Choosing an AI tool isn’t really about buying software. It’s about entering a long-term learning process. Organizations must be prepared to manage something that will evolve, occasionally act surprisingly, and sometimes get things wrong.
The AI selection process is unique because it doesn’t involve comparing features, reviewing demos, and launching a system that will always work as expected. It requires a different approach.
The following questions are designed to help leaders approach AI selection with a greater understanding of what it takes to make it work.
Don’t rush through the process of figuring out the problem space and defining the “why” behind turning to AI.
What problem are we solving for and what are the risks if not accurate?
Start by understanding your tolerance for error and variability. Every AI tool produces outputs with some level of uncertainty, so be clear about what problem you are trying to solve and what happens when the model is wrong. In some use cases, small inaccuracies may be acceptable. In others, one bad decision could create regulatory or financial issues or reputational damage. Be honest about how much risk the organization can absorb and where human oversight must be in place.
Do we have historical data in usable form?
AI systems are only as strong as the data they learn from. Is there truly enough historical data in a usable, well-structured form? This includes knowing where the data lives, who owns it, how complete it is, and if it reflects current business realities. If critical data is fragmented, outdated, or inaccessible, the effectiveness of any AI tool will be limited from the get-go.
Do we need to explain every decision to critical stakeholders?
Some decisions must be transparent to auditors, customers, or critical stakeholders. If your organization needs to clearly explain why a particular outcome occurred, can a proposed AI model can provide that level of insight? Highly complex models may have strong performance but weak interpretability, which can become a serious liability in high-stakes environments.
AI tools don’t behave like traditional software tools where everything is predictable and capabilities are consistent. The process of evaluating tools is different. Real-world performance outweighs theoretical capability.
Who can adjust this without programmers?
Evaluate who will be responsible for making adjustments and if those changes require specialized programmers. If only a small group of experts can maintain the system, it may become a bottleneck. Ideally, business and technical users can refine performance within clear governance boundaries.
How do we detect and correct bad outputs?
Every AI model will produce incorrect outputs at some point. Understand how quickly these can be detected and corrected. Examine what safeguards are built into the platform, how errors are surfaced, and what the escalation processes will be when outputs deviate from expectations. Knowing how the systems fails and how those failures can be managed is crucial.
What level of scalability (e.g., CPU) do we need to support this model?
AI workloads can place significant demands on computing resources and network capacity. Organizations should understand what level of scalability is required, whether processing will occur in the cloud, on-premises, or in hybrid environments, and how performance will be affected as usage grows. Underestimating infrastructure needs can lead to unexpected costs and performance issues down the line.
Implementing AI won’t be like implementing an ERP, for example. Instead of planning how to deploy and optimize a tool, it’s planning how to deploy, learn, adapt, and scale.
What do we need to see in a pilot to justify scaling?
A successful implementation starts with a clear pilot-to-scale path. Define in advance what success looks like during a pilot phase and what evidence is needed to justify broader deployment. This includes performance thresholds, user adoption indicators, risk assessments, and operational readiness. Without clear criteria, pilots often stall or scale prematurely and end up not working.
What level of training do our employees need? When do our people override model outputs?
Training and change management are equally important. Employees need more than basic system training. They must understand how the model works at a practical level, what its limitations are, how to interpret outputs, and when human judgment should override automated recommendations. Organizations should establish clear guidelines around decision authority and accountability so that AI supports (but not replaces) responsible professional judgment.
How do we know when the model stops working?
Ongoing monitoring and model drift management must be built into operations from day one. Business conditions, customer behavior, and data patterns change over time. As a result, models that once performed well might gradually become unreliable. Define how performance will be measured, how frequently models will be reviewed, and what triggers retraining or redesign.
Who will own it?
AI requires shared ownership across business, IT, and data. Effective programs require governance, with clearly defined roles for development, oversight, compliance, and business impact. Without this coordination, accountability becomes fragmented and risks increase.
What new controls might be required in 12-24 months?
Anticipate regulatory and ethical evolution. AI regulations and industry standards are changing, and leaders should consider what new controls, documentation requirements, or transparency measures may be required in the next couple of years. Build flexibility into governance frameworks early to reduce disruption and rework as things shift.
Once an AI tool is in place, it inevitably starts shaping how people think and how they operate. Over time, AI will have an influence on conversations, priorities, and business decisions, even without anyone consciously noticing.
The questions in this article are meant to surface those realities early while there’s room to adjust expectations and train employees accordingly. They’re meant to encourage purposeful choices about where automation helps and where human judgment remains essential.
At Trenegy, we help organizations evaluate and choose realistic AI tools that align with business needs and long-term goals. To chat more, email us at info@trenegy.com.
Trenegy is a non-traditional consulting firm based in Houston, Texas.
