The ROI Trap: Why AI Needs More Than Individual Productivity Gains

Michael Spenceley is a DX and Content Operations Leader and a CMS Critic Contributor. 

The pressure to extract ROI from AI is being felt in organizations of all sizes and in all sectors. Over the past several years, many leaders have been pleased with the incremental efficiency gains and cost avoidance that their organizations have achieved by using AI tools. However, if the ROI pressure continues, investors, boards, and the C-suite will set a higher bar for what constitutes successful AI implementation.

According to PwC’s latest Global CEO Survey, only 30% of CEOs are confident about revenue growth over the next year. That’s down from 38% last year. To get ROI, AI has to either increase revenue, or reduce costs (hopefully, both). This leaves us with a lot of pressure and some difficult choices to make about how to proceed. Before we talk about what to do about it, let’s talk about what’s happening right now.

What most people are experiencing right now are incremental, individual productivity gains through the use of AI tools like Copilot, Claude, and ChatGPT. Unless a company is actively reducing its workforce, re-deploying people to revenue-generating activities, or using AI to accelerate new product development and increase speed to market, they are likely not getting any measurable ROI.

To be clear, individual productivity gains are a good thing, and, in a lot of cases, they improve the employee and customer experience. This is also an important step for everyone to build comfort and proficiency with AI. These are all good things, but if the ROI pressure continues, we’ll have to do more to meet expectations.

There’s no perfect answer, but I think a good starting point is to move past incremental productivity gains and start reimagining processes with AI at the center.

What does it mean to reimagine a process with AI at the center? Well, now that we know what AI can do, how might we change what people do and how they do it at a more fundamental level? How might we implement AI in ways that break down functional boundaries and surface inefficiencies we didn’t know we had?

Let’s run through a simple example…

An organization that sells products has lots of data and information about its products. That product information is used as an input by different functions. A marketing team uses it to create marketing materials, a sales team uses it as a reference when calling on customers, and a training team uses it to train sales and customer support teams.

Especially in large organizations, these might be completely different functions that report into different leaders. Each of these teams are probably using AI in their own way, and I’m sure they collaborate and share information. But is that enough? What if we look across the functional boundaries and reimagine how we execute these processes with AI at the center?

First, product information needs to be stored and organized in one central place, with a clear process to ensure it’s accurate and kept up to date (many organizations already do this).

After assessing all the ways product information is used, imagine building an ecosystem of AI agents trained to handle various tasks that leverage product information.

• A marketer can prompt an agent to build content for a social media campaign, or a product brochure for a trade show
• A trainer can prompt an agent to build a product training curriculum, including videos, slides, role-play scenarios, and objection-handling guidance
• A sales associate can prompt an agent to pull product details and create a branded one-pager they can share with a potential customer, tailored to that customer’s unique needs

Note: These agents will need to be built and trained to do these tasks and, in many cases, the output will require some level of review, at least for now.

Will this approach lead to a tangible ROI?

It depends on what we do next. Using this approach, we’ve saved time across multiple functions and have streamlined handoffs between functions. Using AI in this way can also lower the barriers to entry for some roles, making it easier for people to be redeployed or shared across functions. This might help us go to market faster, reach more customers, and ultimately increase revenue. We may also have the opportunity to shift people between functions and re-skill them for roles that require a human touch (at least for now).

The ROI will also depend on how much it costs to build these tools. Implementing AI increases operating costs, and there is a lot of debate over whether we’re seeing the true costs of AI just yet. On the revenue side of the equation, there is a general lack of confidence about growth, so while all this sounds good on paper, both cutting costs and increasing revenue are difficult to achieve, even if we’re using AI in all the right ways.

Let’s address the elephant in the room…

If we reimagine a process with AI at the center, will there still be layoffs?

Forrester wrote in its latest AI Job Impact Forecast that many companies announcing AI-related layoffs don’t have mature, vetted AI applications that can truly replace people. Additionally, in an HBR study, 60% of the more than 1,000 executives surveyed said they made headcount reductions in anticipation of future AI efficiencies.

I think the writing is on the wall, and it’s likely that layoffs will continue, whether AI is ready to take over or not. However, if the right tools aren’t in place, companies may not get the results they’re looking for; and quality, employee experience, and customer experience will suffer.

There are still a lot of unknowns and a lot to learn, but I think the best next step is to think differently about how we transform a process using AI, moving beyond incremental productivity gains. While layoffs will happen and may bring some short-term ROI; in the long run, companies need to deliver results, either with people or with AI. Hopefully, with a little of both.

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