The Tale of Two Marketing VPs
How one chose dependency, the other chose capability—and the shocking difference three years later
Imagine two VPs of Marketing—let's call them Sarah and David. Both recognized AI's transformative potential in early 2025. Both had similar budgets, similar teams, and similar pressure from their CEOs to "make AI happen." The paths they chose couldn't have been more different. The outcomes, three years later, reveal everything wrong with how enterprise AI gets built today.
Note: I'm using marketing as an example here since I'm a CMO, but you could replace this with any VP of a business function—finance, accounting, sales, HR, customer success, operations, supply chain. The story remains the same.
Sarah's Path: The Consultancy Route
Sarah did what most executives do when facing complex technology decisions: she hired experts. After months of vendor evaluation, she selected a prestigious consultancy to build her "AI-powered marketing transformation." The proposal was impressive—120 slides of technical architecture, machine learning frameworks, and integration roadmaps.
The Investment: $8 million over 24 months, plus ongoing maintenance contracts.
Today, three years later, Sarah's reality is this:
Her AI system handles basic lead scoring and email personalization—functionality that existed a decade ago with different branding. Every modification requires a change request, budget approval, and a three-month development cycle. Her team spends more time explaining their workflows to external developers than actually doing marketing. The system works, technically, but it feels like using a calculator that requires an engineer to change the batteries.
When Sarah's CEO asks about expanding AI capabilities, the consultancy presents another impressive proposal: "Phase 2 of your AI transformation" for an additional $12 million. Sarah has become a customer, not an owner, of her AI strategy.
The hidden costs are staggering. Her team has spent hundreds of hours in requirements gathering sessions, translating their expertise to people who've never run a campaign, managed a funnel, or closed a deal. Every feature request goes through the same painful translation process: business need → technical specification → development → testing → deployment → inevitable revisions.
Sarah's team hasn't learned anything about AI. They've learned to be better customers of AI consulting services.
David's Path: The Domain Expert Route
David took a radically different approach. Instead of hiring AI experts, he decided to turn his marketing experts into AI creators. He partnered with EverWorker to transform his team from AI consumers into AI workforce architects.
The Investment: Under $50,000 for platform access and team training.
Today, three years later, David's reality looks like science fiction:
His team has built 47 specialized AI workers, each designed around specific marketing workflows they understand intimately. The content AI worker that creates campaign briefs knows seasonal rhythms, brand voice, and audience preferences because David's content manager built it. The lead qualification AI worker understands prospect behavior patterns because David's demand generation specialist trained it using real conversion data.
When market conditions change, David's team adapts their AI workforce in real-time. New competitor enters the market? The competitive analysis AI worker gets updated in minutes, not months. Campaign performance shifts? The attribution AI worker evolves immediately to capture new patterns.
David's team has become fluent in AI creation. They think in workflows, not workarounds. They build solutions, not requirements documents.
Most importantly, David owns his AI strategy completely. Every AI worker reflects deep domain expertise that no external consultant could possibly replicate. His competitive advantage isn't just having AI—it's having AI that understands marketing better than any consultant-built system ever could.
The Dirty Secret of Enterprise AI
Enterprise AI has a dirty secret: the industry is built on a lie that's costing organizations millions. While companies spend millions on AI consultants and wait quarters for "technical solutions," their own domain experts could build superior AI workers in days for a fraction of the cost. This translation layer isn't just inefficient—it's the most expensive middleman in business history.
Organizations are paying millions for AI solutions that deliver "partial successes" while their own employees could create AI workforces for the annual cost of a new grad salary. This isn't just inefficiency—it's organizational malpractice.
Large consultancies don't deliver AI solutions—they build permanent AI dependencies. Every consultant-delivered system is designed to need them forever. That's not a bug, it's their business model.
The Four Fatal Flaws of the Consultancy Approach
1. Expensive to Start, Expensive to Continue
The initial consulting contract is just the beginning. Sarah's $8 million investment came with hidden costs that multiplied over time:
- Multi-year lock-in contracts that make switching prohibitively expensive
- Translation costs as internal teams spend hundreds of hours explaining their expertise to external developers
- Opportunity costs as competitors move faster while Sarah waits for her next development cycle
- Maintenance contracts that often exceed the original development costs
Meanwhile, David's EverWorker investment works differently. You only pay for AI workers that deliver measurable business results at scale. It costs less than a junior employee's salary to get started with five AI workers. Most importantly, it eliminates the hidden costs of internal resources and time spent on translation by removing the middleman layer between business knowledge and AI creation entirely.
2. Permanent Dependency by Design
Any approach that delivers agentic AI as a custom engineering exercise creates the same issues that have plagued enterprises for decades: maintenance nightmares and technical debt. The custom-coded AI agents built using consultancies' proprietary frameworks are designed to be impossible for anyone but their own developers to understand, operate, and iterate.
It's even worse than using your own internal engineering team to create agentic AI. At least your internal team understands your business context.
EverWorker's platform makes creating, iterating, and modifying AI workers as simple as having a conversation. We don't create AI dependency—we make you AI capable. Resilient. Independent.
- Iteration takes minutes, not months
- Deploying AI workers is as simple as a button click
- System integration is as straightforward as connecting with an API key
- Change a model with one sentence: "Change the model to Claude on node 3"
- Creating vector databases happens with drag-and-drop file uploads
- RAG happens automatically
- Agent-to-agent communication doesn't require technical knowledge—you're just creating teams and defining job responsibilities, like you would with human employees
3. Time is Your Scarcest Resource
Think about your competitive landscape. The first company to truly become AI-native and empower their business users to create sophisticated AI workers will capture massive market share. We're witnessing a fundamental reshaping of what makes companies successful in the future. Companies like Meta are paying millions to poach the best AI engineers because they understand this reality.
But here's what they're missing: the bottleneck isn't AI engineering talent. It's the translation layer between business expertise and AI implementation.
Sarah's 24-month implementation timeline meant her competitors had two years to gain AI advantages while she waited for consultants to deliver. David's team was deploying AI workers within weeks and iterating based on real-world results while Sarah was still in requirements gathering sessions.
The fastest path to an AI workforce isn't through consultants—it's through a complete solution that includes an intuitive platform, training for your team to become AI-first, and guaranteed results. EverWorker delivers AI workers producing real business impact within eight weeks of kickoff.
4. The Expertise Inversion Problem
Here's the fundamental flaw in the consultancy approach: they position their technical AI frameworks and architectures as their unique value proposition. This falls victim to what we call "expertise inversion"—the backwards belief that technical complexity is more valuable than domain expertise.
The expertise inversion is a scam. The underlying technology behind AI workers is not complex and can be easily generated by modern LLMs. You don't need technical expertise anymore. You need a way to let your business operators—your domain experts—leverage their expertise through natural language and harness AI agents that create other agents on their behalf.
Sarah's consultants spent months building "breakthrough AI architectures" that any competent AI system could generate in hours. The real breakthrough would have been letting Sarah's team directly translate their marketing expertise into functional AI workers.
David's approach inverted the inversion. Instead of translating business expertise into technical specifications, his platform translates technical capabilities into business-friendly interfaces. The domain knowledge drives the AI creation, not the other way around.
The Market Has Spoken
The market is beginning to recognize this fundamental shift. Accenture just lost $60 billion in market value because investors finally realized what should have been obvious: in the age of AI, who needs consultants?
The expertise you need to win with AI already exists in your organization. Why pay millions for consultant-filtered versions of your own knowledge when:
- Your marketing director understands campaigns better than any consultant
- Your sales team knows prospects better than any external firm
- Your operations leaders understand workflows better than any Big 4 partner ever will
- Your finance team knows your numbers better than any outside analyst
- Your HR team understands your culture better than any workforce consultant
The Choice Every Executive Faces
Every business leader today faces Sarah and David's choice. The consulting reality offers $5-50 million budgets, 18-36 month timelines, and failure rates exceeding 80%—expensive translations of your own expertise built by people who don't understand your business.
The domain expert reality offers immediate deployment, complete control, continuous iteration, and AI workers that understand your business because they're built by people who do the work every day.
Sarah chose dependency. David chose capability.
Sarah became a customer of AI. David became an owner of AI strategy.
Sarah's team learned to manage vendors. David's team learned to create AI workers.
Three years later, Sarah is preparing for "Phase 3" of her AI transformation. David is preparing to capture market share from competitors still stuck in consulting cycles.
The Most Expensive Mistake in Business
Perhaps the most expensive mistake isn't hiring large consultancies to create custom-coded AI solutions. It's assuming your own people can't do it better.
What if the people best equipped to create your AI workforce are already on your payroll?
What if the marketing expertise that took your team years to develop is more valuable than any technical framework a consultant could build?
What if the future belongs to organizations that empower their domain experts to become AI creators, not AI customers?
The choice is yours. You can follow Sarah's path and remain dependent on expensive translations of your own expertise. Or you can follow David's path and discover what happens when the people who know the work best are empowered to build the AI workers that do it.
The technology exists today. The platform is ready. The only question is whether you'll choose dependency or capability.
Your competitors are making their choice right now. What will yours be?
Ready to turn your domain experts into AI workforce creators? EverWorker empowers business leaders to build sophisticated AI workers without technical dependencies or consultant middlemen. Discover how domain expertise becomes your competitive advantage in AI implementation.
Ameya Deshmukh
Austin Braham
Joshua Silvia
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