Council Post: It’s Time To Change How IT Teams Learn About AI

Tim Beerman is Chief Technology Officer at expert technology advisor Ensono.gettyIT teams traditionally adapt to new technology in a structured way: As new platforms emerge, teams evolve skills incrementally o...

July 31, 2026

Tim Beerman is Chief Technology Officer at expert technology advisor Ensono.

IT teams traditionally adapt to new technology in a structured way: As new platforms emerge, teams evolve skills incrementally over time.

AI has upended that process. Research from Model Evaluation and Threat Research shows that AI systems are roughly doubling their ability to complete complex tasks every seven months, far faster than most organizations develop expertise. Engineers are now expected to understand everything from prompt design to model behavior and output validation, often without established best practices to guide them.

Most organizations haven’t adjusted. While over half of leaders say AI literacy has become more important over the past year, according to a 2026 DataCamp/YouGov survey, far fewer provide even basic training. The result is a growing gap between expectations and team preparation.

Keeping pace requires more than adding stand-alone training programs. It calls for a shift in how IT skills are developed, and how organizations support teams as those skills continuously evolve in real time.

AI is accelerating faster than organizations can keep up.

As AI’s rapid evolution forces IT teams to constantly recalibrate their skills, governance struggles to keep up. Policies, standards and regulatory guidance are often developed without consistent guardrails.

This challenge worsens as AI adoption expands across the enterprise. Developers are using it to generate code, operations teams are embedding it into automation workflows, and security teams are evaluating its impact on risk and compliance. This drives productivity gains, but without a shared understanding of how to apply AI and when to question its outputs, usage fragments. Teams move at different speeds, operating on inconsistent assumptions.

In this environment, AI-generated outputs can appear complete while introducing hidden risks, from brittle codebases to overlooked security vulnerabilities. What feels like acceleration in the moment often creates downstream friction, where fixes are more complex and costly.

At the center of this issue is a gap in AI literacy—not just in how to use these tools, but in how to think about them. When outputs are treated as authoritative rather than something to evaluate, teams lose visibility into how decisions are made. Speed increases, but understanding doesn’t. That reduces the value organizations get from AI and erodes confidence in AI systems.

The answer isn’t to slow adoption, but to create a stronger, more consistent foundation for how AI is understood, governed and applied across the organization.

4 Steps To Building AI Literacy Into Team Operations

AI literacy should be treated as a core capability for every team, not something isolated within a single function. Here are four practical steps to empower your teams to use AI effectively:

1. Define your AI strategy.

Start by aligning AI initiatives with core business goals. Focus on a few high-impact use cases within your existing systems and define measurable success metrics. Without that clarity, experimentation can scale quickly without delivering meaningful value.

Just as important is how that strategy is communicated. Teams need to understand where AI is expected to drive outcomes and how it will augment their work (not replace it).

Clear direction focuses adoption and ties it to business impact. It's not just tool usage for its own sake.

2. Assess your foundation and establish a baseline.

Once internal priorities are set, the next step is evaluating whether your organization is equipped to execute them. Can your data infrastructure support reliable outputs? Are there gaps in how teams are currently using and understanding AI?

From there, define a baseline for how AI should be applied across the organization. As usage expands across functions, teams need clear, shared expectations for where AI is appropriate, where it introduces risk and when human oversight is required.

At Ensono, we’ve made this a priority by requiring foundational AI training across roles. Establishing that shared baseline helps ensure teams are aligned on how to use AI, even as they apply it differently within their own functions.

3. Embed role-specific AI practices into workflows.

Training only matters if it shows up in day-to-day work. The goal is to translate organizational expectations into how each team actually operates.

For developers, that may mean incorporating prompt design standards, model selection criteria and structured output validation into their workflows. For security and operations teams, it involves building in checks for model-related risk, performance variability and the ways AI affects system behavior.

These practices should be embedded into existing processes—code reviews, testing protocols and monitoring—so they become part of how work gets done, not an added step. Reinforcing practices through role-specific certifications and tying them to career progression help ensure these skills are applied consistently as teams grow.

4. Create space for continuous learning and experimentation.

AI is evolving too quickly for skill development to be a one-time effort. Organizations need structured ways for teams to test, learn and adapt as new capabilities emerge.

This can take the form of small pilot programs, internal forums or communities of practice where teams share what’s working and where challenges are surfacing.

At Ensono, these kinds of feedback loops have helped teams identify risks earlier, learn from real-world use cases and build collective expertise over time. It also gives teams the confidence to experiment knowing there’s a framework in place to support them.

Preparing Teams For A New Kind Of Skill Evolution

AI is reshaping both the IT stack and how teams interact with it.

The difference between simply using AI and truly understanding it is already showing up in the form of technical debt, security exposure and operational inefficiencies that are difficult to unwind.

Addressing that divide requires treating AI skill development as an ongoing, organization-wide effort that is aligned with business goals, built into career development and reinforced through everyday workflows. With that foundation in place, AI can be tailored to each team while staying grounded in the same standard of quality and control.

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