2023 IT Trends: The Year of Generative AI

December arrives with a new question in every board meeting: what do we do about generative artificial intelligence? In a matter of weeks it went from being a technical curiosity to a topic on everyone's lips across the organization, and 2023 will be the year that conversation turns into decisions about budget, talent and risk. The good news is that there is no need to improvise: the trends that will define the year follow a logic that a board can prioritize with a clear head.

In short: 2023 will be led by generative AI and copilots, but their real value will depend on three decidedly unglamorous enablers: well-ordered data, AI governance and cybersecurity. The recommendation is to choose a small number of use cases with measurable impact and to build the guardrails from day one.

1. Generative AI moves from demo to use case

The leap in quality of large language models (LLMs) has been so visible that the risk, paradoxically, is enthusiasm itself. For the board, the priority is not to "have AI" but to identify where it creates concrete value: drafting and summarizing documents, customer service, development support, contract analysis or the generation of commercial drafts.

• Implication: it is wise to start with two or three well-defined use cases, each with a clear business owner and a metric tied to savings or revenue. Proofs of concept without an owner end up in a drawer.
• 2023 decision: determine whether to consume AI through existing vendors or to build in-house capability. For most, the sensible path is to combine both, depending on the case.

2. Copilots arrive at the workplace

The fastest way generative AI will touch your people will not be a large project but assistants embedded in the tools they already use: email, spreadsheets, code and support. The copilot does not replace human judgment; it accelerates repetitive tasks and frees up time for the work that genuinely requires it.

• Implication: the change is as much cultural as it is technological. Teams will need to be supported with training and with clear rules about what information can be entered into these tools.
• Risk to watch: blind reliance on answers that sound convincing but may be wrong. The human being remains accountable for the outcome.

3. AI governance is no longer optional

If 2022 was the year of wonder, 2023 will be the year of hard questions: what data feeds these models? who approves a use case? how do we prevent bias and leaks of confidential information? A board that fails to define these rules will let each area make isolated decisions, multiplying the risk.

• Implication: establish an AI committee, an acceptable-use policy and an inventory of approved use cases. This is not about slowing things down, but about enabling them responsibly.
• Starting point: classify information by sensitivity before connecting it to any model.

At SUMāTO we understand this governance as part of an AI-first strategy: technology first, but with guardrails built in by design.

4. Data returns to the center of the conversation

No model shines on top of disorderly data. Generative AI has made it evident, to the entire leadership team and not only to the technical area, that the quality and order of information are the true differentiator. Those with data that is accessible, clean and well governed will be able to ride the wave; those without it will see disappointing results.

• Implication: investing in data architecture and information governance pays off more than chasing every new tool.
• Advice: prioritize the data domains that feed the chosen use cases rather than trying to put everything in order at once.

5. Cybersecurity adapts to a new playing field

The same technology that helps your organization also helps whoever attacks it: more believable phishing emails, malicious code that is faster to produce and new surfaces of exposure. At the same time, generative models raise unprecedented questions about data leakage when an employee pastes sensitive data into an external tool.

• Implication: 2023 demands stronger staff awareness and tighter controls over what information leaves the organization.
• Board priority: treat cybersecurity as a continuous investment, not a one-off project.

You can learn how we address these fronts in our cybersecurity practice.

6. FinOps: bringing discipline to cloud and AI spending

The cloud already consumes a meaningful share of the IT budget, and generative AI adds a variable-cost component that grows with usage. Without discipline, the bill spikes before the value becomes visible. FinOps -the practice of managing cloud spending with shared accountability across finance, technology and the business- becomes a leadership-level conversation.

• Implication: measure cost per use case and per team, and review it with the same seriousness as any other budget line.
• Benefit: cost transparency allows you to invest more where there is a return and cut what does not deliver one.

A well-governed cloud foundation is the platform on which everything above rests.

7. Talent and culture: the silent enabler

This entire agenda depends on people willing to work differently. The scarcity of specialized profiles remains real, but the answer for 2023 is not only to hire: it is to train today's teams so they can work alongside these tools and know when to trust and when to question.

• Implication: include AI literacy in the training plan, from leadership down to operational teams.
• Sign of maturity: that each area can explain, in business language, which problem it solves with AI.

How to prioritize without losing your head

Faced with so many trends, the temptation is to open many fronts at once. The recommendation for 2023 is the opposite: choose a few initiatives with a clear owner, metric and horizon, and make sure the enablers -data, governance and security- advance in parallel. Generative AI rewards those who experiment in an orderly way, not those who run without direction.

Frequently asked questions

Should we launch a large generative AI project in 2023?

Not necessarily. It is better to start with two or three well-defined use cases, each with an owner and a metric, and to scale whatever proves its value. Large, unfocused projects tend to consume budget before delivering results.

What is the biggest risk of adopting generative AI quickly?

The combination of enthusiasm and lack of governance: sensitive information leaving the organization, incorrect answers taken as fact and costs that grow out of control. That is why AI governance and cybersecurity must be in place from day one.

Where do we start if our data is disorganized?

By prioritizing. There is no need to put everything in order; it is enough to prepare the data domains that feed the chosen use cases and to classify information according to its sensitivity before connecting it to any model.

Who should lead this agenda in the organization?

A joint effort across business, technology and finance, with an AI committee that approves use cases and safeguards the rules. Leadership sets the priorities; the areas execute them with clear owners.

The first step

2023 will not reward those who adopt the most technology, but those who adopt it with judgment. If your board wants to turn the noise of generative AI into concrete decisions, the first step is an honest diagnosis: which use cases are worth pursuing, what data supports them and what guardrails are needed. At SUMāTO we guide leadership teams across LATAM along that path. Let's talk about your IT agenda for 2023.