What Separates AI ROI From AI Waste?

What does the data actually say about who's winning with AI?

Most companies spending on AI are not getting measurable returns. A recent survey covered by IBTimes draws a hard line between two types of organizations: performative adopters, who deploy AI visibly but diffusely, and value creators, who target specific workflows and measure outcomes. The gap between them is not about budget. It is about focus.

This matters for SMB operators because the temptation is to do what large enterprises do: buy the tools, roll them out broadly, and call it an AI strategy. The data says that approach is exactly what produces waste.

Why do most AI investments underdeliver?

The pattern shows up across company sizes. Organizations that treat AI as an initiative rather than a workflow intervention tend to see low adoption, unclear ownership, and no measurable outcome. The tools get purchased. Seats go unused. Leadership reports that AI is "being explored."

Value creators do something different from the start. They identify a specific bottleneck, a process that is slow, error-prone, or expensive, and they build or deploy AI directly into that process. They measure before and after. They assign someone accountable for the result.

This is not a complicated framework. It is just discipline about where AI gets applied before it gets bought.

What workflows are actually producing ROI?

Across the survey data, the highest-ROI applications cluster around a few categories:

• Content and proposal generation: Reducing the time a sales or marketing team spends drafting first versions
• Customer support triage: Routing, summarizing, and drafting responses to inbound inquiries
• Internal knowledge retrieval: Letting staff query internal documents instead of hunting through folders or asking colleagues
• Data summarization: Turning reports, call transcripts, or reviews into structured summaries

None of these require a large AI budget or a dedicated data science team. They require clarity about what problem is being solved and a willingness to actually change how work gets done.

The bottleneck is almost never the AI tool. It is the unwillingness to redesign the workflow around it.

What makes someone a 'performative adopter'?

Performative adoption has a few reliable signals. The organization has purchased AI tools but cannot name a specific process that runs differently because of them. There is no before/after metric. The rollout was led by IT or a vendor rather than an operator who owns the outcome.

Another signal: AI is being used to generate outputs that nobody reads or acts on. Reports that sit in inboxes. Summaries that duplicate what already existed. The tool is running, but nothing downstream changed.

For SMBs, this often happens when AI adoption is driven by fear of missing out rather than a clear problem. A competitor is "using AI," so the business buys something. That is a recipe for spend without return.

How much does budget actually matter?

The survey data does not support the idea that higher AI spend produces better outcomes. What it supports is that intentional deployment at any budget level outperforms broad deployment at any budget level.

For context, most of the workflow applications producing real ROI for SMBs run on tools costing $20–$100 per user per month. ChatGPT Team, Claude Pro, and similar products are in that range. The cost is not the constraint. Workflow redesign is.

A business spending $500 per month on a focused AI workflow that saves 10 hours of staff time per week is generating a return. A business spending $2,000 per month on licenses across a team that has not changed how it works is not.

How do value creators actually implement AI differently?

The operational difference comes down to three things:

1. They start with a process audit, not a tool selection. Before picking software, they map the workflow. Where does time get lost? Where do errors happen? Where does handoff break down? The tool selection follows from the problem, not the other way around.

2. They assign ownership. Someone is responsible for the AI-assisted workflow performing. Not responsible for "AI adoption" in the abstract. Responsible for a specific metric: turnaround time, cost per output, error rate.

3. They set a measurement baseline before deploying. If you do not know how long something takes today, you cannot know whether AI improved it. Value creators measure first. This sounds obvious. Most organizations skip it.

Does company size affect these patterns?

The divide between performative and value-creating adoption appears across company sizes, but SMBs actually have structural advantages here. Smaller organizations can move faster, change workflows with less coordination, and get cleaner before/after data because fewer variables are changing simultaneously.

The disadvantage for SMBs is that they often lack someone whose job it is to think about this. In a 20-person company, nobody has "AI strategy" as a dedicated function. That is where outside guidance or a peer community of operators doing this work becomes genuinely useful.

What we'd actually do

• Audit one workflow this week. Pick the process in your business that is slowest or most error-prone. Write down how long it takes, who touches it, and where it breaks. That is your AI implementation target, not the whole business.
• Set a measurable baseline before you buy anything. Time it. Count it. Cost it. You need a number before deployment so you have something to compare against 60 days later.
• Join a community of operators doing this in real businesses. The gap between performative and value-creating adoption closes fastest when you can see what is actually working in businesses like yours, not vendor case studies. That is exactly what skool.com/aiforbusiness is built for.