AI & Tech Training

👋 Happy New Year 2026! Welcome to TryTami’s weekly newsletter. Each week, we share insights on AI and tech training.

TryTami’s training operations platform enables teams to learn directly from live experts to upskill in AI, new technologies, and more.

By now, AI is part of everyday work in most organizations. Engineers rely on it while writing and reviewing code. Product teams use it to explore ideas and synthesize feedback. Leaders use it to speed up analysis and decision-making.

What has become clear over the past year is that adoption alone hasn’t translated into confidence. Many organizations are using AI extensively, but few feel they have a strong handle on how well it’s being used, where it introduces risk, or how prepared their teams are for what’s coming next.

That tension is showing up in familiar ways. Output is faster, but quality is harder to reason about. AI-driven decisions are made across teams without shared standards for evaluation. Governance exists on paper, but breaks down when people have to make tradeoffs in real workflows. And as AI agents move from experimentation into production, the gaps are becoming harder to ignore.

This is why AI training programs look very different in 2026 than they did even a year ago.

There is no shortage of AI tools or learning resources available today. What organizations are running into instead is a shortage of applied skill, especially when AI is embedded deeply into day-to-day work.

One of the most common gaps is evaluation. Teams know how to get output from AI systems, but many are still unsure how to assess that output consistently. Subtle errors are easy to miss, especially when AI is used repeatedly across a workflow or handed off between teams.

Another gap is shared judgment. Different teams often develop their own norms for how much they rely on AI, which creates inconsistency and makes it difficult for leaders to understand risk at an organizational level.

Governance is also struggling to keep pace. Policies and principles are in place at many companies, but teams are rarely trained on how to apply them when deadlines are tight and tradeoffs are real. As a result, governance often becomes reactive rather than preventative.

These gaps were manageable when AI tools acted primarily as assistants. They are much more serious now that AI systems are starting to take action on their own.

Over the past year, AI agents have moved from isolated pilots into real workflows. In some organizations, agents now handle multi-step processes, coordinate across tools, and make decisions that previously required human intervention.

This shift has raised the bar for AI skill across the organization. Teams now need to think carefully about how work is delegated to AI, how autonomy is bounded, and how failures are detected and handled when they inevitably occur.

Very few teams were trained for this. Most early AI training focused on individual productivity rather than system behavior. As agents become more capable, that gap becomes increasingly risky.

Organizations that are adapting well are treating AI agents as an operational capability that requires deliberate training. They are investing in programs that teach teams how to design safe agent workflows, monitor behavior over time, and intervene effectively when things go wrong.

The most valuable AI skills are no longer about knowing which tool to use. They’re about knowing how AI behaves inside real systems and how to manage the consequences of that behavior.

The organizations making steady progress with AI are training for the following skills explicitly, rather than hoping teams pick them up along the way.

1. AI Evaluation and Output Quality Assessment:

One of the biggest AI skills gaps organizations face in 2026 is the ability to consistently evaluate AI output.

Many teams still treat AI responses as either “good” or “bad” based on surface-level plausibility. In practice, meaningful evaluation requires people to assess accuracy, completeness, bias, and downstream impact. This is especially important when AI output is reused, automated, or fed into other systems.

Effective AI training programs teach teams how to:

This skill has become foundational as AI systems move deeper into production workflows.

2. AI Systems Thinking (Not Tool Usage):

AI rarely operates in isolation. It is part of a broader system that includes data pipelines, user inputs, business rules, and human decision points.

Teams that struggle with AI often focus too narrowly on the model or tool itself. Teams that succeed are trained to think about AI as a component within a larger system.

This includes understanding:

Strong AI training programs emphasize systems thinking so teams can reason about outcomes, not just interactions.

3. Designing and Managing AI Agents:

AI agents have become a defining capability in 2026, and they introduce an entirely new category of skill requirements.

Unlike traditional AI tools, agents take actions, chain tasks together, and operate with varying levels of autonomy. This requires teams to be trained in how to design, monitor, and constrain agent behavior.

Key skills include:

Organizations that are unprepared for agent-based workflows are already seeing increased operational risk.

4. Human-in-the-Loop Decision Design:

One of the most misunderstood AI skills is knowing where humans should remain involved.

In 2026, the question is no longer whether humans should be in the loop, but where and how they should intervene. Poorly designed oversight slows teams down. Poorly designed autonomy increases risk.

Effective AI training programs teach teams how to:

This skill is critical for scaling AI responsibly without overwhelming teams.

5. Governance in Real Workflows:

Most organizations now have AI policies. Far fewer have trained teams to apply them under real conditions.

Governance in 2026 is less about policy documents and more about everyday decision-making. Teams need to know how to apply governance principles when data is incomplete, timelines are tight, and tradeoffs are unavoidable.

AI training programs that work focus on:

This turns governance from a blocker into an operational capability.

Why These Skills Define AI Readiness in 2026:

Organizations that focus AI training on these skills are better equipped to:

Those that don’t often find themselves reacting to problems rather than shaping outcomes.

When organizations take AI training seriously, it shows up in how they structure learning around real work rather than abstract capability building. While each company approaches this differently, the underlying intent is consistent: reduce risk while increasing confidence as AI becomes more autonomous.

1. Google’s AI Residency:

AI training continues to be deeply tied to applied work. Programs like the AI Residency and internal machine learning education paths focus on building long-term judgment, not short-term productivity. Engineers and researchers spend significant time working on real systems while learning how models behave under real constraints. Evaluation, data quality, and responsible use are treated as core skills rather than afterthoughts.

2. Amazon’s Machine Learning University:

Machine Learning University has expanded beyond early experimentation into a sustained internal capability. Teams across technical and non-technical roles are trained to understand how AI decisions are made and how those decisions affect downstream systems. The emphasis is on internal relevance. Training is grounded in Amazon’s real use cases, which makes it easier for teams to apply what they learn immediately.

3. Microsoft’s AI School:

Its role-based structure teaches engineers, data practitioners, and leaders follow different learning paths based on responsibility and risk exposure. Responsible AI is integrated into training rather than positioned as a separate topic, which reflects how AI is actually used inside large platforms and products.

4. Meta’s Engineering Readiness:

AI education is embedded directly into engineering readiness. Before engineers ship systems that rely on AI, they are trained on how those systems behave, how they fail, and how to intervene when behavior changes. This approach reflects a clear understanding that AI training is inseparable from production readiness.

5. IBM’s AI Skills Academy:

The focus is on enterprise realities. Training emphasizes governance, deployment, and operational decision-making rather than experimentation alone. This reflects the kinds of constraints most large organizations face when AI systems intersect with customers, regulators, and long-lived infrastructure.

Most organizations will not build internal AI training programs at the scale of Google or Amazon. Instead, they rely on external partners to accelerate capability building across teams. In 2026, the distinction between “courses” and “training programs” matters more than ever.

The providers organizations choose increasingly reflect whether they are optimizing for awareness or real capability.

1. TryTami (for teams and organizations):

TryTami focuses on live, customized, role-based AI training programs designed for how teams actually work. Rather than teaching generic courses through videos, TryTami’s programs are taught by live experts and built around real workflows, real decisions, and real constraints.

2. DeepLearning.AI (self-paced e-learning):

DeepLearning.AI continues to be a strong option for building foundational understanding of AI and machine learning concepts. Many organizations use it to raise baseline literacy, especially for technical teams. It is most effective when paired with applied training that helps teams translate theory into practice.

3. Global Knowledge (traditional IT training provider):

If your organization already buys instructor-led technical training, Global Knowledge is one of the most familiar names. It offers an AI & machine learning course catalog and delivers training in formats enterprises expect (virtual classroom, private team delivery, etc.). Global Knowledge is part of Skillsoft.

4. Fast Lane (vendor certified training):

Fast Lane publishes enterprise-focused material around upskilling teams in generative AI and ML, especially in the context of major vendor ecosystems (for organizations training across AWS/Microsoft-style stacks).

5. OpenAI (for enterprise customers):

OpenAI supports enterprise customers with applied workshops and enablement focused on using models responsibly within real workflows. This often includes guidance on agent design, evaluation, and governance rather than generic model usage.

This checklist is designed to help leaders quickly assess whether their organization is actually ready for AI in 2026, especially as AI agents become more common.

1. Strategy and Direction:

☐ We have a clear point of view on how AI supports our business, not just where it’s being used
☐ We understand which workflows should involve AI and which should not
☐ Leadership agrees on acceptable levels of automation and risk

2. Skills and Training:

☐ We run structured AI training programs, not just tool rollouts
☐ Training is role-specific for engineers, leaders, and operators
☐ Teams are trained on evaluation and judgment, not just usage
☐ AI agents are explicitly covered in training, not treated as future work

3. Evaluation and Oversight:

☐ Teams know how to assess AI output quality consistently
☐ There are clear standards for when human review is required
☐ We can explain how AI decisions are made in critical workflows

4. Agent Readiness:

☐ We understand where AI agents are already acting autonomously
☐ Guardrails and scopes of autonomy are clearly defined
☐ There are clear escalation and recovery paths when agents fail

5. Governance and Risk:

☐ AI policies are actively applied in real workflows
☐ Accountability is clear when AI contributes to decisions
☐ Teams understand data, bias, and security implications in practice

If you checked fewer than half, you are not alone. Most organizations are here. The gap is usually not intent or investment. It’s the absence of structured, applied AI training programs that help teams build shared judgment.

Many organizations use AI readiness frameworks like this to realize they don’t need more tools.

They need training programs that help teams operate at higher layers of the stack, especially evaluation, agent oversight, and governance in real work.

That’s where live, customized, role-based AI training programs like those from TryTami are increasingly being used to close the gap more quickly.

Request a demo of TryTami’s training operations platform below to learn more:

Until next Tuesday,
Kelby, Dean, & Dave

About the Authors:
This article was written by the TryTami team, who work with engineering, enablement, and learning leaders to design training programs that build real technical capability. With decades of experience, TryTami focuses on helping organizations close skill gaps faster by automating training operations and connecting leaders with vetted instructors.