Three questions to build workforce readiness for AI transformation with Pluralsight

For years, boardroom conversations about AI focused on whether the technology was ready to be rolled out into production environments. Although AI seems to be everywhere today, an estimated 80% of AI pilots still fail to reach production. One important reason for this breakdown is that workforces aren’t adequately prepared to adopt the technology.

Pluralsight is a leader in technology skills development and the Amazon Web Services (AWS) 2025 Emerging Business Applications Partner of the Year, which recognizes rising AWS technology partners with a horizontal business area focus around business applications. Pluralsight helps organizations move from pilot to production by closing the gap between AI preparation and AI adoption.

“Successfully adopting AI comes down to three fundamental questions,” explained Drew Firment, Pluralsight’s vice president of partnerships. How do you know you’re ready for AI? Where are you in your journey? And how do you measure success? The answers can mean the difference between pilots that stall and solutions that scale.

Question 1: Are you ready for AI?

For your organization to use AI effectively, three pillars must be in place: cloud-based practices, security readiness, and a modern data strategy. Without these foundations, projects rarely make it past the proof-of-concept stage:

1. Cloud-based practices are about more than shifting workloads to the cloud. They’re the technical and cultural habits that mean software evolves safely and continuously. This means using infrastructure as code (IaC), taking advantage of elasticity and scalability, and embedding operational best practices throughout the organization.
2. Security readiness is also essential. AI adoption can introduce new vulnerabilities as the potential attack surface spreads across people, prompts, and workflows. Organizations need to have processes in place that protect data and manage exposure while facilitating AI experimentation. Creating an environment where security is viewed as a shared responsibility means AI outputs are more likely to be both reliable and safe.
3. A modern data strategy underpins both cloud and AI initiatives. High-quality governed data is one way AI delivers actionable insights. As noted by Faye Ellis, Pluralsight’s author fellow, “Data is the most important asset you have. It’s the only thing that differentiates and the one way that organizations are going to make money with AI. Without it, you’ve got the same generic model that everybody else has.” Organizations that neglect this foundation often struggle to extract value, regardless of the sophistication of their AI models.

Question 2: Where are you on your journey?

With these fundamentals in place, you can focus on what you actually want AI to achieve. What problems are you trying to solve? The answers shape both the technical approach and the skills your workforce needs. They can help leaders identify the roles necessary for success.

“Workforce transformation actually follows a path similar to technology migration,” said Firment. He outlined three phases:

1. Assess whether employees possess the skills needed to deliver on AI goals. Skills assessments provide a baseline and highlight gaps.
2. Mobilize through hands-on labs and certifications that give employees practical experience with AI technology. “Getting hands-on practice with the latest technology is really essential,” said Ellis. “That’s how you understand what’s possible and can start thinking about how this technology can solve the problems you’re facing in your own business.”
3. Modernize after your workforce has the fundamentals in place. With a cloud-based workforce, teams can start using new technology to optimize and continuously improve.
   Certifications can play a key role in mobilization by creating shared understanding across teams. “A cornerstone to any culture is a shared language,” said Firment. “Any sustainable transition to a new operating model requires a critical mass of literacy in order to create that social consensus around that language.”

When teams understand the AI they’re working with and can talk about it fluently with each other, that’s a hallmark of successful deployment. Upskilling existing employees is often more efficient than hiring externally because your teams already understand how the organization operates and what its business objectives are.

Timelines for workforce transformation can vary, but a structured approach produces results faster. Short-term milestones can demonstrate value within a few months, though comprehensive transformation typically reaches a tipping point within 18 to 24 months. “And then it’s a continuous journey,” noted Firment. “But now you’ve invested in the foundation building blocks, and you’ve created a learning organization.” This structured approach means leaders can measure progress along the way, adjust learning strategies, and confirm the workforce is evolving in step with AI initiatives.

Some organizations are already putting this into practice. Sage, an AI-powered software company, uses Pluralsight’s hands-on labs and skill assessments to drive culture and innovation. “As a business, it’s important to understand where our current skills are, to know where to find skills internally, without relying on word of mouth. Skill IQ is crucial for helping us build the database that drives our internal talent marketplace. We use Pluralsight to collect the data on different skills, so we get a full picture of our inner workforce,” said Damian Robinson, product and technology learning director at Sage.

Question 3: How do you measure success?

Success in AI and workforce transformation shows up in both hard metrics and human feedback. On the business side, look for productivity gains, faster project delivery, fewer errors, and less rework. On the skills side, track assessment scores, certification progress, and lab completion rates. Together, these indicators can paint a clear picture of your AI adoption journey.

Robinson also described how Sage tracks progress over time. “One of the ways we measure success is through Pluralsight’s Skill IQ, used to benchmark real-life literacy, understand how competent colleagues are with certain skills, and how they have progressed over time. It’s important for us to be able to understand collectively what progress has been made over a 6, 12, and 18-month period.”

In practice, these skills show up as teams troubleshooting with more confidence, employees coaching one another, and stronger collaboration between technical and business groups. When people begin sharing knowledge more freely and applying new skills without being prompted, that’s a clear sign that AI readiness is translating into real value.

Priorities and practical advice for leaders

Getting AI into production takes more than the right technology. Leaders who navigate this transition well are intentional about workforce readiness while focusing on a few key priorities:

• Assess organizational readiness, including cloud practices, security, and data maturity
• Invest in your workforce as intentionally as you invest in technology
• Identify the skills required to achieve your intended business outcomes
• Develop structured learning paths combining certifications, hands-on labs, and experiential projects
• Encourage leaders to walk the talk by taking assessments and learning alongside their teams

Invest in your people to unlock AI’s value

Organizations that prioritize workforce readiness by tracking preparedness, growth, and real-world results are far more likely to move beyond the pilot phase and into solutions that scale.

When leaders treat skills development as a strategic investment alongside AI integration, they build both technical capability and a learning organization that drives sustained, measurable impact, adapting as AI continues to evolve.

If you’re ready to assess your workforce readiness and build AI fluency, explore AWS training and certification options today.