What the f**k is a CAO and do I need one?

An attempt to address the mysteries around a new technology leadership title from someone who holds it.

Let’s address the elephant in the boardroom. There is, yet again, a new acronym popping up on LinkedIn and in pitch decks. You know how it goes. Someone from the board sees this new title and starts asking questions, “Do WE need one of those?” Maybe your board has even asked if you have a strategy for it:

The CAO (Chief Automation Officer).

The questions start. Is it a glorified CTO? A prompt engineer with a C-suite title? Or just another expense line item?

While the questions are easy to ask, the answers may not flow as freely. In fact, if you ask ten people, you’ll get eleven answers. But if you want the answer that actually impacts your P&L, I’ve got one for you: The CAO is the Chief Enabler.

We’re going to explore what that statement, “the Chief Enabler” actually means, how we assess the ensuing chaos, and provide the litmus test for whether your company actually needs one.

The Role: The Bridge Between Hype and Reality

The gap between “AI can do everything” and “AI is actually useful for us” is massive. There are big questions to answer here and there can be extensive resources required to implement. The CAO’s job isn’t to write code all day; it is to assess feasibility. In other words, to answer the questions of need, practicality and operational benefit and then figure out if it can be done with automation. We look at a business problem and decide if AI is the lever that moves the boulder, or if we just need a better spreadsheet.

In my role at BforeAI, I am looking for ways to both impact internal processes that help the team work better, faster, and more efficiently. Externally, I am looking for pathways to automating our predictive security PreCrime platform in new ways, by improving collation, prioritization, and reporting capabilities. In either case, as mentioned above, each potential opportunity for automation requires a feasibility assessment to determine if automation is necessary, helpful, and ultimately drives some sort of benefit. Whether it’s internal or external, at the end of the day, everything is an effort to deliver a better customer experience, so this is the perspective we take when we attempt to answer these questions.

The Methodology: How We Assess Projects

When a someone comes to me with a “great idea” for an AI feature, I filter it through two lenses:

• Deterministic: Input A leads to Output B. The operation in question is predictable, reliable, and safe (e.g., classifying support tickets).
• Agentic: The AI is given a goal and figures out the path on its own. These operations are autonomous, powerful, and also higher risk (e.g., a research agent that browses the web and synthesizes markets).

Simply put, the CAO’s job is to stop the company from using expensive agentic workflows for problems that require simple, deterministic solutions. Conversely, the CAO also ensures that complex, novel problems aren’t being forced into brittle, non-adaptive, deterministic code.

The Tools: Code vs. No-Code

There is a misconception that “serious” tech requires custom code for everything. That is a fast way to burn out your engineering team.

• No-Code solutions are great for minimally viable products (MVP), internal efficiency tools, and testing hypotheses fast. If we can solve a problem in 48 hours with a drag-and-drop tool, writing Python script is a waste of resources.
• Coded solutions should be reserved for core IP, high-scalability needs, and production environments where security and latency are non-negotiable.

A good CAO doesn’t prioritize the tech stack; they prioritize the speed to value.

Measuring Impact: The Money Question

So, let’s say your leadership team determines that having someone manage your AI strategies is a pressing need. How do you measure the ROI of this role? The measured impact usually falls into two buckets, and you need to know which one you are chasing:

• Cost Savings impact a direct reduction in manual hours.
  • Example: Automating tasks that were previously manual and time-consuming.
• Cost Avoidance impacts the money you don’t have to spend.
  • Example: Automating a workflow so you don’t have to hire five more junior analysts next quarter.

If your AI initiatives aren’t hitting one of these buckets, you are just doing R&D.

The Kill Switch: When to Say Goodbye

The hardest part of all this is knowing when to quit. AI projects rot. Models drift. Data changes. A project that was brilliant six months ago might be a liability today. For perspective, at BforeAI, our projects have a Time to Live (TTL) of three months with the possibility of renewal or migration into more stable services if they seem like they are delivering value.

Part of the “maintenance program” is knowing when to sunset a project. A CAO needs the discipline to look at a feature that took months to build and say, “This is no longer serving us. Kill it.”

The "Build vs. Buy" Litmus Test: The PreCrime™ Case Study

Another quandary in this process is answering yet another question: Do we build this functionality ourselves or buy it? To bring this down to earth and give an example, let’s look at brand protection.

Protecting an organization’s brand is a lot of work. It is a high volume activity, with loads of potential false positives. The threats are generally lower level, which means it can be easy to overlook and miss a nasty one. Traditionally, it is also an extremely manual process, even with tech solutions doing some of the work.

Every company probably thinks at some point, “I can just build an internal tool to monitor my brand, right? It’s just keyword matching.”

Technically? Sure, it may be possible. Strategically? It’s a trap.

Let’s use PreCrime as an example. When would a security leader choose an automated prediction and takedown solution like ours, versus doing it manually?

For most companies (90+%), trying to “do” brand protection in-house is a nightmare of data ingestion and scalability issues. It distracts your engineering team from focusing on your core product or capabilities, and gives you more headaches by forcing you to work with your legal department. This is where the CAO steps in and says: Don’t build this. Buy PreCrime.

PreCrime is an automated predictive security platform that manages everything from the prediction of malicious infrastructure (not just domains), to disrupting web traffic to said infrastructure, and performing the takedown; all at a scale that would otherwise drown your internal team. Let’s be clear, this is a repeatable, but time-consuming and nuance-filled operation to perform manually. Making it a great candidate for intelligent automation. It keeps your security team focused on areas of high impact, not on playing whack-a-mole with scammers.

Now we come back to the original question: Do you need a CAO?. In my honest opinion, it depends on your technology strategy. If your company views AI as a neat add-on or a marketing gimmick, save your money. In most cases, investing in some relatively inexpensive, pre-built tools that will meet most of your needs without overengineering the process.

But, if you are looking to scale up fast, solving complex operational bottlenecks, or the potential to automate core parts of your business model—and you don’t know the difference between a deterministic and agentic approach—then you are going to need help. This is when a CAO might be the next logical hire into your team

And, maybe, that is the reason why top Fortune 2000 companies choose BforeAI to protect their brands from impersonation abuse by cybercriminals. These businesses are not on the search to become the latest hip tech company that does it all. They just need an automated solution that can perform these duties at scale. As an expert in automation, take it from me, PreCrime is the way to go for that.