Artificial intelligence entered the workplace with a clear promise: efficiency.
Specifically, efficiency resulting from faster workflows, reduced manual effort, and better, more scalable outcomes. At the individual level, that promise is often real. Tasks that once took hours can now take minutes. From drafting to summarizing to analyzing, AI is undeniably accelerating work.
But when you take a step back and look at the organizational level, you start to see a different pattern. Many companies aren’t becoming more efficient. They’re becoming more fragmented, because unmanaged AI also introduces friction, risk, and inefficiencies.
That is all to say, efficiency doesn’t come from using AI. It comes from governing how it’s used.
The Hidden Inefficiency: When Everyone Moves Fast in Different Directions
In many organizations, AI adoption hasn’t been top-down; it’s been organic. Teams experiment. Individuals adopt tools. Departments solve for their own immediate needs. On the surface, this may look like progress. Underneath, however, it often creates issues because there is:
- No centralized visibility into what tools are being used
- Duplicate or overlapping technologies
- Inconsistent outputs across teams
- Unclear data handling practices
With all of that comes risks of sensitive data being entered into unknown systems, hallucinated or unreliable outputs being used without validation, and lack of clear standards for accuracy or human oversight.
The result is a paradox: what feels like efficiency at the individual level often creates inefficiency at the organizational level. Work gets done faster in silos, but it’s also being redone or requires detailed explanations later.
Why This Happens: AI Isn’t a Tool, It’s an Ecosystem
One of the biggest disconnects in AI adoption is how organizations conceptualize what they’re using. AI is often treated like traditional software: a single tool, purchased from a single vendor. But that’s not how AI actually works. In reality, AI is an ecosystem that includes: an application vendor (the interface your employees use), a large language model (LLM) provider powering the system, and multiple subprocessors handling data storage, infrastructure, and APIs.
When an organization only sees the surface layer, they evaluate the tool but not the system behind it. This disconnect creates a fundamental problem: you cannot run an efficient system if you don’t understand what’s actually in it. Without that visibility, you cannot fully assess risk, control data flow, or ensure consistent performance.
The Shift That Changes Everything: Governance Enables Efficiency
If visibility is the problem, governance is the clear answer. But “governance” is often perceived as a constraint that slows innovation, adds friction, and limits flexibility. In reality, however, the opposite is true. It isn’t slowing AI down, it’s allowing AI to scale without breaking.
Real efficiency requires:
- Consistency: the same inputs produce reliable outputs
- Repeatability: processes can be used across teams, not reinvented
- Trust: users understand when and how to rely on AI
- Transparency: leadership can explain how AI is being used
None of that happens organically. Organizations that are getting this right are doing a few key things. They’re assigning clear ownership for AI governance. They’re inventorying AI tools and mapping use cases. They’re classifying risk across those use cases. They’re establishing policies around data use, validation, and oversight. And they’re training employees on both the capability of AI and using AI responsibly.
External Partners: Efficiency Multipliers or Complexity Creators?
Keep in mind, however, that if an organization’s governance framework only covers internal practices, it’s leaving some of the biggest inefficiencies (and risk) unmanaged. Very few organizations can operationalize AI alone. They rely on that ecosystem of partners (application vendors, LLM providers, consultants, and legal and compliance experts).
At this level, efficiency often breaks down for a few reasons. There may be misalignment between vendor capabilities and organizational needs. The organization can have a lack of clarity around data usage and model training rights. Rather than relying on contractual protections, organizations can over rely on demos instead. Finally, fragmented communication across internal and external stakeholders can cause inefficiencies.
Organizations that maximize efficiency treat vendor management as part of governance and not a separate activity. What does that mean, practically speaking? Defining clear roles and expectations between internal teams and external partners. Centralizing communication to avoid conflicting guidance. Scrutinizing contracts and not just the tool’s functionality. Understanding where data is stored, how it’s used, and who has access to it. And, maintaining internal accountability, even when expertise is external.
Ultimately if you don’t understand your vendors, you don’t understand your AI. And if you don’t understand your AI, you can’t operate efficiently or safely.
What Efficient AI Actually Looks Like
An efficient AI-enabled organization doesn’t necessarily use the most tools, but rather, it uses them intentionally. In practice, that means such an organization:
- Knows what AI tools are in use across the enterprise
- Classifies use cases based on risk and impact
- Has clear, documented policies governing AI use
- Applies consistent oversight and validation standards
- Actively manages vendors and third-party risk
- Trains employees in both usage and accountability
- Can clearly explain its AI practices to leadership and/or regulators
Notably, the final bullet point matters more than many realize because increasingly, the question isn’t just “Are you using AI?,” it’s: “Can you demonstrate control over how you’re using it?”
Efficiency Is a Governance Outcome
There’s no question that AI will continue to accelerate work. What is in question is whether that acceleration translates into real organizational value or just faster chaos. Speed without structure creates drag due to risk exposure, inconsistent outcomes, and loss of trust.
In short, the organizations that benefit most from AI won’t be the ones that adopt it fastest. They’ll be the ones that operationalize it most effectively.
Emily Fedeles Czebiniak
Author
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Emily is an attorney with more than 14 years of experience across litigation, eDiscovery, privacy, cybersecurity, and artificial intelligence. At TCDI, she helps shape the company’s technology strategy, focusing on responsible AI governance and the integration of generative AI into innovative workflows. A recognized thought leader in technology and data, Emily regularly shares her insights through speaking engagements and published work.
