It is one of the most common questions business leaders are asking today: “We want to start using AI in our company. Do we need to hire an AI leader, developer, or engineer to make that happen?”

The short answer? Not necessarily.
The long answer is a bit more nuanced, and it is where many organizations get tripped up.

The Rush to “Do Something with AI”

Across industries, leaders feel a growing sense of urgency to do something with AI. They see competitors experimenting with automation, hear stories about teams saving hundreds of hours with chatbots and data assistants, and wonder if they are already behind.

That urgency is understandable. McKinsey estimates $4.4 trillion in annual productivity growth is up for grabs from corporate AI use cases, and 92 percent of companies plan to increase their AI investments over the next three years. Everyone wants to move quickly, and no one wants to be left behind.

This often leads to a quick, almost reflexive next step: hire someone.
A Head of AI, Chief AI Officer, AI Engineer, or Data Scientist. The titles vary, but the thinking is the same: “We need an expert in-house who can make AI happen.”

It sounds logical until the job description hits the page. That is when the uncertainty sets in.
What exactly should this person do? Should they build custom models? Integrate ChatGPT into workflows? Manage data pipelines? Run prompt engineering workshops?

The truth is, most companies at this stage do not yet have the clarity needed to define an effective AI role. The result is expensive hires with unclear mandates, misaligned expectations, and projects that stall before they deliver value.

Why Clarity Comes Before Hiring

Hiring an AI professional too early is like hiring an architect before knowing whether you are renovating a room or building an entirely new house.

AI is not a one-size-fits-all solution. It is a toolkit that can automate tasks, surface insights, and optimize decisions across dozens of business areas. But without a clear understanding of your pain points, data readiness, and desired outcomes, even the best AI hire will not know where to start.

According to McKinsey, only one percent of business leaders say their companies have reached AI maturity. That means most organizations are still exploring, not executing. Hiring AI talent without a clear roadmap usually leads to frustration and stalled initiatives.

Here is what we often see: - A company hires a highly skilled AI developer who spends months exploring ideas but struggles to tie them to business impact. - Leadership expects quick results, but the underlying systems, processes, or data are not ready. - The “AI initiative” becomes a side project rather than a business driver.

That is not a failure of talent, it is a failure of planning.

The 30% Rule: Where AI Ends and Human Value Begins

The “30% Rule in AI”, as highlighted by The Economic Times, offers a powerful reminder of why clarity matters. The idea is simple: AI can automate up to 70 percent of repetitive and predictable tasks, leaving the remaining 30 percent for humans to focus on high-value, creative, and strategic work.

For leaders, this means that not every process needs an AI engineer or data scientist. The goal is not to replace human judgment but to enhance it. Without understanding which 70 percent of work can be automated and which 30 percent must remain human-led, companies risk hiring for roles that AI itself could handle.

An AI consultant helps you make that distinction. They identify where automation creates efficiency, and where human expertise drives value. This balance ensures your future hires are focused on the right work, the 30 percent that moves the business forward.

The Smarter First Step: An AI Readiness Assessment

Before you hire anyone, the most valuable investment you can make is in an AI readiness assessment, a structured process to evaluate where AI can create measurable impact for your business.

This is where an AI consultant comes in.

An experienced AI consultant acts as a translator between business needs and technical solutions. They do not start with technology, they start with your goals. They will work closely with your leadership and team members across departments to uncover: - Pain points: What repetitive, manual, or time-consuming tasks could be automated? - Business needs: Where are bottlenecks affecting productivity, revenue, or customer satisfaction? - Data maturity: Do you have clean, accessible data to support AI tools and automation? - Quick wins vs. long-term goals: What can deliver impact in 30 days, and what requires a phased roadmap?

The result is a clear AI roadmap, a practical guide outlining where to focus first, what tools make sense, and what type of talent, if any, you actually need to implement them.

What AI Consultants Actually Do

Many leaders assume AI consultants only give advice, but the good ones go much deeper.

They meet with your different teams, operations, finance, marketing, HR, to see how work gets done day to day. They analyze workflows, identify where data lives, and map out inefficiencies that AI could meaningfully improve.

For example: - In accounting, they might identify opportunities to automate transaction categorization, anomaly detection, or report generation. - In customer service, they could recommend AI assistants to handle FAQs, triage requests, or summarize support conversations. - In sales and marketing, they might map how AI can enhance lead scoring, campaign insights, or personalized outreach.

Once the opportunities are defined, they help select and implement the right tools, whether that means integrating off-the-shelf automation platforms, setting up data pipelines, or designing custom AI workflows.

Importantly, they also help you measure success, linking each initiative back to concrete metrics like time saved, cost reduced, or revenue gained.

Consultants also play a key role in preparing your people for AI. McKinsey found that 48 percent of employees rank training as the most important factor for AI adoption, yet nearly half say they receive moderate or minimal support. Consultants help close this gap by training your teams, building confidence, and ensuring adoption feels empowering rather than intimidating.

When and What to Hire Next

After a clear roadmap is in place, you can confidently decide if and what kind of AI expertise you need to bring in-house.

For some businesses, an AI Project Manager or Automation Lead may be enough to coordinate tools and vendors.
For others, a Data Engineer might be critical to manage infrastructure and analytics.
And for larger organizations ready to scale, hiring a Head of AI or Chief AI Officer can make sense, but only once there is a strategy to execute.

By that point, the role description will practically write itself. You will know what skills are required, what outcomes are expected, and how success will be measured.

The Bottom Line

Rushing into AI hiring without direction can lead to wasted resources and slow progress. The most successful companies start with clarity, not code.

An AI consultant gives you that clarity, helping you see where automation makes sense, where human expertise remains essential, and how to build an AI strategy that grows with your business.

So before posting that “Head of AI” job on LinkedIn, take a step back.
Start with an assessment. Build your roadmap. Identify your quick wins.
Then, when you are ready to hire, you will know exactly who you need and why.

Ready to Start Smart?

At InscendAI, we help businesses cut through the noise and move from curiosity to clarity. Our AI Readiness Audit uncovers your best automation opportunities, maps your next steps, and ensures every investment in AI drives measurable business value.

Sources:

• McKinsey & Company, Superagency in the Workplace: Empowering People to Unlock AI’s Full Potential at Work (2024)The Economic Times, Be Human, Stay Relevant: The 30% Rule in AI (2024)