The Hidden Costs of AI Adoption in TPRM
INTRODUCTION
The AI gold rush is on. Every vendor is adding it, boards are demanding it, and teams are scrambling to deploy it, and to defend against it.
But in the rush to keep up, few stop to ask what all this AI is really costing.
The pressure to “do something with AI” often overshadows the financial and operational realities that come with it. AI isn’t just another software subscription. It introduces new forms of cost and complexity that many third-party risk management (TPRM) programs don’t account for upfront.
The programs that succeed with AI will be the ones that have a clear view of the total cost of ownership before investing, understanding where hidden costs often arise and how to manage them.
Beyond the Sticker Price: 4 Hidden AI Costs
The biggest costs of AI don’t appear on a purchase order. They surface over time, from the oversight AI demands to the infrastructure it relies on. In TPRM, these hidden expenses fall into four main categories:
Governance and oversight
The steepest cost of AI adoption often isn’t the technology itself, it’s everything required to govern, deploy, and use it responsibly.
Most organizations are still playing catch-up. When I asked a room of 100 CIOs last year how many had an AI governance policy, only 10 hands went up. If I asked today, maybe 50 or more would, but that’s still far too few, and that doesn't account for the fact that having governance policy is only part of the battle.
Meanwhile, vendors keep rolling AI into their products without explaining where it’s used, why, or what value it adds. Internally, many teams deploy AI without defining success, clarifying data boundaries, or assigning accountability. Too often, decisions about ethics, explainability, and oversight happen after deployment (if ever) instead of before.
And because your vendors are doing the same, that governance gap extends across your third-party ecosystem. Every supplier that embeds AI into its tools introduces new, and constantly changing, risks. If your vendors don’t have clear policies for validating and documenting changes, you inherit uncertainty you can’t see or control.
Some companies have created AI review boards only to discover how resource-intensive real oversight is. Getting security, legal, compliance, and business leaders aligned on accountability can take more time than the technology ever saves. Oversight is essential, but it’s rarely efficient.
Misinformation
Hallucinations, bias, and inconsistent outputs are foibles of how large language models (LLMs) operate. But in TPRM, those errors can have severe consequences. If an AI tool misinterprets a vendor’s security data or flags the wrong risk, you could spend weeks chasing false positives, or worse, miss a serious issue.
Most AI systems are non-deterministic, meaning the same input can produce different results each time. That unpredictability complicates audits and accountability. How do you justify a vendor risk decision when the model gives a different answer tomorrow?
Bias in AI models compounds the problem. Ask an image generator like Midjourney or ChatGPT for “a group of professionals,” and you’ll often get the same narrow demographic, usually all white men. In TPRM, biases can creep into vendor scoring, weighting certain factors unfairly and masking real areas of risk.
There’s also the issue of misplaced confidence. When an AI model doesn’t know the answer, it often gives you one anyway, and does it with bluster, bravado, and a certainty that isn’t warranted. In a risk context, that can mean basing vendor decisions on fabricated intelligence.
AI can process data faster than any analyst, but it can’t replace human judgment. The real cost of misinformation isn’t just the error, it’s how bad intel distorts decision-making.
Technology and compute
Running AI at scale isn’t cheap. It requires massive compute power, and those costs are climbing fast. We’ve already seen it with all of the major platforms: demand for high-performance processing drives up prices, and vendors inevitably pass those costs on. What starts as an affordable tool can quietly become a budget-eating dependency.
Once an AI-powered tool becomes part of your ecosystem, it’s hard to unwind. The workflows you build around it make switching vendors costly and disruptive. If a vendor hikes prices or changes its licensing terms, you're stuck with two bad options: pay more or start over. It’s the same mistake many companies made in the early cloud era, when they discovered too late they’d traded ownership for perpetual rent.
AI also generates enormous volumes of data, logs, prompts, training inputs, audit trails, all of which must be stored, protected, and analyzed. That adds ongoing storage and management costs that many TPRM teams don’t plan for.
And that’s before you even look at the environmental impact. Running large AI data centers takes huge amounts of power and water. Those costs might not show up in your budget today, but emerging sustainability standards and regulations could soon push them into your total cost of ownership.
For TPRM leaders, understanding how vendors handle compute, storage, and sustainability isn’t just due diligence, it’s part of financial risk management. The price of AI isn’t only what you pay to access it, it’s also what you’ll spend to sustain it.
Architecture and optimization
One of the most overlooked costs of AI comes from building it the wrong way. Poor design choices made early, by vendors or internal teams, can drive up expenses and reduce long-term value.
Think back to early cloud migrations. Many companies “lifted and shifted” workloads without re-architecting, only to find they were paying more without gaining scalability or resilience.
The same thing is happening with AI. Vendors are bolting AI onto legacy products without optimizing how they’re trained, integrated, or governed. That shortcut might get features out the door faster, but it often creates fragile systems that cost more to maintain than they’re worth.
At Black Kite, we’ve taken a different approach. AI isn’t an add-on. It’s been part of the platform’s design from the start. It works behind the scenes to improve how risk data is gathered, analyzed, and connected. That foundation helps teams identify issues faster and interpret vendor information more efficiently.
How To Keep AI Costs Under Control
You don’t need to hit pause on AI, you just need to go in with eyes open. That starts with asking the right questions. Before you invest, or let a vendor sell you on “AI-powered” anything, ask them:
- What do you do with AI that you couldn’t do before?
- What do you do better with AI?
- What do you do with AI that your competitors don’t?
If a vendor can’t answer those clearly, walk away. They’re not using AI to create value, they’re using it as a buzzword. And buzzwords don’t improve risk outcomes, they just inflate costs.
Once you’ve separated hype from value, focus on visibility. Ask vendors where and how they’re using AI, what data it relies on, and whether that data changes over time. Request their AI governance policies and verify their practices match their claims. For high-impact decisions, keep human oversight in place to validate AI-generated insights.
Frameworks like Black Kite’s Global Adaptive AI Assessment (BK-GA³™) can also help give organizations a standardized way to evaluate how AI is deployed across their vendor ecosystem without adding unnecessary overhead.
Making AI Worth the Investment
AI can be a powerful tool for third-party risk teams, but you need to understand what it takes to make it work. The idea isn’t to pull back on AI, it’s to make sure the investment actually delivers value.
Good governance takes people and time. Unchecked outputs can create more noise than insight. Compute and storage costs add up quietly. And when AI is tacked onto legacy systems without a plan, the promise of efficiency turns into overhead. These aren’t reasons to avoid AI, they’re reminders to manage it intentionally.
The most effective TPRM programs don’t chase every new feature labeled “AI-powered.” They focus on visibility, accountability, and reliable data, the foundations of good decision-making. That’s what turns AI from a marketing claim into an advantage.
When AI is built thoughtfully into the foundation of a platform, as it is at Black Kite, it simplifies instead of complicates. It improves data accuracy, reduces manual analysis, and gives teams a clearer, more dependable view of vendor risk. Done right, AI delivers on its promise, without adding hidden costs along the way.
Ready to turn AI promises into real value? Explore Black Kite AI to learn how we put AI to work to empower your TPRM team.