AI Ready: Scale AI for business outcomes with this framework

Organizations are investing in AI tools and deploying them throughout the business. But when it comes to getting value from these tools, most organizations come up short.

The problem isn’t the technology. It’s how organizations adapt and integrate it into their existing workflows. 

Eric Van Gorden, Senior Product Manager, and Merwin Moss, Lead Solutions Consultant, explain why organizations continually run into these challenges and how Pluralsight’s AI Ready solution can help you overcome them.

Watch the on-demand webinar.

95% of generative AI pilots fail. There are three key reasons for this, according to Eric.

Too often, organizations adopt AI without rolling out training for employees to use it effectively. 

“You see it represented in token waste, costs increasing, and really no outcomes to prove the value of AI,” says Eric.

Even when organizations provide AI upskilling, they tend to take a one-size-fits-all approach. They lack the data needed to tailor training to their employees’ unique needs and capabilities.

“Organizations typically see teams that use AI highly, while some don't use it at all. So it's very inconsistent. Understanding where an organization is, and level setting across the organization, is very hard to do without a guided training program,” explains Eric.

Last but not least is stale investment. “AI is changing quickly,” says Eric, “and if your training programs aren't keeping up, you're not going to see benefits and outcomes [with AI].”

Overcoming these challenges and getting value from AI investments requires a multimodal approach—and that’s exactly what AI Ready delivers for engineering teams.

AI Ready takes a program management approach to upskilling, blending assessments, hands-on learning, and capstone projects with a program manager to help you build real-world expertise and measure success against business outcomes.

“At the end of the day, it's all about providing validation and proof, making sure that your learners are capable of executing within your own codebase, without any of the risks that come along with using agentic coding,” explains Merwin.

AI Ready works across five key levels to help engineers reach the next stage of skill proficiency.

Note: AI Ready currently focuses on Claude Code. GitHub Copilot, Codex, and others will be added over time.

The first level focuses on accelerating speed and maximizing ROI by turning tool access into verified engineering output.

“It’s about reducing friction across your organization, mastering token cost management, and making sure you're using things in an optimal way across the organization,” says Eric.

To give learners a high-level understanding of AI-assisted coding and Claude Code, this level includes: 

• Skill assessments to get a baseline of employee capabilities

• Courses from industry experts 

• Our AI assistant Iris that learners can use to personalize the learning journey, ask questions, and check their understanding with real-time feedback

• A hands-on lab that gives technologists real experience working with Claude Code (without using your own token budget)

• A seminar on context engineering where they learn to control output quality, configure project memory and instructions, set up permissions, and more

“The content has been developed in order to take the learner from using autocomplete to potentially using AI as a coding assistant to help them to be more effective and efficient,” says Merwin.

Level 2 includes more courses, labs, and seminars focused on developing agents with architectural guardrails and design patterns that reduce risk and exposure.

“As we start to implement more and more agents into our overall ecosystem, and they are autonomous, we have to really understand the risks that are involved in that process and mitigate them along the way,” says Merwin.

“This is what’s important to Level 2, where we focus on advanced Claude Code aspects, and applying governance and security in the Cloud Code space.”

Level 3 takes it a step further by focusing on multi-agent workflows and human and AI agent collaboration. This level includes courses and seminars to help technologists orchestrate and scale multi-agent systems across the business.

“This seminar is all about decomposing a task across specialized sub-agents, delegating safety with scoped and read-only sub-agents, or even gating the agent execution behind human approval,” says Merwin.

Level 4 is where your teams prove they’ve learned new skills with an assessment and capstone challenge. 

The assessment is a criterion-referenced assessment with 34 multiple choice questions covering the information in the AI Ready curriculum.

“Once employees have completed this assessment, we understand if they passed or if they've failed. And if they’ve failed, then we have course materials to close those gaps in knowledge,” says Merwin.

“But as an organization, and as a learner, we then know that we have the knowledge and capability to execute effectively and efficiently when it comes to autonomous agents and building end-to-end workflows.”

Learners then move on to the capstone project: a code-along challenge focused on building and shipping a complete agentic coding workflow. “This is a full end-to-end validation that learners can build in a real-world scenario,” explains Merwin.

Level 5 provides continuous learning to keep up with new models, tools, and developments. This includes:

• AI sandboxes. AI sandboxes are individual cloud environments with AI services. Technologists can spin them up, practice with new AI, and then spin them down as much as necessary to become proficient.

• Prompt sandboxes. Prompt sandboxes allow learners to compare various LLMs (like Opus vs. Sonnet) and understand their differences without incurring costs within your organization.

• Generative AI First Look. This learning path contains short courses designed for anyone who wants to understand the latest AI models, emerging developments, why they matter, and how they can impact your organization.

“Unlike traditional training that goes stale the second it's delivered, AI Ready is built as a true compounding asset that helps scale your organization over time,” says Merwin. 

“By continuously refreshing our labs, instructor-led training content, and capstone challenges, the program scales alongside the changing models and tools, ensuring your team always stays ahead of the curve.”

Want to see AI Ready for yourself? Check out the on-demand webinar to watch Merwin’s demo.

If you already have a learning platform with AI-focused content, what value does AI Ready bring?

“Think of it like a gym,” says Eric. “There's great tools in the gym. You can use them as you see fit. But if you really want to achieve a specific goal, you might need to layer in some personal training.

“AI Ready is that personal trainer to ensure you’re bringing solution thinking and driving towards business outcomes. It adds those structured pathways for assigned cohorts. It goes through that objective skill validation and an end-to-end programmatic experience.”

Adds Merwin, “Without a unified architecture and skill framework, AI spend turns into a shadow tool sprawl with inconsistent output and stalled ROI. AI Ready was created to close that gap.”

Watch the webinar to see AI Ready in action or learn more about AI Ready for developers.