Preparing for the AI Workforce Shift: Benchmarking Data Every Executive Should Have

AI is reshaping the workforce faster than most organizations can adapt. It’s moving so fast that BCG say “What feels advanced today will be table stakes by 2030 - if not before”. Roles are changing, functions are merging, and the definition of productivity itself is being rewritten.

Looking for trusted workforce benchmarks? Search here

As AI transforms how work gets done, executives need reliable data to guide decisions on headcount, investment, and performance. Benchmarking data offers that reality check. This blog explores the key benchmarks every executive should monitor to prepare for the AI workforce shift.

Table of Contents

• Understanding the AI Workforce Shift
• What Executives Are Asking About the AI Workforce
• The Benchmarking Data That Matters Most
• How Companies Use Benchmarks to Guide AI Workforce Planning
• Rebalancing Your Headcount For AI
• Measuring AI Productivity Gains with Benchmarking Data
• The Role of HR, Finance, and Strategy Leaders in the AI Transition
• Three Key Takeaways
• Frequently Asked Questions

Understanding the AI Workforce Shift

The rise of AI is much more than simple task automation. It involves redesigning the entire workforce. This leads us to an important point, which is that AI is not about jobs disappearing but about the redistribution of human capital.

Every industry is being affected by AI, with many organizations seeing three types of changes:

• Role augmentation: AI tools are making professionals more productive, from engineers using code assistants to analysts using LLMs to gather research insights.
• Role automation: Repetitive or transactional tasks in finance, HR, and customer service are being automated, reducing headcount in certain areas.
• Role creation: New jobs in AI operations, data governance, and machine learning ethics are emerging faster than most companies can hire for them.

Executives who understand these patterns early on can make smarter decisions about where to invest, redeploy, or reduce headcount. We expect this to result in distinct competitive advantages for those getting on the front foot.

What Executives Are Asking About the AI Workforce

Executives everywhere are asking variations of the same questions. Each one reveals a growing need for benchmarking data, such as:

• How does our current workforce mix compare to peers using AI?
• Are we over or under-invested in AI, data, and automation capabilities?
• Which roles will likely expand or contract over the next three years?
• What productivity gains should we realistically expect from AI?

Answering these questions with external data allows leadership teams to make fact-based decisions instead of relying on assumptions. Without benchmarks, organizations risk misjudging where to focus their AI investments. While there is a lot of money being invested in AI, most of it won’t provide the return hoped for by executives.

The Benchmarking Data That Matters Most

When preparing for the AI workforce shift, certain benchmarks provide the clearest insight into readiness and efficiency. These are the most valuable categories to track:

1. Functional benchmarks

Show how headcount is distributed across IT, Finance, HR, Marketing, and Operations. They reveal where peers are leaning into or away from automation. CompanySights is a leading provider of functional benchmarks – Search here.

2. Digital and automation benchmarks

Measure the ratio of employees working on data, AI, or automation compared to traditional functions. These ratios are increasingly important as more companies progress their digital agenda.

3. Productivity benchmarks

These link output, revenue, or value created per full-time equivalent to workforce design. One key example is revenue per employee, which is a high-level measure of overall headcount efficiency.

4. Cost benchmarks

Identify how AI driven efficiency changes cost-to-serve, cost per employee, or functional spend ratios. The cost base is one key area where AI is expected to have a high impact, with a general belief that costs will fall as AI adoption increases.

5. Span of control benchmarks

Track how AI affects management layers, revealing opportunities for leaner organizational structures. Spans are a key area of efficiency in the world of AI, as it is widely anticipated that they will increase with efficiency gains.

Search functional, cost, and span of control benchmarks

How Companies Use Benchmarks to Guide AI Workforce Planning

Benchmarking data is already informing major decisions across many industries. For example:

• The finance function in a global company used headcount benchmarks to identify duplicated roles and implemented AI tools to increase the efficiency of those in transactional finance roles, saving millions without losing performance.
• A large retailer benchmarked its digital maturity and found that it was under-invested in automation relative to specific competitors. The company rolled out a digital transformation program, which focused on automating manual processes via the introduction of specific software products and employee training.
• A small professional services firm compared its span of control ratios to peers leveraging AI and redesigned leadership layers. This had a direct impact on improving both agility and cost efficiency in the business.

These three short examples show that benchmarking is a helpful tool to initiate and plan major workforce changes across various industries. Imagine what it could do for you!

Rebalancing Your Headcount For AI

Every organization is facing pressure and tough questions about where AI will add the most value. In fact, McKinsey state that “Workforce planning is more difficult than ever.” The truth is that no-one really knows yet, but three trends are appearing:

1. Some functions are being optimized

Routine, process-heavy areas such as accounts payable, HR operations, and basic reporting are seeing AI result in fewer headcount. One example is developing a single repository of information for all HR employee information, which reduces the number of internal HR inquiries received.

2. Some functions are growing

Data science, AI operations, and analytics governance teams are expanding as companies build their internal AI capability.

3. Talent is being redeployed

This is linked to functions that are growing due to the high demand for AI skills. Professionals with strong domain expertise are being retrained to work alongside AI systems, such as “prompt engineering” in the Technology function.

The key is to use benchmarks to identify how peers are restructuring their workforce. Cutting too deeply in operational functions or doing this too quickly can backfire if employees haven’t adjusted to the new way of working. Also, letting critical employees go will eliminate institutional knowledge that AI tools rely on too.

Measuring AI Productivity Gains with Benchmarking Data

AI promises significant productivity improvements, but only benchmarking can show us the extent of what is possible. Executives should monitor these two productivity metrics:

1. Revenue per employee: This is calculated as annual revenue divided by the average number of employees in the same period. It helps to confirm whether AI adoption is translating into real business growth, not just cost savings.
2. Profit per employee: Similar to the metric above, this is the bottom-line figure of profit divided by the average number of employees. With this metric we are measuring whether productivity gains are reflected in profitability and efficiency ratios.

Forward-thinking executives use these productivity benchmarks to separate the hype from measurable outcomes. Plus, they help to keep their team accountable for real improvement.

The Role of HR, Finance, and Technology Leaders in the AI Transition

AI transformation is not just another IT project. It is a revolutionary technology that is here to stay and the single biggest enabler of competitive advantage in this century. Leaders across the org will play a crucial role in successful AI adoption, including:

• HR leaders must use benchmarking and AI to plan for workforce transitions, reskilling employees, and anticipating talent gaps early on.
• Finance leaders will need to be flexible and focused on increasing the efficiency of their function through the use of AI. Further to this, they must also leverage benchmarks to model cost savings, ROI, and headcount realignment scenarios.
• Technology leaders are the key to any AI transition. They are the ones with the expertise to advise other department leaders on how they can transition their workforce to be as efficient as possible.

The most successful organizations will have leaders driving the AI agenda forward in a strategic and impactful way.

Three Key Takeaways

The AI workforce shift is happening now, and benchmarking is the compass that can help management teams navigate it with confidence. Executives should remember these three things:

1. Benchmarking is forward-looking. The best data reveals not just where you are but where you should be heading.
2. Resilience matters more than reduction. AI should strengthen the organization, not hollow it out. Be especially careful about making big changes too quickly.
3. Data beats assumptions. Reliable benchmarks are the difference between smart transformation and costly missteps.

Start by identifying your baseline headcount mix, compare it against industry peers, and pinpoint where AI can create the most value. Then use benchmarking data to monitor your progress and adjust in real time.

Be bold - Explore trusted workforce benchmarks today

Frequently Asked Questions

What percentage of roles are being automated by AI?

Current benchmarking data shows early adopters are reducing 10% to 20% of headcount in transactional functions, while increasing digital roles at a similar rate.

Which functions are seeing the biggest productivity gains from AI?

Customer service, marketing, and software development are leading the way, with measurable output gains of 20% to 40% per employee.

How does revenue per employee change in organizations with higher AI adoption?

Companies with greater AI adoption are reporting higher revenue per employee, reflecting an increase in headcount efficiency.

How is HR using AI to optimize workforce planning and talent acquisition?

HR leaders are leveraging AI to forecast talent needs, match candidates to skill gaps, and automate routine tasks such as sourcing and screening. This allows HR teams to focus on strategic workforce planning, which is improving both speed and quality of hiring decisions.

How do I know if my organization is ready for AI from a workforce perspective?

Compare your functional headcount mix to peers leveraging AI. A balanced mix of automation, analytics, and change management capabilities signals readiness.