How to evaluate AI readiness

According to new research from the Infosys Knowledge Institute, only 2% of 1,500 surveyed firms across the Anglosphere actually consider themselves to be “AI ready.” Here’s a closer look at what’s holding organizations back and what needs to change.

Organizations may understand the potential benefits of AI and may even be eager to adopt the technology, but there’s a difference between AI eagerness and AI readiness — and there are numerous factors that can impact both.

“AI is not being embraced to its full potential by most employers, and it’s not because the tools are complicated or ineffective,” says Leo Goncalves, the vice president of the Workforce Solutions Group at University of Phoenix. “Employers are struggling with AI are because of misaligned skills and siloed training, unequipped managers, leadership ambiguity, and outdated workflows.”

For companies looking to grow their AI readiness, there is hope. Read on for a closer look at how organizations can overcome the challenges. 

AI used to exist solely in the technology department, yet now virtually every department can feel the impact of AI. From learning and development to human resources to sales to marketing, a variety of sectors are discovering AI’s capabilities — but they aren’t necessarily communicating cross-functionally. This is what Goncalves meant by training silos. That can lead to duplicative efforts and knowledge gaps that lead to ripple effects down the road.

Instead, Goncalves explains, department heads need to align on:

• How they set expectations for employees
• How they develop their managers to coach employees
• How leaders measure success 

Another factor that impedes AI readiness can be a lack of clear leadership for its adoption. “AI readiness is failing where leadership is failing,” says Goncalves. “Managers are [frequently] not equipped to coach their teams through this change, and there are often unclear expectations.”

Bypassing such roadblocks starts at the top, Goncalves says. He explains: “Senior leaders of an organization need to think about — and articulate why — AI matters to their company, how people are going to be supported along the way and what the policies are around the technology once it’s in place.”

Leaders must move beyond vague AI messaging and toward measurable workforce outcomes — for example, setting clear targets for skills progression, role readiness and retention tied to AI adoption. When leaders use AI-driven skills intelligence to inform development, they can reduce guesswork and enhance engagement metrics. 

AI isn’t going anywhere, but it is evolving. That means leadership needs to figure out where and how to deploy it within an organization — and update the workflow accordingly.

“Employers are working with workflows that were put into place in a pre-AI world, but now that infrastructure is not equipped to operate in a post-AI reality. If AI is not embedded into the flow of work, it results in inconsistent, isolated pockets of AI adoption. It becomes this side project instead of a performance engine,” Goncalves says.

Outdated:

1. HR distributes general training once per quarter.
2. Managers manually try to assess skills gaps based on intuition.
3. Training is the same for every employee, regardless of role or proficiency.
4. Employees receive feedback long after training is completed.

AI-enhanced workflow:

1. Leadership and HR use AI-powered skills intelligence to identify real-time gaps across teams.
2. Personalized learning pathways are generated for employees based on their skills and goals.
3. Scenario-based AI assessments provide immediate feedback and help employees apply learning in realistic work contexts.
4. Managers can track progress and adjust pathways to better reflect evolving business needs.
5. Data from AI learning tools feeds into retention and internal mobility strategies, helping to show ROI and align workforce development with strategic priorities.

According to the 2025 Career Optimism Index^® study from University of Phoenix, workers who use AI experience several benefits that can influence overall AI readiness. They are more likely to feel hopeful about their careers, for example, and they experience a stronger sense of autonomy in their jobs. Additionally, they report better work-life balance.

All of that can prime your workforce to adopt AI, which is a good first step. But adoption without clear skills, workflows and leadership accountability rarely leads to sustained readiness. Read on for four approaches that can lead to enhanced AI readiness at an organizational level.

Invest time and resources to determine where AI can benefit your business. Then, build a strategy around it.

“The leadership team needs to identify which skills and competencies of AI are relevant to their organization’s goals and provide that clarity to the rest of the organization,” says Goncalves. “Determine which AI skills matter to you and then utilize HR and learning and development leaders as enablers, not the owners of that process.”

To move from intent to readiness, leaders need more than a vision statement. Organizations should use data to identify current and future skills gaps, clarify which roles AI will most affect, and define what “AI proficiency” actually means across job families. When strategy is grounded in measurable skills and competencies — not just tool adoption — AI becomes a workforce strategy rather than an experiment.

“AI can function as a digital team member, yet most companies are not taking the time to create an onboarding process for that team member, detailing what their job will entail,” says Goncalves. “Take the time to rethink how work should be done in this new environment. Then, map out what’s going to be done by humans and what the AI co-worker will take on.”

For example, an organization will need to develop policies, approval processes and human-powered oversight to ensure accuracy and quality when deploying AI in any part of a workflow. Managers should also be prepared to inform and train their direct reports regarding the organization’s AI strategy.

Redesigned workflows should also include how employees learn and adapt alongside AI. Rather than relying on static training, organizations can use scenario-based learning and real-time feedback to help employees practice AI-enabled decision-making in realistic contexts. This approach allows teams to build confidence and capability while leaders maintain visibility into quality, accuracy and performance.

Rather than working in silos, it’s important that all teams speak the same language about AI. “A common language or fluency needs to be developed across an organization around the topic of AI, and it needs to come from leadership,” says Goncalves. “That cross-functional fluency needs to have the manager equipped and trained to oversee not only the human but also the new digital co-workers in order to steer them in the right direction to ensure that adoption is heading the right way.”

Building fluency also requires a shared understanding of skills — not just terminology. When teams align on common AI capabilities and expectations, leaders can more easily identify gaps, tailor development and ensure adoption is consistent across departments rather than fragmented by function.

One way to boost AI readiness is to create an environment where employees can explore AI with no restrictions or expectations. “It’s not about forcing employees to change their ways of working. It’s just about saying, ‘Hey, come explore. Here are all the tools available to you — no strings attached,’” Goncalves says.

He speaks from experience: University of Phoenix has such a program, and he says he has personally seen his team benefit from it.

“I was recently in a meeting with my team, and one of the members shared something she had learned about how AI could help her in ways she hadn’t realized, and she was really excited about it,” Goncalves recalls. “Creating that safe haven where employees can experiment with AI helps them learn about it on their own and naturally get excited about it rather than feel like it’s something they were directed to do.”

From increased productivity and innovation to an improved bottom line, AI readiness brings a host of potential benefits. It just takes a little upfront thinking about where and how AI can help a company and then clearly communicating it throughout the organization.

“AI should be considered a leadership and talent strategy, not an IT initiative,” Goncalves reiterates. “Those companies that treat AI as just a technology project or a technology upgrade are not going to go that far. But the companies that treat AI as a talent and a leadership transformation opportunity, those are the ones that are going to reap the biggest benefits of embracing the technology.”

AI readiness isn’t just a checkbox. It’s a skills and strategy transformation. If your organization is ready to move past curiosity and into measurable preparedness, explore how University of Phoenix uses AI-powered skills intelligence, learning pathways and scenario-based training to help upskill teams and address critical skills gaps. 

Learn more about how your organization can enhance its AI readiness.

Request information about workforce solutions at University of Phoenix.