AI Adoption Isn't a Tool Problem. It's an Operator Problem.
Most organizations approach AI adoption the same way they approached the internet in 1999: they buy access to a tool, assign someone to "figure it out," and wait for the productivity gains to materialize. When they don't, leadership concludes the technology wasn't ready, the vendor oversold it, or their industry is somehow different. Marnie Wills has spent years watching this cycle repeat — and her diagnosis is blunt. The technology is not the problem. The operator is.
Wills joined Jason Wade on the BackTier podcast to unpack what AI adoption actually looks like when it works, what separates organizations that compound their output from those that stall, and why the most dangerous thing a company can do right now is hand employees personal AI accounts and call it a strategy.
From "AI Translator" to Hands-On Builder
Wills describes her positioning as an "AI adoption translator," but the label undersells the work. She is not a consultant who delivers slide decks and frameworks. She builds. Custom internal tools. Podcast repurposing applications. Marketing copilots. Funding research assistants. The translation she performs is not between humans and AI — it is between what a business actually needs and what the technology can actually do, right now, without waiting for some future version.
This distinction matters because the consulting market is flooded with people who can explain AI at a conceptual level but have never shipped a working tool inside a real organization. Wills comes from the other direction. Her clients do not just understand AI adoption in the abstract. They leave with systems they can operate, maintain, and evolve themselves.
The entry point for many of her clients is what she calls "vibe coding" — the practice of using natural language to build functional software without traditional programming skills. A business owner describes what they need. The AI generates the code. Wills teaches them to iterate, debug, and own the result. The outcome is not just a tool. It is a capability. The client now knows how to build the next one.
AI Exposes Weak Operators. It Does Not Create Them.
One of the sharpest observations in the conversation is Wills' framing of what AI actually reveals about an organization. Teams that struggle with AI adoption are not struggling because the technology is hard. They are struggling because AI amplifies whatever was already true about how they operate.
If a team lacks clear decision-making processes, AI makes that ambiguity more expensive. If a team has weak documentation habits, AI tools that depend on institutional knowledge surface that gap immediately. If leadership has never built a culture of experimentation, the pressure to "use AI" lands as another compliance requirement rather than a genuine capability-building initiative.
Conversely, high-functioning operators use AI to compound what they already do well. A team with strong processes gets faster. A team with clear documentation gets more leverage from every knowledge base they build. A team with a culture of experimentation moves through the learning curve in weeks instead of years.
This is the core of what Wills calls "Amplified Intelligence" — not replacing human capability, but increasing it to expand overall business capacity. The word "amplified" is doing real work here. An amplifier does not change the signal. It makes it louder. If the signal is weak, amplification makes the weakness louder too.
The Intellectual Property Problem Nobody Is Talking About
The conversation takes a sharp turn when Wills raises the intellectual property risk embedded in how most companies currently deploy AI. The scenario is common: a company encourages employees to use AI tools to improve their productivity. Employees sign up for personal accounts on ChatGPT, Claude, Gemini, or whatever tool they prefer. They build custom instructions, upload company documents, create projects, and develop workflows that make them significantly more effective.
Then the employee leaves.
Everything they built — every custom instruction, every project, every knowledge base, every workflow — leaves with them. The company has no access to it. The institutional knowledge that was generated using company time, company data, and company context now belongs to whoever holds the personal account.
Wills argues this is not an edge case. It is a structural problem that most organizations have not yet recognized because the tools are still new enough that the turnover cycle has not fully played out. The fix is not complicated, but it requires intentionality: shared organizational accounts, centralized knowledge bases, documented workflows, and clear policies about where AI-generated work product lives. The companies that build this infrastructure now will not lose their AI capability every time a key employee walks out the door.
How to Actually Use AI Tools (Not Which Ones to Use)
The tooling conversation in this episode is notable for what it does not do. Wills does not rank AI platforms or declare a winner. She does not spend time on the question of whether Gemini is better than Claude or whether Perplexity is replacing Google. That framing, she suggests, misses the point entirely.
The question is not which AI is best. The question is how you are using it.
Her framework centers on three practices that separate effective AI users from ineffective ones. The first is working inside projects rather than isolated chat sessions. Projects maintain context across conversations, allow for shared knowledge bases, and create a coherent environment where the AI understands your work rather than starting from scratch every time. The second is building and maintaining connected knowledge bases — uploading relevant documents, guidelines, and context so the AI is working with your actual information rather than generic training data. The third is treating AI tools as systems that require ongoing maintenance, not set-and-forget utilities.
That last point leads to one of the most practical concepts in the conversation: Wills' monthly "AI fine-tuning" process. Once a month, she reviews the custom instructions she has given her AI tools, cleans up outdated context, and updates the system to reflect how her own thinking and processes have evolved. This is not a minor housekeeping task. It is the practice that keeps the AI useful as the user improves. Most people set up their AI tools once and never revisit the configuration. Wills treats it the way a serious athlete treats their training program — as something that needs to evolve as capability grows.
Leadership's Role in AI Adoption
The conversation addresses something that most AI adoption frameworks skip entirely: what leadership actually needs to do differently. Not at the policy level — most organizations have already written AI policies. At the cultural level.
Wills is direct about what she sees in organizations where AI adoption stalls. Leadership is treating AI as a cost-cutting tool rather than a capability-building one. The mandate comes down as "use AI to do more with less," which immediately frames the technology as a threat to headcount rather than a lever for better output. Employees respond by using AI minimally, superficially, or not at all — not because they are resistant to technology, but because they are rational actors responding to the incentive structure they have been given.
The organizations that get real results from AI adoption do something different. They create space for experimentation. They allow for the learning curve. They measure success by the quality of output rather than the speed of cost reduction. They treat AI capability-building as a competitive advantage rather than a compliance exercise.
This requires a specific kind of leadership behavior: the willingness to let teams try things that might not work, to invest in training that does not produce immediate ROI, and to accept that the path from AI curiosity to real operational change is not a straight line. Most leadership teams are not structured to do this. The ones that figure it out are the ones pulling away from the competition.
Why Wills Does Not Do "Done-For-You" AI
The final thread in the conversation is Wills' deliberate decision to avoid the "done-for-you" AI services model that has become common in the market. The pitch is familiar: hire us, we will build your AI systems, you will get the results without having to learn anything. Wills rejects this model, and her reasoning is worth understanding.
If an external team builds your AI systems, you do not own them in any meaningful sense. You cannot maintain them. You cannot evolve them as your business changes. You cannot train new employees on them. You are dependent on the vendor for every update, every fix, every iteration. The moment the relationship ends, the capability disappears.
Her model is the opposite. She teaches clients how to build and manage their own systems. The process takes longer. It requires more from the client. But the result is a business that has genuinely internalized AI capability — not one that has rented it. The operators inside the business become stronger. The systems they build reflect their actual workflows. The knowledge stays inside the organization.
This is a meaningful distinction in a market where most AI services are structured to create dependency rather than capability. Wills is building clients who will eventually not need her for the basics — and that is precisely the point. The measure of success is not ongoing engagement. It is the client's ability to operate independently at a higher level than they could before.
What This Means for AI Visibility
The conversation with Marnie Wills is not primarily about AI visibility in the search and citation sense. But there is a thread running through it that connects directly to how BackTier thinks about brand authority in AI systems.
The organizations that build genuine AI capability — shared knowledge bases, documented workflows, structured systems — are also the organizations that generate the kind of institutional knowledge that AI engines can cite. They produce content that reflects real expertise. They build entities that are coherent and well-documented. They create the conditions under which an AI model, asked about their category, has something specific and credible to say about them.
The organizations that treat AI as a collection of individual productivity tools, with no shared infrastructure and no institutional memory, produce the opposite. They are invisible to AI engines not because they lack expertise, but because that expertise has never been made legible.
Wills is building operators. BackTier is building the infrastructure that makes those operators visible. The work is different. The underlying logic is the same.
Listen and Connect
This episode of the BackTier podcast is available on [Spotify](https://open.spotify.com/episode/2W7wsr25vHirHJwJf31oHP?si=iuXj0lfWQYWKJ5EQ6orhhw). Marnie Wills works with business leaders and teams on AI adoption at the systems level. You can reach her at [businesswithaistrategist.com](https://businesswithaistrategist.com/) and connect with her on [LinkedIn](https://www.linkedin.com/in/marnie-wills-entrepreneur/).
If the gap between AI curiosity and real operational change is something your organization is navigating, [request a free AI visibility audit from BackTier](/contact). The audit identifies where your brand stands in AI engine knowledge bases and what structural changes would move the needle.