2023 was the year generative AI moved from collective astonishment to the mandatory conversation in every executive committee. For months we launched proofs of concept, internal copilots, and experiments with language models; many of them shone in the demo and went dark before ever reaching a real customer. The question that defines 2024 is no longer whether generative AI works, but how to take it from pilots to production with measurable value, serious governance, and costs under control. Below I share the IT trends that, from SUMāTO, I recommend putting on your committee's table as the new year approaches.
In short: 2024 will be the year generative AI is industrialized: fewer demos, more production systems that generate savings or revenue. The organizations that win will be those that combine well-ordered data, AI governance, and cost discipline, not those that accumulate more pilots. Competitive advantage will shift from "having AI" to "operating AI reliably and securely."
After a 2023 full of experiments, the challenge in 2024 is turning those prototypes into stable capabilities. Production implies monitoring, versioning of models and prompts, regression testing, and clear owners, something very different from an isolated demo. The implication for the committee is to prioritize a few use cases with a clear return and see them through to the end, rather than funding dozens of initiatives that never scale.
Retrieval-augmented generation (RAG) established itself in 2023 as the practical way to connect language models to the company's own knowledge without retraining full models. In 2024 it will be the dominant architecture for internal assistants, support, and document search, because it reduces made-up answers and keeps information current. Its success depends less on the model and more on the quality of the sources that feed it.
Toward the end of 2023, the first AI agents began to appear: systems that not only answer but also plan steps and execute actions on tools. It is an emerging and still immature trend, but it points the direction of 2024 toward automating more complex tasks. My recommendation is to explore them in bounded, supervised settings, not to delegate critical processes to them without controls.
The regulatory progress of 2023 made it clear that AI will arrive accompanied by demands for transparency, traceability, and data handling. For an organization in Latin America, getting ahead of these requirements is an advantage, not a burden. An AI governance framework defines who approves a use case, how risks are assessed, and what data may be used, avoiding legal and reputational surprises.
The same technology that boosts productivity also empowers attackers: in 2023 we saw more convincing phishing emails and more personalized campaigns. At the same time, AI strengthens the defense, helping to detect anomalies and respond faster. In 2024, security must be designed from day one of any generative AI project, especially when internal data is exposed through assistants.
Inference and infrastructure costs can turn a promising use case into an unviable one. In 2024, the mature conversation includes choosing the right model for each task, optimizing consumption, and leveraging the cloud efficiently instead of always using the largest model. The profitability of generative AI will depend as much on the engineering as on the cloud architecture that supports it.
An AI tool that no one uses generates no value. The differentiating factor in 2024 will be people's ability to incorporate these tools into their daily work, with confidence and judgment. This requires training, support, and process redesign, not just licenses. The executive committee must view adoption as part of the project, with explicit targets for real usage.
By choosing one or two use cases with clear value for the business and taking them all the way to production with defined owners, metrics, and controls. One working system is preferable to ten demos on the shelf.
In most cases, no. With architectures like RAG it is possible to connect existing models to the organization's knowledge, which is faster, more economical, and easier to keep up to date.
By combining curated data sources, reliable information retrieval, traceability of every answer, and human review in sensitive processes. The quality of the sources weighs more than the size of the model.
Yes. Getting ahead with an AI governance framework reduces legal and reputational risks and makes it easier to scale with confidence when more concrete requirements arrive.
2024 will reward discipline over enthusiasm: well-ordered data, clear governance, security by design, and costs under control. If your organization wants to turn its generative AI pilots into capabilities that generate real value, at SUMāTO we can help you prioritize the right use cases and build the foundation to take them to production. Let's talk about your roadmap for 2024 at sumatogroup.com/contacto.