Three mistakes that hinder ROI in AI projects (and how to avoid them)

1. The gap between promise and return

In the last two years, artificial intelligence has gone from experiment to business obsession. Thousands of SMEs have tested automation, assistants, and chatbots with the promise of saving time and reducing costs. However, various analyses agree that less than 25 % Most AI projects in small and medium-sized enterprises achieve a measurable economic return in their first year. This is not due to a lack of technology, but rather to... change management: expectations, use cases and measurement.

We see it often: teams excited about “doing something with AI” but without a business objective, metrics, or tracking structure. The result is invisible attrition: hours invested in testing that don't translate into real improvement.

2. Mistake #1: confusing “AI activity” with “business result”

A company can "do AI" every day—testing prompts, generating content, or automating tasks—and still not move any business metrics. The trap is in measuring. activity, No result. Indicators such as "number of automations" or "active users" do not provide economic value on their own; what is relevant are hours saved, additional revenue, or error reduction.

A sales team can implement a tool to write proposals faster, but if the closing rate or cycle time doesn't improve, the ROI is zero. The difference is clear in this table:

How to avoid it

• Define specific objectives (e.g., "reduce reporting time by 30 %").
• Establish a baselineHow much does that process cost today?.
• Evaluate 90 days the real delta on hours, costs and revenues.

As we explained in “Where AI does save time (and where it doesn’t)”,The savings come when AI acts on repetitive and measurable tasks, not on initiatives without context. Supporting reading: Harvard Business Review – Why so many AI projects fail.

3. Mistake #2: Implementing without a clear use case or governance

Buying licenses or adding a plugin doesn't mean you have an AI project. Without defining What a problem The question of who leads the effort and how success will be measured leads to fragmentation, technical dependence, and frustration. Each person uses AI in their own way, and the organization loses control of the knowledge base.

Profitable projects share three fundamental principles: defined use case, pilot controlled and governance (a business manager—not a technical one—with the authority to decide on continuity and escalation). According to the McKinsey's State of AI 2024,Companies with a cross-functional "AI Owner" role multiply by 2.5 the probability of generating measurable return.

How to avoid it

• It begins with a operational diagnosis (repetitive, costly, or critical tasks).
• Define a 60–90 day pilot with bi-weekly objectives and reviews.
• Name a responsible for monitoring that does not depend on the supplier.
• Close each phase with a report and decision: escalate, adjust, or discard.

Related approach: “Practical AI with ROI: when it makes sense and when it doesn’t”.

4. Mistake #3: Thinking of AI as an isolated tool, not as an integrated system

A chatbot, a copywriter, or a predictive model without a connection to CRM, ERP, or marketing automation generates islands of efficiency.Data doesn't flow, tasks are duplicated, and traceability is lost. A typical example: marketing generates leads "with AI," but sales continues managing them in Excel; follow-up is diluted.

Today there are accessible integrators: Make o Zapier for workflows between tools; HubSpot AI o Power Automate for reporting and tasks; Google Workspace Duet AI o Microsoft 365 Copilot for integrated analysis and writing. The key is the data map previous.

How to avoid it

• Draw the full map of the data flow before automation.
• Check which systems should receive and send information.
• Evaluate maintenance and security costs before climbing.
• Avoid relying on a single tool: prioritize interoperability.

Recommended reading: Data integration and governance practices (Gartner). 70% of % failures are associated with poor integration or lack of data governance.

5. How to measure ROI in AI projects

The ROI of AI is not measured by usage volume, but by operational and financial impact. The practical formula is: ROI = (Profit obtained – Total cost) / Total cost. In AI, the benefit is expressed in hours saved × cost/hour, attributable revenue (more leads or fewer returns) and reduction of errors or rework.

Example: Investment of €4,000 in automation that saves 60 hours/month at €25/hour. Annual profit: €18,000. ROI = (18,000 – 4,000) / 4,000 = +350 %. If these savings aren't measured, the project will appear useful without being profitable. Recommendation: a control sheet with investment, estimated impact, and actual results after 90 days.

6. Mini-checklist: warning signs in your AI project

• There is no formalized use case with clear objectives.
• Nobody is responsible for monitoring nor to measure results.
• The indicators are from activity, not business.
• Several tools are used without integration each other.
• There is no plan to learning or documentation of good practices.

7. From fascination to method

AI is full of promise. The challenge isn't believing in it, but rather transforming it into sustainable processes. The shift from fascination to methodology requires three changes: from “trying new things” to “optimizing what we already have”; from “tools” to “integrated systems”; and from “technical enthusiasm” to business responsibility.

As we say at Rumbo & Resultados: “"The future does not belong to those who test the most tools, but to those who best measure their results."” Profitable AI is not born from enthusiasm, but from a combination of strategy, governance and clear metrics.