The AI Era Is Increasing Demand for People, Not Eliminating It

Companies across industries are working to make their internal processes more efficient. As a result, we’ve seen the technology become the scapegoat for a consistent string of layoffs. Specifically in the tech sector, companies are blaming massive rounds of layoffs on AI. Market trends around flatter organizations and leaner operations to fund investments are being represented as efficiency gains when in reality, enterprise AI leaders would simply disagree that it’s even possible to replace humans at this scale with the technology available today. 

Regardless, the narrative around AI replacing people is further cemented and fear of the technology is sowed within every workforce – even those who haven’t been directly impacted. 

It is true that AI is eliminating certain tasks, reshaping how we spend our time and driving greater scale in what we can achieve. But, it is not eliminating the need for expertise. In fact, it’s increasing the demand for human expertise at the intersection of business operations and AI. 

Shifting Human Value At Work

Over the past year, countless enterprises have announced AI-related layoffs. To some extent, AI is indeed making certain work more efficient and even redundant; however, in many cases, it’s simply a convenient excuse for broader cost-cutting efforts rewarded by public markets. 

While it is at the expense of company reputation, at face value, layoffs due to increased efficiency thanks to new technology is a great sign for investors; however, this strategy prioritizes short-term gains in profit margins over longer-term returns. In fact, according to a Deloitte study, most organizations are devoting 93% of their spending to technology while only 7% gets reinvested in the people. This can come at the detriment of the company if they don’t reinvest their productivity gains into new capabilities and a solid workforce strategy. A balance is necessary to support the workforce transformation that will succeed in an AI-first era.

The framework for driving change still relies on people, process, and technology – even within the AI narrative. While the underlying technology is changing, organizations would be remiss to neglect the process and people part. 

Afterall, yes, most AI is used to automate administrative and repetitive tasks, but we’re forgetting all the backend work it takes to get to that point. Someone still has to define objectives, validate outputs, govern AI decisions to make sure the systems are continuously improving, and redesign entire workflows around those systems. Technology is the enabler. It’s not a replacement for critical thinking, building relationships, and supporting a group of diverse opinions and perspectives to get to the right answer. 

If you look at it this way, enterprise AI might be creating more work than its eliminating, shifting where humans are showing value at work. 

You see, large language models know language, but they have limited insights when it comes to the inner workings of how a particular business runs – their policies, regulatory environment, company culture, and operational nuances. As a result, we’re seeing increased demand in the roles that connect those two things, like forward-deployed engineers (FDEs), AI workflow architects, and transformation and enablement leads. 

Caution! Organizational Change Ahead

Enterprises have realized that AI pilots are the easy part. Shifting from experimentation to production deployment is the difficult part – and where most enterprises sit right now. To successfully scale pilots to deployment requires redesigning whole workflows and business processes. Aside from model performance, broader organizational change is necessary. 

That is why demand is rising for people who can translate AI for specific business workflows. In fact, we saw it earlier this year with the rise of FDEs, but it would not be surprising if these types of AI experts became a competitive advantage. Think about how valuable it would be to have someone within your organization with the answers to questions like:

• Which problems/workflows need redesign?
• How should we measure success within our specific organization?
• How can we get employees to actually adopt AI at scale?

So, what can business leaders do to ensure their organization is set up for success with this type of talent?

First, decide whether you want to build or buy AI talent. A huge discussion right now, both have pros and cons. Bringing in fresh talent can fill technical expertise gaps immediately, boost initial AI strategy, and pilot fresh ideas. However, if we’re talking about a new role in which AI fluency is combined with business expertise, the natural fill is someone within the organization already that is familiar with its operations. Of course, this requires strong leadership to identify and train these people, and this could be a longer process than hiring someone externally. 

The answer instead is to combine both build and buy to get your ideal outcome. By pairing technical AI specialists from the outside with internal workflow experts, you can achieve the perfect balance. 

Second, build cross-functional AI teams that don’t just exist in the IT silo. If the framework for successful workforce transformation is people, process, and technology, you cannot afford to neglect any of the three. Some of the best AI deployments are led by business leaders across human resources, product teams, the legal department, and more. 

AI Era And Beyond: Looking At A New Workforce

If history is any indication, we know that every major technology boom upended the workforce as we then knew it, eliminating roles – yes, but then replacing them with new roles. For example, the internet reduced demand for traditional brick and mortar retail and print jobs. However, on the backend, we got digital marketers, graphic designers, cybersecurity professionals, cloud architects, and more. 

All this to say, we are likely to continue to see mass layoffs blamed on AI, especially as companies promote their increased usage and productivity gains. However, truly AI-forward companies know that while the jobs themselves might change, the number of roles is unlikely to fluctuate much. 

The new workforce already in flux will see a need for professionals who can bridge the gap between business operations, change management, and technical expertise. The companies that lean into this early will be the ones that build a workforce capable of turning AI into a sustainable competitive advantage.