Hiring for AI? Don't make These Expensive Mistakes with Richard Doran

Hiring for AI? Don’t Make These Expensive Mistakes

A Leadership Briefing with Richard Doran, Founder & CEO, Sierra ITS

Executive Summary

As organizations move quickly to adopt AI, many are making a series of avoidable and expensive mistakes. The issue is not a lack of urgency. It is how that urgency is being applied.

In this Leadership Briefing, Richard Doran walks through the most common hiring missteps he sees across the market. From starting with org charts instead of outcomes to overbuilding teams too early, these decisions slow progress and drive unnecessary cost. Leaders who take a more focused approach are able to move faster, spend less, and generate real business value.

The Pattern: Hiring Before Clarity

One of the most common patterns Richard sees is organizations jumping straight into hiring.

The thinking is straightforward. AI is a priority, so the next step must be to build a team. The problem is that many of these organizations have not clearly defined the business challenge they are trying to solve.

Without that clarity, hiring becomes reactive. Roles are created based on assumptions rather than outcomes, and teams are assembled without a clear direction. This often leads to a lot of activity but very little measurable impact.

Richard consistently brings the conversation back to a simple starting point. Define the outcome first. Everything else should follow from that.

Mistake 1: Starting with Org Charts Instead of Outcomes

When leaders begin with structure instead of results, they create misalignment from the start.

Job titles, reporting lines, and team size become the focus. Meanwhile, the actual business problem remains loosely defined. This creates confusion around priorities and makes it difficult to measure success.

A more effective approach is to start with the outcome the business is trying to achieve. Whether that is reducing cycle time, lowering cost, improving accuracy, or driving revenue, the goal should be clear before any hiring decisions are made.

Mistake 2: Overhiring Too Early

Another issue Richard sees frequently is overhiring at the beginning of an initiative.

Organizations assume they need a full team to get started, often bringing in five to ten people before the scope of the work is fully understood. In reality, many AI-driven initiatives can be validated and executed with a much smaller group.

When too many resources are applied too early, it increases cost and adds complexity. It also makes it harder to adjust direction as new insights emerge.

In many cases, a focused team of three to five people can achieve the same outcome more efficiently.

Mistake 3: Ignoring Internal Talent

A third mistake is overlooking the people already inside the organization.

Most companies already have individuals who understand the business, the workflows, and the operational challenges. That institutional knowledge is critical when identifying where AI can actually create value.

Instead of replacing that capability, strong leaders build around it. They combine internal expertise with targeted external support to accelerate progress.

What Works Instead: AI-Capable Leadership

Rather than building a team first, Richard emphasizes the importance of starting with the right leader.

An AI-capable leader brings both domain experience and technical understanding. They can translate business needs into practical use cases and guide the organization toward the right solutions.

Once that leadership is in place, the path forward becomes clearer. The organization can define use cases, select the appropriate tools, and build a team that is aligned to a specific objective.

A Practical Example

Richard shared an example of a healthcare organization that initially planned to hire an AI expert immediately.

Instead of moving forward with that plan, the focus shifted to defining the business and operational challenges first. Once those were clear, the organization brought in a contract resource to work on specific use cases.

Within a few months, they had a working solution that the business supported. With that foundation in place, they were able to hire a full-time leader to own and expand the effort.

This approach reduced risk, controlled cost, and created a much clearer path to scale.

Speed Still Matters

While clarity is critical, timing also plays a role.

Top AI talent is in high demand, and it does not stay available for long. Richard points out that once the right person is identified, organizations need to move with intent.

Delays can result in missed opportunities, both in terms of talent and business progress. The goal is not to rush blindly, but to move decisively once the strategy is clear.

How to Approach AI Hiring Moving Forward

The most effective approach starts with defining the business problem and the outcome you are trying to achieve. From there, leaders should identify where AI can create measurable value across time, cost, risk, or revenue.

The next step is to secure the right leadership. Someone who understands both the business and the technology can guide the initiative and align the team.

Only after those elements are in place should hiring expand. Even then, the focus should remain on precision rather than scale, building a team that is aligned to clearly defined objectives.

Key Takeaways for Technology Leaders

Start with outcomes, not org charts

Avoid overhiring before the problem is defined

Leverage internal talent and institutional knowledge

Hire AI-capable leaders who understand both business and technology

Move quickly once the right talent is identified

Closing Thoughts

AI is moving quickly, and organizations feel pressure to act. The difference between success and failure often comes down to how that action is taken.

Leaders who prioritize clarity, align the right expertise, and stay focused on outcomes will move faster and create lasting value.

Learn more about Richard Doran and watch all of his Leadership Briefings on his channel here.

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Originally recorded as Hiring for AI? Don't Make this Expensive Mistake)