Build AI skills
for the work you will do.
AI changes each role differently. Kashyap helps leaders and professionals develop the understanding, practical skills, and judgment their work requires—through learning shaped around their responsibilities.
Different responsibilities.
Different learning needs.
Role-specific upskilling is central to Kashyap’s educational work. A board member needs to oversee AI; a finance professional needs to assess its economics and use it in financial work; an HR leader needs to prepare people for changing roles.
Learning starts with what participants do today, where AI changes that work, and what they need to do well next. AI Profs, a sister firm of RPA2AI, focuses on professional education, training, and publishing. Formats include tailored briefings, workshops, and onsite, remote, or blended learning.
What does your team need
to do well with AI?
The audience’s responsibilities shape the examples, practical work, and depth of learning.
Executives & boards
Set AI priorities, assess investment proposals, and ask informed questions about performance, accountability, and risk.
Finance & investment teams
Apply AI to financial analysis and research, check its outputs, and examine the costs and assumptions behind AI investments.
Lawyers & governance teams
Evaluate AI use in professional practice, understand its limitations, and develop oversight that respects confidentiality and professional duties.
HR & learning leaders
Identify changing tasks and skills, plan learning for different roles, and assess the responsible use of AI in people-related decisions.
Technology & information teams
Connect business needs to AI capabilities, prepare information for use, and evaluate systems for reliability and responsible deployment.
Marketing, sales & operations
Find useful applications in daily work, develop effective ways to use AI tools, and recognize where human review remains essential.
Skills people can bring
back to their work.
Prepare information for AI
Assess whether your organization’s information is ready for AI, understand how generative AI changes information management, and turn that assessment into an AI roadmap. The curriculum connects data preparation with governance and responsible use, supported by worksheets and knowledge checks.
Put generative AI to work in finance
Explore applications in investment management, investment banking, and private equity. Develop relevant skills through practical exercises, while learning to recognize unreliable outputs and manage confidentiality, intellectual property, and security risks.
Start with the work.
Build the right skills.
Tell us which roles you are preparing, what is changing, and what people need to do differently.
- Audience: roles, experience, and responsibilities.
- Purpose: the questions or decisions learning should support.
- Format: briefing, workshop, or a more sustained program.
- Context: sector, existing tools, and preferred timing.
For a conference or public audience, explore speaking and conversations.
Questions about learning to lead with AI.
What should executives learn about AI?
Executives need to prioritize applications, assess risk, assign accountability and set limits on agent authority. Effective learning should support the decisions they actually make.
Learning should give leaders a way to examine proposals and question assumptions. Understanding the language of AI helps, but it should lead to better choices about where to deploy it and how to oversee it. Kashyap’s skills analysis connects technical knowledge with the business and collaborative capabilities needed for adoption. Read more about the AI skills organizations need.
Is prompt training enough for a professional AI program?
No. Professionals also need to verify outputs, protect information and recognize when AI is unsuitable. Learning should reflect both their role and the systems they use.
Practical exercises should expose the limits as well as the strengths of the tools. For example, an answer that draws on internal documents may still omit relevant material or introduce errors. Kashyap’s analysis of retrieval-based AI explains why access to sources does not remove the need to evaluate the result. Read more about the limitations of retrieval-based AI.
Should every employee receive the same AI training?
A shared foundation helps, but proficiency should be role-specific. A finance leader, engineer and operations manager face different decisions, risks and responsibilities.
Begin with the responsibilities people hold and the gaps they need to close. Foundational learning can be shared, while deeper work should address the situations each group encounters. Kashyap’s reskilling guidance recommends connecting training to real tasks and revising roles as the work changes. Read more about role-specific AI learning.
What new skills are needed to supervise AI agents?
Teams need to understand permissions, handoffs, failure recovery and escalation. Supervision involves managing actions and consequences, beyond checking a generated answer.
People must be able to recognize when the system is behaving outside its intended scope and know how to respond. Training should therefore cover the surrounding workflow and responsibility for failures. Kashyap’s workforce-skills article explains why agent supervision calls for capabilities beyond ordinary use of a chatbot. Read more about building AI skills across the workforce.
What is AI Profs?
AI Profs is a sister firm of RPA2AI, focused on education, training and publishing. Its work connects AI knowledge with the needs of professional and leadership audiences.
What is your next AI decision?
Talk to us about your business priorities, an investment, or your team’s learning needs.