it's the difference between an AI strategy built on actual economics and one built on capability demos and vendor promises.

How are you approaching AI cost modeling in your organization? Let's discuss.

Reference

1. Fishkin, Rand. “We Analyzed Millions of CHATGPT User Sessions: Visits Are down 29% since May, Programming Assistance Is 30% of Use.” SparkToro, 30 Aug. 2023, sparktoro.com/blog/we-analyzed-millions-of-chatgpt-user-sessions-visits-are-down-29-since-may-programming-assistance-is-30-of-use/

How Do You Make Workers Choose AI Over Their Old Methods?

There is a massive challenge with current AI adoption in the workplace. While 37.4% of workers report using AI at work, they spend only 5.7% of their work hours actually using it. Publicis Sapient research reveals 60% of CEOs believe AI will revolutionize operations, but only 24% of executives managing those functions agree. The gap between "we have AI" and "AI transforms how we work" is enormous leaving opportunity for leaders to focus on AI adoption.

But AI is different from past technologies. When companies adopted email and intranets in the 1990s, workers had no alternative—you either used the company system or couldn't communicate. Adoption was fast because it was mandatory infrastructure. AI is optional. Workers can always fall back to their old methods. There's no forcing function.

Unlike becoming "an internet company" (which required rebuilding business models), AI adoption is about workers choosing to change how they personally work. That's harder. Electricity took 40 years to boost productivity because factories had to redesign layouts. AI requires workers to redesign their individual workflows—and they can simply refuse.

Historical adoption required clear forcing functions: "read the Bible or face damnation" for literacy, "track your money or go bankrupt" for bookkeeping. For AI, "be more productive" is too abstract to drive behavior change.

What Leaders Must Do Today

1. Make AI Mandatory for One Task

   Stop making AI optional. Pick one concrete task where AI demonstrably saves time.

   Action: For example, Starting Monday, require employees to attempt an AI answer before asking colleagues any question. They must share: (1) their prompt, (2) AI's response, (3) why it's insufficient. This creates both immediate friction reduction and a database of real use cases. Track questions saved per week.

2. Create Your First Worked Example

   For AI to be adopted, you need worked examples—showing exactly how someone uses AI for a specific task, like Manzoni's 1540 bookkeeping manual that taught through real merchant ledgers.Action: Screen-record your best performer using AI for one task start-to-finish (prompts, iterations, output). Make it a 3-minute video showing exactly how Sarah reconciles invoices in 8 minutes instead of 40.

3. Hire Junior People as AI Champions

   Research shows 93% of Gen Z use two or more AI tools weekly versus 79% of millennials. Gen Z workers lead with 82% adoption compared to 52% of Baby Boomers. Yet many organizations are cutting junior roles, assuming AI replaces entry-level work—exactly backward.

   Action: Hire junior employees specifically as AI champions. Train them from day one on AI tools. Have them spend 25% of their time teaching older colleagues. The strategy of eliminating junior people because "AI will do their work" misses that junior people are your fastest AI adopters and best teachers.

4. Measure Time Spent, Not "Adoption"

   Workers spend only 5.7% of work hours using AI despite 37% reporting they "use" it. Adoption theater wastes resources.

   Action: Track daily for one month: (1) tasks using AI, (2) minutes spent, (3) time saved. Publish internally weekly. Celebrate highest time-saved, not most "engaged."

The Bottom Line

Successful cognitive technologies required 200-400 years for mass adoption. You don't have that long. Winners won't have "AI strategies"—they'll have rebuilt specific work processes.

Start with one process. Make it work. Document exactly how. Teach person-to-person. Make it mandatory. Measure actual time saved. Then move to the next one.

Stop strategizing. Start rebuilding.

Sources:

• Publicis Sapient, "AI and Digital Business Transformation," 2025

• St. Louis Federal Reserve, "The State of Generative AI Adoption in 2025," November 2025

• Google Survey on Gen Z AI adoption, November 2024

• London School of Economics & Protiviti, "Bridging the Generational AI Gap," 2025

Chat With Your Documents: Build a RAG AI Agent with No Code in 20 minutes

In my previous tutorials, you built AI agents for web scraping and conversations. Today: teach an AI to answer questions about YOUR documents.

What you're building: A simple chatbot that reads your documents and answers questions with accurate information—no hallucinations.

What you need: N8N account, OpenAI API key, a document, 20 minutes.

Why This Matters

ChatGPT doesn't know your company policies, resume, or product docs. RAG (Retrieval-Augmented Generation) fixes this by letting AI pull from YOUR documents before answering.

Without RAG: "Tell me about my work experience" → Generic advice With RAG: Specific details from your resume about Johns Hopkins, Morgan State, your M.Sc.

This is how companies build internal assistants, support bots, and knowledge bases.

How It Works

Two workflows that share data:

Workflow 1 - Upload: Document → Breaks into chunks → Converts to vectors → Stores in database

Workflow 2 - Chat: Chat → AI Agent → Chat Model→ Searches database → AI reads chunks

Vectors are coordinates on a mathematical map. Similar meanings = close together. When you ask "What university?", it finds education chunks, not hobby chunks.

Build Part 1: Upload Workflow

Step 1: Create workflow, add Form Trigger

• Add form field: Type = File, Name = "document", Accept = .pdf,.txt

• Form Title: "Upload Knowledge Document"

Step 2: Add Simple Vector Store (not connected yet)

• Mode: Insert Documents

• Memory Key: my-knowledge-base (critical—you'll reuse this)

Step 3: Click Vector Store's "Document Loader" port → Add Default Data Loader

• Text Splitter: Recursive Character Text Splitter

• Chunk Size: 1000, Overlap: 200

Step 4: Click Vector Store's "Embedding" port → Add Embeddings OpenAI

• Model: text-embedding-3-small

• Add your API key

Step 5: Execute workflow, upload test document, verify chunks created.

In my case, I have created .txt file with the content of my resume to be used for testing

Build Part 2: Chat Workflow

Step 1: New workflow, add When chat message received

• Public Chat: ON

• Initial Message: "Ask me anything about uploaded documents"

Step 2: Add AI Agent, connect to Chat Trigger

• Prompt:

You answer questions using only the knowledge base.
If no relevant info found, say "I don't have that information."
Be specific. Cite details.

Step 3: Click Agent's "Model" port → Add OpenAI Chat Model

• Model: gpt-4o-mini

• Temperature: 0.2 (factual, not creative)

Step 4: Click Agent's "Tools" port → Add Simple Vector Store

• Mode: Retrieve Documents (as Tool)

• Memory Key: my-knowledge-base (MUST MATCH upload workflow)

• Description: "Search knowledge base for facts, dates, names, details"

• Limit: 4

Step 5: Click this Vector Store's "Embedding" port → Add Embeddings OpenAI

• Model: text-embedding-3-small (MUST MATCH upload workflow)

Step 6: Execute, get chat URL, ask questions about your document.

Real Example

I uploaded my resume. Asked: "What university did the candidate go to?"

Response: "Johns Hopkins University (M.Sc. Engineering Management, May 2016) and Morgan State University (B.Sc. Electrical Engineering, May 2013)."

No hallucination. Just facts from my document.

Common Issues

"I don't have that information" but it's clearly there:

• Both workflows must use identical Memory Key

• Increase Limit to 6 chunks

• Decrease chunk size to 500-700

AI uses general knowledge instead of documents:

• Add to prompt: "NEVER use training data. ONLY use knowledge base tool."

Production Upgrades

Simple Vector Store works for learning but data clears on restart.

For production:

• Pinecone/Qdrant: Persistent storage

• Authentication: Add password/API key protection

• Metadata: Tag documents by department/date for filtering

• Cost monitoring: Track OpenAI API usage

What You Built

Document → Chunks → Vectors → Storage → Search → Answer

This architecture powers:

• HR bots ("What's our vacation policy?")

• Support bots ("How do I reset password?")

• Research assistants (50-paper summaries)

• Documentation bots ("How do I use this API?")

Subscribe for the next tutorial. Tag me on LinkedIn if you build something.

The insight: Companies winning with AI use better systems, not better models. You just built one.

Resources: N8N RAG Docs | RAG Template

Build Your Web Scraper AI Agent in 15 Minutes with No Code

In my previous post, you built a basic chatbot. Today, we're leveling up: an AI agent that scrapes websites and extracts structured data through conversation.

What you're building: A web scraper that takes natural language commands and returns clean JSON data.

What you need: N8N account, OpenAI API key, 15 minutes.

Why AI Agents Change Web Scraping

Traditional scrapers break when websites update their HTML. This agent adapts. You tell it what you want in plain English—it figures out how to extract it.

Step 1: Build the Foundation

Create a new workflow. Add two nodes:

1. Manual Chat Trigger (search "chat")

2. AI Agent (connect it to Chat Trigger, select "AI Agent" in dropdown)

Step 2: Configure the Model

Inside the AI Agent:

1. Chat Model → Add → OpenAI Chat Model

2. Select gpt-4o, set Temperature: 0.2

3. Add your API credentials

Step 3: Add Tools

In "Tools" section:

1. Add → HTTP Request Tool

2. Method: GET, Response: "Include Full Response"

3. Leave URL empty (agent fills dynamically)

Key concept: The agent decides when to use this tool through reasoning, not predefined logic.

Step 4: Write Agent Instructions

Click on the AI agent and configure it system message, add option, select “System Message”

In "System Message", paste:

You are a professional web scraping AI agent. Follow this protocol:

SCRAPING WORKFLOW:
1. Validate the URL format before scraping
2. Use HTTP Request tool to fetch the page
3. Check HTTP status code:
   - 200: Success, proceed with extraction
   - 404: Page not found, inform user
   - 403/401: Access denied, suggest authentication needed
   - 429: Rate limited, inform user to retry later
   - 5xx: Server error, suggest retry

EXTRACTION RULES:
- Extract ONLY requested data in valid JSON format
- Strip all HTML tags unless specifically requested
- Handle missing data gracefully with null values
- Always include: {"status": "success/error", "data": {...}, "source_url": "..."}

ANTI-PATTERNS TO AVOID:
- Don't scrape if robots.txt disallows (inform user)
- Don't make multiple requests to same URL in one session
- Don't hallucinate data if extraction fails

ERROR RESPONSE FORMAT:
{
  "status": "error",
  "error_type": "http_error/parsing_error/access_denied",
  "message": "Clear explanation",
  "suggestion": "What user should do next"
}

This prompt defines behavior. The agent follows instructions > relies on training data.

Step 5: Add Memory

In AI Agent's "Memory" section:

1. Add "Window Buffer Memory"

2. Set Context Window: 10

Now you can say "scrape that same site but get prices instead" and it remembers context.

Step 6: Test Your Scraper

Save and click "Test workflow". Try:

Scrape https://example.com and extract the main heading
Scrape https://news.ycombinator.com - Extract top 3 story titles as JSON
Scrape https://wikipedia.org and return page title, first paragraph (no HTML), and number of languages

You should get clean JSON. If it fails, the agent explains why.

What Just Happened (Strategic View)

The agent isn't following predefined rules. It's reasoning: "I need data from a URL → I have an HTTP tool → I'll use it → Now I'll parse the HTML → Format as requested."

This is fundamentally different from traditional automation.

Making It Production-Ready

Scale: Replace Chat Trigger with Schedule (scrape hourly), store in Google Sheets/Postgres, add Slack alerts.

Monitor: Insert IF node after AI Agent to catch errors and route to notifications.

Rate limits: Add Wait nodes (2-3 seconds), check robots.txt, don't hammer servers.

Limitations

Works for static HTML. Doesn't work for JavaScript-heavy sites, login walls, or CAPTCHAs. Use ScrapingBee or Browserless for those.

The Real Insight

You just built a scraper that adapts to any page structure without writing parsing logic. Compare this to traditional scraping: writing CSS selectors, handling layout changes, managing error states.

The skill isn't just building this. It's knowing when AI agents are the right tool versus when you need traditional scraping's precision.

Subscribe below to follow along as I build and share more on AI, Strategy & Automation.

GreenBookAI: Custom Itinerary Generation with Agentic AI

In a world where travel can feel exciting for some and unsafe or isolating for others, especially for people from underrepresented backgrounds, personalized and culturally aware trip planning isn’t a luxury it’s a necessity. Yet, most travel platforms today still offer one-size-fits-all recommendations, often overlooking safety, cultural fit, or inclusion.

GreenBookAI was built to change that.

It’s a travel planning tool powered by AI that not only curates personalized itineraries. It does so with an emphasis on trust, safety, and cultural relevance. This post provides a high-level technical look into how GreenBookAI works under the hood, and how we’re using agentic artificial intelligence to build a better travel experience.

If you're curious, I invite you to sign up and test GreenBookAI. It’s still in active development, but the core functionality is already live and your feedback would be incredibly valuable.

Backend Architecture: The Core Intelligence

At the heart of GreenBookAI is a Python-based backend that orchestrates every aspect of the itinerary generation process. This is where most of the intelligence lives.

Multi-Agent System

A standout feature is our use of agentic AI, powered by OpenAI Agents. We’ve created a dedicated Activities Search Agent that performs structured searches and returns detailed JSON responses for businesses.

These results are processed by a central orchestrator that allocates hobbies, restaurants, and content across a multi-day plan based on the user’s travel window and preferences.

Concurrent Processing

To ensure speed, we’ve implemented concurrent async processing using asyncio and httpx.AsyncClient. This allows us to search for multiple types of businesses at once, validate multiple URLs simultaneously, and respond quickly, even when generating multi-day plans

Rate Limiting

To stay compliant with third-party APIs (like OpenAI’s), we use rate limiting via asyncio.Semaphore to throttle concurrent requests and prevent overload.

Frontend Experience: Built in React.js

The user interface is built with React.js & Vite.js, designed to keep things simple but dynamic. The frontend handles:

• Collecting user inputs (dates, hobbies, food preferences, etc.)

• Sending that data to the backend

• Displaying the structured itinerary, complete with location info and image assets from Pexels for added context

It’s a lightweight but functional interface that makes exploring your travel plans feel personal.

Real-World Sharing: What’s Next?

I recently demoed GreenBookAI during a panel at Schneider Electric, where we discussed how AI can be used to make tools more inclusive and context-aware.

The reaction reinforced the need for products like this; tools that don’t just optimize for efficiency, but for empathy and safety too.

There’s still a lot of work ahead but the core engine is live and I’d love for you to give it a try.

Whether you're someone who wants culturally relevant travel suggestions, a builder curious about multi-agent AI systems, or just looking for a fresh take on travel planning. I welcome your thoughts, ideas, and feedback.

Building JapaAdvisorAI for Agentic Immigration Assistance In LangGraph

In my latest hobbyist project, JapaAdvisorAI, I engineered an agent-driven AI assistant that leverages stateful orchestration, large language models (LLMs), and real-time information retrieval to provide dynamic, high-fidelity immigration guidance.

This project is not just a chatbot—it embodies AI as an autonomous agent, making real-time decisions, adapting its strategy based on user interactions, and leveraging external tools for knowledge augmentation. Below, I outline the technical stack, architectural principles, and agentic AI design that underpin JapaAdvisorAI’s intelligence.

Tech Stack for JapaAdvisorAI: From Frontend to Backend

Building an AI-enabled product involved a whole host of learnings and utilizing various tools and products. I relied on using AI questions and answering tools for code generation and UI generation but spent a good amount of time getting things to work. Below are the different categories of tools used:

1. Frontend & API: Javascript/Html/Css (UI), FastAPI (backend API).

2. AI Orchestration: LangChain (prompt management), LangGraph (stateful conversation flow).

3. LLM Integration: ChatOpenAI (response generation, query refinement).

4. State & Storage: PostgreSQL (data storage).

5. Knowledge Retrieval: TavilySearchResults (real-time search), content extraction for summaries.

6. Cloud & Deployment: Digital Ocean Serverless tier, Docker.

7. Code generation: Github co-pilot, Galileo UI, ChatGPT and Deep Seek.

Architecting an Agentic AI System with LangGraph

Unlike conventional rule-based chatbots, JapaAdvisorAI employs LangGraph to model a multi-agent system where conversational nodes represent discrete agents responsible for distinct cognitive functions. This modular approach enhances adaptability, ensures context retention, and enables autonomous decision-making within the AI system.

The key components include:

• Multi-Agent Workflow with LangGraph’s StateGraph

At the core of JapaAdvisorAI is LangGraph’s StateGraph, which orchestrates the AI’s decision-making process by defining functional agents as graph nodes. This ensures:

1. Dynamic conversation routing based on user queries

2. Adaptive state transitions based on contextual understanding

3. Parallel execution of reasoning and retrieval tasks

Each node in the graph represents a specialized AI function, forming a decomposed agentic system rather than a monolithic model.

• Defining Agent Roles and Responsibilities

The AI system consists of distinct functional agents that operate in a coordinated manner:

Preprocessing Agent (Initialization & Context Injection)

1. preprocess_node: Captures user intent, extracts contextual details, and initializes the state with prior interactions.

2. Agent Role: Ensures that every user session retains contextual continuity, mitigating information loss between queries.

Query Refinement Agent (Enhancing User Input)

1. refine_tool: Utilizes prompt engineering to restructure and disambiguate user queries before passing them to the core LLM.

2. Agent Role: Acts as a pre-LLM filter, ensuring that user questions are structured optimally for precision in AI responses.

Conversational AI Agent (LLM Integration & Response Generation)

1. LLM backbone: ChatOpenAI API processes refined queries and generates contextual, multi-turn responses.

2. Agent Role: Dynamically adapts responses based on conversation history and prior decisions in the state graph.

Information Retrieval Agent (Real-Time Search & Augmented Responses)

1. generate_content_from_link: Extracts and summarizes retrieved documents for inclusion in responses. Uses TavilySearchResults to fetch relevant immigration information from trusted sources.

2. Agent Role: Functions as an external knowledge augmenter, ensuring that responses remain factually accurate and policy-compliant.

Intent Extraction Agent (Conditional Routing & Adaptive Transitions)

1. Intent extraction: Implements conditional_edge logic to route user queries dynamically based on complexity and knowledge gaps.

2. Agent Role: Determines whether to proceed with response generation, follow-up questioning, or external retrieval, ensuring a multi-step reasoning framework.

   • Each agent operates independently yet collaboratively, forming a distributed AI system where decisions emerge from collective reasoning rather than a single-pass LLM call.

Key Innovations in the Agentic AI Architecture

1. Stateful Orchestration with Graph-Based AI: By leveraging LangGraph’s graph-based AI architecture, JapaAdvisorAI transitions from traditional conversational models to a true agentic AI system.StateGraph ensures a structured, adaptive flow where conversations evolve dynamically rather than following static rules. Agents execute concurrently, enabling parallel processing of follow-ups, search queries, and response generation.AI decisions are no longer sequential; they are emergent behaviors resulting from interactions between different nodes in the system.

2. Multi-Agent Collaboration for Enhanced Decision-Making: JapaAdvisorAI’s agents operate as a distributed decision-making system where each node specializes in a particular task.

   1. Autonomous Follow-Up Generation: The Follow-Up Agent analyzes gaps in user input and triggers clarifying questions automatically.

   2. Search-Augmented Reasoning: The AI seamlessly determines when it lacks sufficient knowledge and invokes the Retrieval Agent for real-time updates.

   3. Adaptive Response Refinement: The Query Refinement Agent continuously optimizes user inputs to maximize response accuracy.

      This approach mirrors real-world expert consultations, where multiple specialists contribute insights to refine answers progressively.

3. Hybrid AI: LLM Augmentation with External Knowledge

   One of the major challenges in AI-driven advisory systems is maintaining factual accuracy in rapidly changing domains like immigration law. To address this, JapaAdvisorAI employs a hybrid AI approach:
    - LLM-powered natural language processing for reasoning and conversation
    - External search integration for real-time fact-checking
    - Decision logic to determine when search augmentation is needed

   This ensures that the AI remains accurate, up-to-date, and grounded in real-world data rather than relying on outdated model knowledge.

Lessons Learned: Building the Next Generation of AI Advisors

1. Agentic AI Is Great For Conversational Systems: Traditional chatbots struggle with context retention, adaptability, and complex decision-making. By employing graph-based AI orchestration, JapaAdvisorAI demonstrates that:

• AI assistants can dynamically adjust their strategies based on real-time inputs.

• Autonomous agents enhance AI reasoning by specializing in distinct tasks.

• Multi-agent systems enable emergent behaviors, where responses evolve based on collective agent decisions.

2. The Best AI Advisors Combine Intelligence with Knowledge Augmentation: LLMs are not sufficient on their own—they require:

• Structured query refinement to ensure clarity

• Automated knowledge retrieval for real-time updates

• Graph-based decision-making to route user queries intelligently

• This hybrid AI paradigm significantly outperforms traditional chatbot architectures in knowledge-sensitive domains.

3. Modular AI Design Increases Scalability & Maintainability: By decoupling AI functions into agent-based nodes, I ensured that:

• New capabilities (e.g., new search APIs, expanded Q&A models) can be added without overhauling the system.

• The AI can scale horizontally, distributing different functions across specialized modules.

• The logic remains explainable and auditable, improving trust and compliance in high-stakes AI applications.

JapaAdvisorAI Demo

Here is a video demonstration of Japa Advisor AI agent deployed using Digital Ocean and responding to a simple query about documents required for a USA visa from Nigeria.

Final Thoughts: Building AI for Complex Decision-Making

JapaAdvisorAI represents a paradigm shift in AI advisory systems, proving that agentic architectures, knowledge augmentation, and adaptive workflows are the key to next-gen AI assistants. If you want to use JapaAdvisorAI, please contact me.

As a Principal AI Expert, I specialize in:

• Designing multi-agent AI systems using LangGraph

• Developing LLM-powered advisory tools with real-world augmentation

• Architecting AI that blends reasoning, search, and stateful decision-making

If your organization is exploring agentic AI solutions or needs a leader to drive AI innovation, let’s connect!

Using Machine Learning to know Patients that are No Shows

Here is a brief introduction into the project.

Please check out the blog post: https://www.dotunopasina.com/datascience/noshowappointments

Introduction

In this project, we will be utilizing machine learning algorithms to perform feature selection on patient appointments data. The goal is to understand what characteristics of a particular patient that makes them miss their appointment.

Dataset

The dataset for this project was gotten from Kaggle consisting of 14 columns and 110527 rows of data.

The data consists of the following columns:

1. Patient Id

   • Identification of a patient

2. Appointment ID

   • Identification of each appointment

3. Gender

   • Male or Female. Female is the greater proportion, woman takes way more care of they health in comparison to a man.

4. AppointmentDate

   • The day of the actual appointment, when they have to visit the doctor.

5. Scheduled Date

   • The day someone called or registered the appointment, this is before appointment of course.

6. Age

   • How old is the patient.

7. Neighborhood

   • Where the appointment takes place.

8. Scholarship

   • True of False . Observation, this is a broad topic, consider reading this article https://en.wikipedia.org/wiki/Bolsa_Fam%C3%ADlia

9. Hypertension

   • True or False

10. Diabetes

    • True or False

11. Alcoholism

    • True or False

12. Handicap

    • True or False

13. SMS_received

    • 1 or more messages sent to the patient.

14. No-show

    • True or False.

Machine Learning Process

The steps taken to accomplish our results include the following:

1. Data preprocessing.

2. Create awaiting time field (Days between Scheduled and appointed times)

3. Exploratory data analysis.

4. Pass the data through the machine learning algorithm

5. Select top 10 features that affect appointment times and least 10 features that affect appointment times.

The code of the project can be found on my github.

Exploratory Data Analysis

The below pie chart shows the number of Yes (shows up to appointment) as 85,299 and No (misses appointment) as 21,677. This implies we have an imbalanced data set and we need to keep that in mind as we move along.

Number of Yes and No to appointments

Machine Learning Model

The machine learning model used here was a logistic regression with lasso regularization. Regularization is a way of penalizing the model’s cost function to ensure that the model does not overfit. In this case, the features that are not important are made to zero while we can select the important features.

Results and Insights

The model selected the most important features that affect patients missing their appointment as seen in the figure below.

Feature selections of Appointment No Shows

From the image above we can break the groups of data into more likely to miss appointment and less likely to miss appointment.

More Likely to Miss Appointment

• Patients who had a large difference between their scheduled and appointment date missed their appointment the most

• Interestingly patients who received an SMS message still missed their appointment

• Patients in the Itarare and Santos dumont neighborhood were more likely to miss their appointment

• Patients between the ages of 13 and 14 were more likely to miss their appointments

Less Likely to Miss Appointment

• Patients who were age 64 and 69

• Patients who lived in Santa martha, Jardim da Penha and Jardim Camburi

• Patients who had Hypertension were less likely to miss their appointments

Credit Card Fraud Detection using Logistic Regression , Naive Bayes and Random Forest Classifiers

Introduction

The goal of this project is to utilize machine learning algorithms to classifier a transaction as fraudulent or not based on multiple inputs.

Datasets

The datasets for this project was gotten from Kaggle . The datasets contains transactions made by credit cards in September 2013 by european cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions. The transaction contained inputs of datasets from dimension reduced observations V1…V28 , amount and class representing if the transaction is fradulent or not.

Machine Learning Algorithm Process

The machine learning algorithm process highlights the steps taken to get the models up and running from start to end. It also describes the data preprocessing and cleaning stage of the problem. The machine learning algorithm process includes:

1. Download data sets from Kaggle.

2. Load data into Jupyter notebook and perform exploratory analysis.

3. Split the data into input and output columns.

4. Standard scale data to remove skewness in the datasets.

5. Pass the data into grid search logistic regression, naive bayes, support vector classifiers and random forest regression.

6. Calculate the performance metric of the models. Note since we have imbalanced data we use a confusion matrix and f1 score to evaluate models.

Code for project can be found on my github website

Results

The results of the 4 machine learning models evaluated are as follows:

1. Naive Bayes performed worse with a f1 score of 11.31 %,

2. Logistic regression was 72.9 %

3. Random forest was 87.4 %

4. Support vector machines took so long to run that I had to stop the process.

Conclusion

In general random forest classifier performed best as it is a combination of decision trees and is protected from the problem overfitting due to it ensembling method. This project was an interesting to learn from and the outcomes from the result was used to further my knowledge in machine learning. Please find the code used for the project on my github page