Artificial intelligence is changing more than the tools people use. It is reshaping job responsibilities, skills expectations, training strategies, and the ways organizations prepare their people for the future.
In the first full episode of Certifying the Future, host Scott D’Amico speaks with Charlotte Evans of Coursera about what companies, learning providers, and professionals should be paying attention to as AI becomes more deeply embedded in the workplace.
The conversation explores why basic AI literacy is only the beginning, how learning is becoming more practical and personalized, and why both technical skills and distinctly human capabilities will matter in a workforce where people increasingly collaborate with AI agents.
AI Skills Are Growing, but AI Literacy Is Only the First Step
Organizations have moved quickly to introduce employees to generative AI. Early training often focused on foundational topics such as writing prompts, understanding large language models, and becoming familiar with new tools.
According to Evans, demand for this type of learning has increased dramatically. She shared that Coursera has seen approximately 14 enrollments per minute in generative AI content and 234% year-over-year growth in generative AI skilling.
But knowing what AI is and how to write a prompt does not necessarily translate into meaningful business results.
The next stage of AI readiness requires employees to understand how these tools can be applied within their specific roles, workflows, and business environments. A salesperson, customer success manager, data professional, and HR leader will each encounter different opportunities, risks, and use cases.
Organizations therefore need to move beyond a single company-wide AI course and begin helping employees answer more practical questions:
- Where can AI improve or automate parts of my workflow?
- Which tasks should remain primarily human?
- What data, technical, or business knowledge is required to use AI effectively?
- How should AI-generated outputs be reviewed and validated?
- What new risks or downstream implications could be created?
The “Yes, And” Approach to Workforce Skills
Evans described the changing skills landscape through the improv concept of “yes, and.” Generative AI may be the starting point, but adopting it successfully creates demand for many related skills.
For example, an organization may want to add an AI layer to its data but quickly discover that the underlying data is incomplete, inconsistent, or poorly organized. That creates a greater need for skills such as SQL, data hygiene, natural language processing, machine learning, and systems thinking.
The same pattern applies to nontechnical skills.
AI tools can generate confident, polished answers that are still incomplete or incorrect. Employees must be able to evaluate those outputs, identify missing context, ask better questions, and recognize when human expertise is required.
That makes critical thinking, judgment, communication, and problem-solving essential parts of AI readiness.
The future workforce will not simply need more AI skills. It will need a broader combination of technical knowledge, human judgment, communication, and adaptability.
AI Must Become Part of the Flow of Work
Many organizations are still experimenting with AI without achieving widespread adoption or measurable business impact.
Employees may use AI to improve an email, summarize a document, or create a first draft. Those applications can save time, but they represent only a small portion of the opportunity.
To create more meaningful value, organizations need to help employees examine entire workflows and identify where AI, automation, and human expertise can work together.
This requires a different kind of training. Instead of teaching people only how a tool works, organizations need to help them understand how to redesign work around the tool.
That could include:
- Mapping existing processes and identifying repetitive tasks
- Finding appropriate opportunities for AI-supported automation
- Establishing clear review and quality-control processes
- Understanding data, privacy, security, and regulatory implications
- Defining which decisions require human oversight
- Measuring whether AI is improving efficiency, quality, or business outcomes
The goal is not simply to give employees access to AI. It is to prepare them to use it responsibly and effectively within the context of their work.
Learning Is Becoming More Personalized and Practical
Traditional corporate training has often relied on standardized courses delivered to large groups of employees. That model becomes difficult when people have different roles, experience levels, responsibilities, and existing knowledge.
Evans explained that advances in technology are making it possible to create more personalized learning experiences. Rather than assigning every employee the same course, organizations can use diagnostics and learner data to create pathways that account for individual needs while still maintaining company priorities and guardrails.
Practice is also becoming a more important part of the learning experience.
Watching someone demonstrate a skill can make the task appear simple. Applying it independently is different. Learners need opportunities to write prompts, build agents, work with data, practice conversations, make decisions, and receive feedback.
AI-supported role plays and learning environments can help create those opportunities at scale. A learner may practice presenting an AI investment idea to a skeptical executive, handling a difficult employee conversation, or applying a new technical skill within a realistic environment.
This movement toward personalized and practice-based learning could help organizations close the gap between course completion and demonstrated capability.
Data Is Helping Learning Providers Keep Pace
One of the challenges facing training providers, universities, and content developers is the speed at which skills requirements are changing.
Evans noted that Coursera serves approximately 195 million learners and uses data from across its platform to identify changes in learner interest, job roles, industries, and regional demand.
That type of data can help learning providers understand which skills are growing, where new content may be needed, and how job requirements are evolving.
For example, the responsibilities associated with a data scientist today may look substantially different from those attached to the same title a decade ago. Training content must evolve accordingly.
This creates an important shift for learning organizations. Program decisions can no longer be based only on historical demand or broad assumptions. Providers increasingly need to use labor-market data, learner behavior, employer feedback, and skills taxonomies to make faster and more informed decisions.
Skills Are Becoming a New Form of Career Currency
Degrees and traditional education remain important, particularly in industries and roles where they are required. But as jobs evolve more quickly, professionals increasingly need shorter, more targeted ways to build and demonstrate new capabilities throughout their careers.
That is where certifications, micro-credentials, and other forms of skills validation can play an important role.
Evans referenced Coursera’s 2025 micro-credentials research, which found that:
- 96% of employers viewed micro-credentials as strengthening a candidate’s job application
- 88% of students were more likely to enroll in a degree program that included credit-bearing credentials
- 85% of students believed micro-credentials would increase their confidence in their skills
These findings suggest that credentials can serve multiple purposes. They can help professionals build confidence, demonstrate continued development, strengthen a job application, and pursue learning without immediately returning to a full degree program.
They can also help employers identify whether candidates and employees are actively building skills that align with changing business needs.
What Employers Should Be Doing Now
Organizations should avoid treating AI training as a single event or isolated learning initiative. Instead, they should build a broader workforce strategy that connects technology, roles, workflows, skills, and business goals.
Employers can begin by:
- Identifying how AI is likely to affect specific roles and departments
- Moving beyond general awareness into role-based applications
- Providing opportunities for hands-on practice
- Developing both technical and human skills
- Creating personalized learning pathways where possible
- Using credentials and assessments to validate progress
- Helping employees reskill into new or changing roles
- Building a culture where continued learning is expected and supported
AI may create opportunities for greater efficiency, but organizations will need skilled employees who can guide, evaluate, and improve the work being performed by these systems.
What Professionals Should Be Doing Now
Professionals do not need to become AI engineers to prepare for an AI-influenced workplace. They do need to understand how the technology may affect their work and what complementary skills will help them remain valuable.
That may mean learning how to use AI for content creation, analysis, quality checking, research, debugging, or workflow automation. It may also mean improving communication, leadership, collaboration, judgment, and stakeholder management.
The right mix will depend on the role.
Technical professionals may benefit from strengthening communication and business skills. Leaders, learning professionals, and other nontechnical employees may need to build greater comfort with data, automation, and AI-supported workflows.
The goal is not to predict every future job requirement. It is to develop the ability to learn, adapt, and demonstrate new skills as expectations continue to change.
Preparing for Human and AI Collaboration
One of the most important ideas from the conversation is that the future of work may not be a simple choice between humans and machines.
It will increasingly involve humans working alongside AI systems and agents.
Employees may need to assign work to agents, evaluate their outputs, manage multiple automated processes, and determine when intervention is required. Those responsibilities will demand a combination of technical understanding, judgment, communication, and management skills.
Organizations that focus only on reducing costs may miss the larger opportunity. With the right training and reskilling strategies, AI can create space for employees to perform more strategic, creative, and valuable work.
Preparing for that future starts with moving beyond experimentation and building the skills required to apply technology responsibly within real work.
Listen to the Full Conversation
Listen to the full episode of Certifying the Future to hear Scott D’Amico and Charlotte Evans discuss AI skilling, personalized learning, micro-credentials, workforce readiness, and what employers and professionals should do now to prepare for what comes next.
About Certifying the Future
Certifying the Future is a podcast from CertNexus exploring how emerging technology is reshaping skills, credentials, and workforce readiness.
Each episode features conversations with leaders across emerging technology, training and education, certification and credentialing, workforce development, and related fields about what is changing, why it matters, and what employers and professionals should be doing now.
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