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Generative AI is creating enormous opportunities for productivity, innovation, and transformation. But technology alone cannot guarantee success. Organizations also need people who know how to implement, scale, and use AI effectively. With professional skills evolving rapidly and the AI skills gap widening, building an AI-ready workforce has become one of the biggest priorities for businesses today. Hello everyone, welcome to the Analytics Insight podcast where we talk about the ever-evolving tech space and I'm your host Priya Diyalani.
Today, we're joined by Kashyap Dalal, Co-founder and COO of Simplilearn, to discuss how organizations can prepare their workforce for the AI era, how learning platforms are evolving, and what skills professionals need for the future.
Kashyap: AI has made upskilling much more central to business strategy. Most leadership teams are now asking what their AI strategy is and what productivity or competitive advantages their AI initiatives can deliver.
But having AI tools isn't enough. If employees aren't comfortable using AI or don't understand how to apply it effectively, organizations will see only superficial changes, such as using AI to draft emails or build basic chatbots.
AI is different from traditional skills because it is horizontal. It affects almost every function, from marketing and product development to technology and customer support.
Therefore, organizations need to think about AI readiness continuously rather than treating training as an annual initiative.
Kashyap: I see three major changes. First, we're moving from static content to adaptive learning systems. AI changes so quickly that a course created several months ago can already become outdated. Learning content therefore needs to be dynamic and continuously updated.
Second, AI itself is becoming part of the learning process. It isn't just something people need to learn about, it can also become their learning companion.
For example, Simplilearn's multi-agent AI system, ALBI, can help learners determine what they should study, clarify doubts, support projects, and conduct mock interviews based on their individual learning history.
Third, learning needs to become much more outcome-focused. Instead of simply watching videos or completing courses, learners should build things, such as applications, bots, or websites and demonstrate that they can actually apply their skills.
Kashyap: The first and most important step is to clearly define the business objective. From our experience, organizations typically move through four phases of AI adoption.
Phase one is personal productivity using AI tools to help employees perform their existing jobs faster and better. Phase two is building AI agents automating repetitive tasks and creating workflows where AI handles processes while humans remain involved for important judgment calls.
Phase three is reorganizing for AI, rethinking roles, identifying new responsibilities, and developing skills for emerging positions.
Phase four is reimagining the product and customer experience using AI to fundamentally rethink what the company offers and how customers interact with it.
The important point is that the skilling strategy should be different for each phase. Once the organization knows which phase it is targeting, it can determine which people need which skills and learning experiences.
Kashyap: AI is changing the traditional separation between planners and doers. Traditionally, planners included people such as marketing managers, product managers, and strategy professionals, while doers included developers, designers, and copywriters.
AI is increasingly bringing those two sides together. The future will increasingly favor what we internally describe as "builder" roles, people who can think strategically but also use AI and technology to execute.
So, professionals shouldn't necessarily abandon their core specialization. Instead, they should add complementary capabilities that allow them to move from planning into execution.
For example, a marketing manager shouldn't only know how to create a brief for a designer. They should increasingly understand how AI tools can help turn that brief into an actual creative output.
Kashyap: AI is changing job roles, but it doesn't eliminate the importance of human capabilities. As machines take on more routine tasks, professionals will need to rely more heavily on skills such as judgment, relationship building, critical thinking, and empathy.
The future isn't simply about competing with AI. It's about understanding how to work alongside AI and combining technological capabilities with human strengths. Professionals who can understand a business problem, use AI tools effectively, and apply human judgment will be particularly valuable.
Kashyap: Organizations need to stop thinking about AI skilling as a one-time training program. AI is evolving continuously, so workforce development needs to become continuous as well.
Companies should first define what they want AI to achieve, identify the roles and skills required to achieve those objectives, and then create learning experiences around those needs. For professionals, the mindset should be similar. Don't think of learning AI as simply adding another certification to your resume.
Think about what you can build and what problems you can solve with AI. The future belongs to people who can combine domain expertise, AI capabilities, and the ability to execute.
Today's conversation makes one thing clear: becoming AI-ready isn't simply about buying new technology or conducting occasional training programs. It requires organizations to continuously rethink skills, roles, learning models, workflows, and business objectives.
For professionals, the opportunity is equally significant. The future isn't necessarily about choosing between human expertise and AI. It's about becoming the kind of builder who can combine both.
To know more about the discussion, listen to the full podcast.
