While the major AI labs debate how to distribute the benefits of the technology, the practical question for any private company is more down-to-earth: what do I do with this next week? OpenAI has just funded 14 independent projects to study how AI can expand economic opportunities and strengthen social resilience in the age of Intelligence (source: OpenAI, «New policies for the age of Intelligence», 2026). It’s an interesting move, but it’s worth translating it from the language of public policy into the language of your bottom line. That’s what we do in this article.
What OpenAI has just announced (in two lines)
OpenAI is allocating one million dollars in funding and up to another million in API credits to research organizations in the U.S., the European Union, Brazil, Singapore, and South Korea. The projects revolve around two ideas: how AI can expand economic opportunities and how societies can gain resilience as the capabilities of these systems grow. There are studies on employment and skills, taxation, energy for data centers, social benefits, AI safety, and clinical infrastructure in public hospitals.
It’s a high-level debate, aimed at governments and institutions. But, if you read between the lines, very useful signals emerge for an SME deciding how much and how to invest in AI.
From global policy to your company
The underlying thesis of the announcement is simple and applicable to any organization: for AI to benefit many and not just a few depends on decisions, not just on having access to the technology. A billion people already use tools like ChatGPT, so access has ceased to be a competitive advantage. What makes the difference is what you do with it and how you integrate it into your processes.
For a company, this means that following the trend is not enough. Buying AI licenses without a plan is the equivalent of installing an ERP and continuing to work in spreadsheets. The value is not in the tool, but in the specific use case, the data that feeds it, and the people who use it every day.
Three ideas from the debate you can already apply
1. Distribute the "AI dividend" within your own house
One of the funded projects studies how the productivity improvements that AI brings are distributed among workers, companies, and regions. Bring it to your scale: if you automate administrative tasks or speed up your team’s work, does that freed-up time translate into better service, fewer errors, or greater sales capacity? Defining in advance where you want the benefit to land prevents the gain from being diluted. Tools like Power Automate and Power BI help automate processes and measure that impact with numbers, not intuitions.
2. Without organized data, AI does not perform
Several projects insist on something we repeat daily: AI is built on data. One of them, in Brazil, tests organizing scattered clinical information using only synthetic or anonymized data, with human supervision. The lesson for your company is twofold. First, before thinking about models, organize and govern your data: quality, access, privacy. Second, AI does not replace professional judgment; it assists it. This is exactly the starting point of our AI consulting: identifying which data is relevant, creating governance protocols, and evaluating tools according to your investment capacity.
3. Resilience means measurable use cases, not headlines
The second major line of the announcement is resilience: preparing for rapid changes. For an SME, resilience is not about accumulating technology out of fear of falling behind, but choosing wisely. A good AI use case is one that solves a measurable business problem: reducing customer response time, detecting incidents before they escalate, prioritizing orders. Start with one, measure it, and decide with data whether it’s worth expanding. It’s cheaper to fail in a limited pilot than in a global rollout.
The dos and don’ts in the age of Intelligence
The announcement itself acknowledges something honest: no company or government has all the answers. That humility is healthy within an organization too. These would be our practical rules:
- Yes: start from a specific problem and give it an indicator. No: "doing AI" just to have it.
- Yes: take care of data and privacy from day one. No: dump sensitive information into any tool without governance.
- Yes: keep people in control of decisions. No: delegate professional judgment to a model that can make mistakes.
- Yes: pilot, measure, and scale what works. No: large rollouts without evidence.
How Tisa puts it into practice
At Tisa we have been helping companies modernize with technology since 1987, first with ERP —Microsoft Dynamics 365 Business Central— and now also with AI applied to business. Our approach fits with what this global debate points to: not selling smoke, but identifying the right use case, putting in order the data that feeds it, and supporting the team so they can take advantage of it.
We work in levels according to your starting point: from basic data analysis —identifying which information would help you decide better— to the development and execution of a use case tailored to your business, passing through data governance and the training of your people. And all on a robust technological base: Business Central, Power Platform and, when it adds value, the embedded AI of the Microsoft ecosystem.
The age of Intelligence will not be won with the flashiest announcement, but with well-made decisions sustained over time. That’s the part that is indeed in your hands.
Take the first step wisely
If you are considering where to start with AI in your company —or if you already use it and want to move from the trial to real value—, let’s talk. At Tisa we help you choose the right use case and build it on reliable data, without promising what cannot be demonstrated.
Write to us at info@grupotisa.com or call us at (+34) 971 305 885. Request a no-obligation assessment at grupotisa.com and let’s take the first step together.