Governance Is the Missing Layer in AI Automation
Governance has become one of the most urgent topics in the corporate world now that artificial intelligence is making decisions at machine speed.
And it makes sense: when you accelerate everything at once, the wins get bigger, but so do the mistakes.
The problem is that a lot of companies still treat governance as an afterthought, a complement, a parking brake only pulled when something has already gone wrong.
Spoiler: that timing does not work anymore. 🚨
Louise K. Allen, Chief Product Officer at Planview, raises exactly this point in an article originally published in Inc. in June 2026. Her thesis is straightforward: more speed demands more control, not less. And we are not talking about bureaucracy or freezing up processes, but something quite different: building the right rails so that automation can run without going off track.
Allen has a background that gives context to her perspective. Before entering the corporate technology world, she was a professional tennis player. And it was precisely in that high-level competitive environment where she learned something fundamental: rules and boundaries, when properly understood, are not obstacles to performance. They are what allow you to play better, with more confidence and less risk of making costly mistakes. That principle, according to her, applies just as much to a tennis court as it does to an enterprise automation framework powered by AI.
It might sound contradictory, but it is not. That is exactly what we are going to talk about here. 👇
When Speed Becomes a Problem
Automation with artificial intelligence delivers something no human team can achieve on its own: real-time scale. An AI model can analyze thousands of variables, make decisions, and execute actions in fractions of a second, while the team is still opening the first meeting of the day. That is powerful. But that same power is what turns a small error into a massive problem before anyone realizes what is happening. Speed amplifies everything, including failures.
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As Allen points out in her article, the AI era has opened unprecedented doors for businesses, allowing them to create, build, and iterate at an unheard-of pace. On the surface, this looks like an absolute win. Speed, agility, and flexibility have always defined operational success. But those advantages only matter if you know where you are headed. Without a clear destination, more options just mean more chances to take the wrong path. And when decisions are made at AI speed, the risk of getting it wrong multiplies proportionally.
The point Louise Allen makes is precise: companies adopting AI without a solid governance structure are essentially putting a Formula 1 engine in a car with no brakes. The agility that technology offers stops being a competitive advantage and turns into a source of operational, financial, and even reputational risks. And the most dangerous part is that these risks often only surface after the damage has already been done.
This does not mean companies should slow down AI adoption. Far from it. What needs to happen is a mindset shift: governance is not the opposite of agility. It is what makes agility sustainable. Without that foundation, any speed gains come with a short expiration date, because sooner or later a process will go off the rails, and the cost of fixing things is usually far greater than the cost of preventing them.
Governance Is Not Bureaucracy — It Is Infrastructure
When someone mentions governance inside a company, the first image that almost inevitably comes to mind is stacks of documents, endless approvals, and processes that take weeks to move forward. That stereotype exists for a reason: in many corporate environments, governance has become synonymous with slowness. But that association is misguided, especially when the subject is artificial intelligence and automation.
Allen describes herself as a longtime advocate of governance as an accelerator. It is a perspective she built over the course of her career, and one rooted in her experience as an athlete. She shares that she was used to operating within rule sets that seemed to be holding back her performance. But she learned early on that accepting those parameters and learning to excel within them made her a better player, a tougher opponent, and a more reliable teammate. She carried that principle into the technology world, and today she watches her peers learning this lesson the hard way.
Think of it this way: when a development team has well-defined code review processes, deployments happen with more confidence and less rework. When a finance team has clear approval controls, investment decisions flow with greater security. The logic is the same for AI. When models have clear operating criteria, well-defined boundaries, and active monitoring mechanisms, automation runs much more efficiently, because there is less room for ambiguity and far more clarity about what can and cannot be done without human oversight.
What Allen advocates is exactly this shift: stop seeing governance as an obstacle and start treating it as infrastructure. Just as no one builds a skyscraper without a foundation just to finish faster, no company should scale AI processes without a control structure that keeps pace with that scale. The risks of skipping this step are not hypothetical. They are concrete, measurable, and in many cases already showing up at companies that bet everything on speed and forgot about control.
Traffic Lights and Speed Bumps Are Features, Not Flaws
One of the most interesting analogies Allen uses in the original article is the one about traffic signals. She says that good governance creates bottlenecks where they are needed. Stop signs and red lights are features of the system, not flaws. They force you to slow down. And slowing down, at certain moments, is what prevents a collision.
Applying this to the context of automation with AI, the logic holds perfectly. Autonomous agents and analytics tools operating at scale need checkpoints along the way. Without those checkpoints, a system can scale a bad decision to thousands of customers in a matter of minutes. With them, there is an opportunity to spot the deviation, correct course, and prevent the impact from spreading.
Allen acknowledges that intentionally adding friction to systems designed to simplify and accelerate operations seems counterintuitive. It might even feel like it goes against the principles of good business. But she is emphatic in stating that governance does not mean stagnation. It means having the intelligence to know where to accelerate and where to pause, even for just a moment, to make sure everything is on track.
This point is particularly relevant for companies beginning to work with AI agents, those systems that do not just analyze data but take concrete actions in the real world. When an agent can send emails, process payments, adjust pricing, or reallocate resources without human intervention, the acceptable margin of error shrinks dramatically. And it is precisely in these scenarios that governance becomes not just useful but absolutely essential.
The Real Risks of AI Without Guardrails
Talking about AI risks can sound abstract for those still in the early stages of adopting the technology, but the real-world examples are everywhere. Automation systems without adequate oversight have already caused pricing errors on products, biased credit decisions, inappropriate customer service responses, and even failures in critical logistics processes. In every one of these cases, the problem was not the AI itself but the absence of control mechanisms that could identify and correct deviations before they caused real impact.
There are at least three categories of risks that any company needs to consider when scaling artificial intelligence without a robust governance layer:
- Operational risks: automated processes making wrong decisions at scale, generating rework, financial losses, or cascading failures that spread across different areas of the business.
- Regulatory risks: the legal landscape around AI is evolving rapidly, especially in Europe and emerging markets. Companies without clear governance are exposed to sanctions and fines that can be significant.
- Reputational risks: when an AI system makes a problematic decision and it goes public, the damage to the company’s image can be far more costly than any efficiency gains the technology delivered.
What connects all three categories is speed. The faster AI operates without proper controls, the faster those risks materialize. And the window to intervene before the problem escalates is much smaller than most companies realize. That is why governance needs to be built alongside automation, not after it.
Agility and Control Can Coexist
The big shift that Louise Allen’s article proposes is recognizing that agility and control are not opposing forces. They are complementary when properly structured. A company that clearly defines which decisions AI can make autonomously and which ones require human review is not slowing down automation — it is calibrating it to operate at peak efficiency within a safe perimeter. And that perimeter can be adjusted as confidence in the systems grows and models prove themselves reliable over time.
In practice, this translates into a few concrete actions that AI-mature organizations are already implementing:
- Decision mapping: understanding which processes have been automated, how often they are triggered, and what the potential impact of an error would be in each one.
- Continuous monitoring: deploying alerts and dashboards that make it possible to spot anomalies before they escalate into bigger problems.
- Clear accountability: someone needs to be responsible for every AI system in production, because technology without a business owner is a recipe for chaos.
These practices do not make operations slower. They make them smarter.
Governance as a Competitive Advantage
What Allen highlights, and what resonates far beyond the Planview context, is that the companies that will win in the artificial intelligence era will not necessarily be the ones that adopt the technology the fastest, but the ones that manage to maintain agility with consistency over time. And consistency requires governance. It requires processes. It requires clarity about where the machine decides on its own and where a human still needs to be in the loop.
The complaints about the speed bumps that guardrails can introduce into workflows are understandable. But they miss the bigger picture. A company that invests in governance from the very start of its AI journey is not spending resources on bureaucracy. It is building the foundation that will allow it to scale safely, maintain customer trust, and adapt quickly when regulations inevitably get stricter.
That balance is not a technological limitation. It is a strategic choice. And the companies that make this choice deliberately will be far better positioned to scale without losing their way.
At the end of the day, Allen’s message is simple and powerful: more automation demands more brakes, not fewer. Governance is the missing layer in most AI strategies. Formalizing, documenting, and embedding these controls into every process is not optional. It is what separates the companies that will thrive in the AI era from those that will learn this lesson the most painful way possible. 🎯
