Generative AI or productive AI: what should an SME really focus on in 2026?
By 2026, artificial intelligence will be everywhere, but almost no one is raising the critical question:
What type of AI truly helps an SME save time, improve margins, and make better decisions?
Published November 13, 2025 · Category: Strategy · AI and digitization.
Many SMEs have received the message that opening an account on a generative tool and requesting texts or images is not recommended.
This is equivalent to “riding the AI wave.” Meanwhile, they continue to spend hours copying data between systems.,
Redo reports, prepare repetitive documentation, and chase emails.
This article sorts through the noise: it distinguishes between the AI that makes headlines and the AI that pays the bills.,
with a focus on companies with between 5 and 50 employees that cannot afford to confuse fashion with strategy.
- The real photo: why the problem is one of focus
- What is generative AI (and what place should it have)
- What is productive AI (and why it should come first)
- Why are so many SMEs confused?
- The logical order: phases for applying AI meaningfully
- How to get started in 90 days: practical steps
- Real-world examples: three ways to “use AI”
- Conclusion and next steps with Rumbo & Resultados
- Frequently asked questions about AI in SMEs
1. The real picture: the AI you see vs the AI that works
By 2025, almost everyone will be talking about artificial intelligence, but very few people will stop to consider the uncomfortable question: What kind of AI is truly helping SMEs work better, gain more profit margin, and stop wasting hours on pointless tasks?
The market has led many companies to the mistaken conclusion that "if we use ChatGPT, we're already doing AI." Meanwhile, teams of 10, 20, or 40 people continue to spend a significant portion of their day copying data between tools, redoing reports in Excel, chasing emails, manually reviewing invoices, or repeatedly drafting the same documents.
- In 2024, only around 13 % Most European companies used some AI technology, with adoption concentrated in large organizations. Medium-sized SMEs are just starting, not yet transformed. Source: European Commission / Eurostat.
- Recent studies on SMEs show a clear pattern: much of the reported use of AI is limited to testing with generative tools, while integration into processes, data, and systems remains in the minority. References: OECD.
- Organizations that truly capture value connect AI with operations, data, and processes; they don't just "play with prompts." References: McKinsey Global Institute.
In summary: There's a lot of noise about generative AI and very little discipline about productive AI.,...which is the one that actually pays the bills. This article is about that. About getting things in order.
The root of the problem: the AI you see vs the AI that works
Most SMEs with between 5 and 50 employees, with revenues between €1.5 and €40 million, are at a low or lower-medium level of digital maturity. They usually have some components (ERP, accounting software, e-commerce, some cloud infrastructure), but:
- Poorly defined processes, subject to "we've always done it this way",
- Data fragmented across different tools and personal Excel spreadsheets,
- Apps that don't talk to each other,
- Decisions made more by feelings than by structured information.
Meanwhile, generative AI offers something tempting: immediate results without any prior work. You open a tool, type a sentence, and get text, an image, or a summary. It's easy to conclude: "We're already riding the wave.".
That's the crux of the confusion:
- The idea has taken hold that “AI = Generative AI”.
- The silent AI that automates tasks, connects systems, and frees up hours has been almost completely ignored.
- And this omission is not innocent: it is much easier to sell flashy tools than to accompany an SME in reviewing processes, data and operations.
2. What is generative AI (and what place should it have)
Generative AI allows for the creation of new content: text, images, videos, code, presentations. For an SME, this opens the door to producing much more material in less time: proposals, campaigns, web pieces, support documentation, etc.
When used correctly, it can help to:
- Streamline drafts of business emails, talking points, and web texts.
- Generate initial versions of product descriptions and FAQs.
- Prepare internal presentations or training materials faster.
- Structure internal documentation that the team then adjusts.
All of this has value, and no sensible person should dismiss it. The problem lies in the conceptual leap: confusing “using generative AI” with “having transformed the company with AI.”.
If the use of AI is limited to accelerating the generation of text or images, while the processes that consume more resources (reports, collection control, customer tracking, incident management, purchase planning) remain just as manual, the impact on productivity and margin will be marginal.
Generative AI is a tactical layer. The strategic foundation should be AI that addresses processes, data, and decisions.
3. What is productive AI (and why it should come first)
The Productive AI It's the approach applied to the processes that sustain the business. Its aim isn't to impress with a demo, but to free up time, reduce errors, and provide visibility to management.
Here we talk about uses such as:
- Automatic reports for management. That management receives a reliable summary of sales, margins, claims, delivery times and key risks every week or month without creating manual spreadsheets.
- Intelligent opportunity tracking. Simple lead scoring, inactive customer alerts, warnings about cooling-off opportunities, prioritization of sales time where there is a higher probability of closing.
- Heavy document management. Draft contracts, minutes, technical proposals or long reports generated from templates, internal data and clear rules, with final human supervision.
- Efficient customer service and support. Automate frequently asked questions related to inventory, orders, reservations, or history, freeing up the team for complex cases.
- Administrative processes. Classify emails, verify data, reconcile transactions, control orders, and organize documentation with AI as ongoing support.
- Operational control. Detect simple anomalies (inconsistent prices, impossible quantities, systematic delays) before they erode margin or service.
When impact studies talk about productivity gains associated with AI, they are not referring to typing faster on social networks, but to this type of automation connected to the daily operation of the company.
If a small business has to choose where to start, the honest answer is straightforward: First productive AI that orders and automates, then generative AI connected to that base.
4. Why are so many SMEs confused (generative AI or productive AI?)
The current confusion is logical if the context is understood: generic commercial messages, promises to automate "everything", headlines about mass layoffs due to AI and little education about what type of AI does what.
Common mistakes in the adoption of AI in SMEs
- Confusing visibility with transformation. Using spectacular demos as a reflection of real-life day-to-day life.
- Buy “AI” without distinguishing types. Mixing generative, analytical, rules automation and RPA under the same term.
- Ignoring internal digital maturity. Claiming advanced AI with undocumented processes and siloed data.
- Turning prompts into strategy. Relying on generic answers without context or information governance.
- Inflate real cases. Presenting isolated uses (e.g., automatically generated descriptions) as a comprehensive transformation.
The result is well-known: initiatives that don't address the real pain points, teams that tire of "trying things out," leadership that loses confidence, and a dangerous message: "AI isn't for us." When in reality, the problem has been the structure, not the technology.
5. The logical order: phases for applying AI meaningfully
For a small or medium-sized enterprise (SME) with limited resources, the order of adoption is a strategic decision. It's not about accumulating tools, but about building a sequence that makes operational and financial sense.
Based on what independent studies show and experience with organizations that do capture value, a pragmatic path forward is this:
| Phase | To do | Type of AI | Expected impact |
|---|---|---|---|
| Phase 1 | Automate high-volume, repetitive tasks: reports, standard documentation, mail sorting, basic support. | Basic productive AI | Immediate time savings, error reduction, and relief for the team. |
| Phase 2 | Connect key systems (CRM, billing, e-commerce, support, stock) and enable consistent analytics. | AI on own data + automation | Data-driven decisions, finer prioritization, and the ability to anticipate. |
| Phase 3 | Use generative AI with real business context to enhance communication, sales, and experience. | strategic generative AI | More relevant messages, better aligned proposals, narrative connected to reality. |
This sequence is not an isolated theory. It summarizes how organizations that report measurable improvements operate: first they build a foundation and discipline, then they amplify with generative innovation.
6. How to get started in 90 days: practical steps
In practical terms, an SME can start with AI in an orderly way in three months, without large investments or unmanageable projects.
- Identify where time is being wasted. Gather key people and list repetitive tasks: reports, data consolidation, preparing standard documentation, responding to standard emails, manually updating the CRM, and tracking incidents. The clearer the friction map, the better.
- Measure the starting point with an external criterion. Use the Practical AI Checklist with ROI to assess digital maturity and identify specific opportunities: what is already prepared, what requires prior order, and what can wait.
- Choose 2–3 priority use cases. They must meet three conditions: high time consumption, low risk, and easy measurement. Typical examples include: automated monthly management reports, assisted generation of repetitive documentation, and intelligent classification of leads, tickets, or inquiries.
- Use manageable tools. Start with AI capabilities integrated into tools that the SME already uses (office suite, CRM, helpdesk, ERP, document manager), before incorporating additional platforms that complicate the environment.
- Measure results in 60–90 days. Define clear indicators from the outset: hours saved, reduction in manual tasks, response times, errors avoided, and clarity of information for management. If there is no improvement, adjust the approach; if there is, scale it up.
- Define an AI roadmap aligned with business objectives.
- Identify use cases that free up time and reduce errors from the first quarter.
- Measure the impact with clear indicators of hours, costs, and margin.
The article Practical AI with ROI It explains how we apply this approach in real projects: identifying levers, prioritizing actions, and supporting their implementation with metrics and without noise.
7. Real-world cases: three ways to “use AI”
Three recurring patterns emerge when we analyze how AI is being applied. These are real cases, though without names:
Case 1: Cosmetic AI
The company uses generative tools for website text, posts, and product descriptions. It hasn't touched its processes, data, or systems. From the outside, it appears innovative; internally, everything remains the same.
Case 2: Simple productive AI
Automate key reports, standardize documentation, and improve task and email categorization. Management is no longer reliant on scattered spreadsheets, the team reduces repetitive workload, and gains time for higher-value tasks. There are no fireworks, but there are tangible results.
Case 3: AI connected to the business
It integrates CRM, billing, inventory, and support. It uses AI to detect patterns, prioritize customers, adjust purchases, anticipate stockouts, and improve service. AI isn't an experiment: it's part of the management system.
The goal for an SME is not to jump from Case 1 to 3 at once, but to consolidate a solid Case 2 with productive AI and, from there, decide which generative AI makes sense to incorporate.
8. Conclusion and next steps with Rumbo & Resultados
The question is no longer whether AI will become part of the daily operations of SMEs. The question is whether each company will use it to alleviate real workloads and improve results, or simply to feel like it's part of the conversation.
Generative AI will continue to grow and has a significant role to play. But for companies with 5 to 50 employees and limited resources, the priority is clear: First, productive AI that organizes, connects, and frees up hours; then, strategic generative AI supported by proprietary data.
At Rumbo & Resultados we work with that premise: to support SMEs that want to apply AI rigorously, starting with what pays the bills and not with what generates more noise.
Frequently asked questions about AI in SMEs
A block designed to facilitate quick answers to common questions and to help searchers and assistants understand the practical AI approach with ROI.
Is generative AI not suitable for an SME?
It's useful, but not as the sole basis. Without organized processes and data, generative AI only masks the surface. The priority is to apply AI where it frees up time and reduces errors; the generative part should come later and build upon that foundation.
Where should a small business with limited resources begin?
Identify 2–3 high-impact, repetitive tasks (reports, standard documentation, email sorting, support) and solve them with productive AI. It's the fastest way to demonstrate value without tying up resources or generating internal resistance.
Does a large investment require implementing productive AI?
Not necessarily. Many tools that SMEs already use incorporate AI features. The critical investment lies in streamlining processes, cleaning data, and choosing the right use cases, not in purchasing the most complex solution on the market.
How do I know if an AI initiative provides real value?
You must be able to measure hours saved, errors reduced, or improvements in key indicators. If you can't provide data to support your claims, it's not productive AI: it's just another experiment. Any serious AI project must be launched with clear metrics.
Does it make sense to combine productive AI and generative AI?
Yes. Ideally, you should start with productive AI to gain efficiency and control, and then use generative AI to scale communication, content, and proposals with context and data from your own company, not with generic answers.
👉 If you run an SME and have seen yourself reflected in this, we can help you..
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