In an interaction with Enterprise Times, Sameer Nigam, CEO and Co-Founder of Stratbeans, discusses the challenges of preparing workforces for AI adoption, highlighting the importance of personalised learning, skills development, knowledge management and human capabilities in an AI-driven workplace.
Enterprise Times: What challenges do organisations face in preparing employees for wider AI adoption?
As organisations move from experimenting with AI to using it across everyday operations, the real test is not how many AI tools they deploy, but whether employees can use them to improve decisions, customer service and productivity. An expensive AI rollout can still produce modest returns if people lack the skills and confidence to apply it in their roles. The World Economic Forum’s Future of Jobs Report 2025 found that 63% of surveyed employers identified labour-market skill gaps as a leading barrier to business transformation. That makes workforce readiness a CEO issue, not merely an L&D issue.
At Stratbeans, we help organisations tackle this through practical application linked to workplace requirements. Bytecasting LMS supports role-based learning, assessment and visibility into skill development, while SIM Studio enables employees to practise real business conversations before they face a customer or colleague. As roles and workflows change, organisations need to identify capability gaps, update learning pathways and evaluate how well people can apply what they have learned. In my view, AI adoption becomes meaningful only when it changes what employees are capable of delivering.
Enterprise Times: How can AI-powered learning help enterprises identify workforce skill gaps and deliver more personalised learning based on individual roles and business requirements?
The starting point for personalised learning should be a business-critical capability gap, not a generic course recommendation. Traditional training programmes often follow a common structure, even when employees have different responsibilities, experience levels and learning needs. AI can help bring together assessments, learning activity and performance against defined role competencies to identify where someone needs support. But those competencies must be grounded in business requirements, such as in a better sales conversation, safer operations or fewer customer escalations.
For instance, a sales professional who needs to improve negotiation skills may benefit from scenario-based practice, while a manager may need support with feedback conversations. With Stratbeans SIM Studio, employees can rehearse such situations through AI-enabled roleplay and receive structured evaluations before handling real business conversations. Bytecasting LMS can deliver and track the relevant learning journeys. Personalisation should be guided by clear business objectives and human judgement: the test is not whether AI assigned different content, but whether the employee demonstrates greater proficiency.
Enterprise Times: How can organisations use learning-in-the-flow-of-work to help employees build and apply new skills without stepping away from their everyday work?
Learning in the flow of work is about reducing the time between an employee facing a problem and being able to solve it correctly. For a frontline or customer-facing employee, stepping away for lengthy training sessions is unrealistic, particularly when products, processes and policies keep changing.
A sales employee, for example, will need a short explanation of a new product feature just before speaking to a customer. A service professional will need the latest approved escalation procedure while handling a complaint. Relevant knowledge, short modules and practical guidance should be accessible at that point of need, and not buried in a course catalogue. Specially for multi-location enterprise training programmes, consistent, timely access to information matters a lot.
AI can make this experience deeply contextual by helping employees access relevant, approved information and practise specific skills. Structured learning remains essential for deeper capability building, but everyday performance also depends on having the right knowledge at the moment of action. The objective is not to replace training with search; it is to make learning useful where work happens.
Enterprise Times: What are the biggest challenges in driving adoption of AI-enabled learning, and how can organisations improve employee engagement with these platforms?
One of the biggest challenges is relevance. Employees are unlikely to engage consistently with a learning platform if they see it as another task rather than something that helps them do their jobs better. Limited time, varying levels of digital confidence and weak managerial support compound the problem. Leaders can make it worse by celebrating enrolments and course completions without asking whether employees can actually perform the tasks their roles demand.
In one large frontline learning programme, Stratbeans integrated gamified, role-relevant assessments into an existing learning management system. Results reported in August 2026 showed a 28% increase in skill competency scores and a 36% rise in participation and course completion rates. These results demonstrate how relevant assessments can strengthen both learning engagement and demonstrated capability.
The lesson for AI-enabled learning is that adoption improves when learning is relevant, manageable and reinforced by managers; the technology alone does not guarantee engagement. Short modules, repeated practice and timely feedback help, but enterprises should ultimately measure proficiency, time-to-readiness and changes in on-the-job performance, not only logins and completions. A learning platform has succeeded when the business can see a difference in how people work.
Enterprise Times: How can AI-powered learning and knowledge management help enterprises capture institutional knowledge and make it more accessible to employees across functions and teams?
Institutional knowledge is one of an organisation’s most valuable assets, but it can also be a hidden productivity risk. It sits across documents, internal systems, training materials and the experience of individual employees. When experienced employees move to new roles or leave the organisation, others can spend valuable time searching for answers, repeat mistakes or escalate problems that should have been resolved independently.
AI-powered knowledge management can make this information usable at the point of need. Stratbeans Knowledge Copilot is designed to help employees ask questions in everyday language and find guidance grounded in approved organisational content, instead of searching through multiple repositories. For example, a frontline employee could locate the relevant policy, product guidance or process step and verify the underlying source. This can improve the consistency of onboarding, customer support and roles that depend on complex procedures.
However, making knowledge accessible is only part of the solution. Organisations must also ensure that answers are accurate, up to date, appropriately governed, accessible only to authorised users and traceable to trusted sources. At Stratbeans, we see knowledge management and learning as connected but distinct: access to an answer helps someone act now; structured learning, practice and assessments help people build the capability to act independently next time. The opportunity is to turn institutional knowledge from something employees search for into something that helps them perform.
Enterprise Times: As AI adoption accelerates, what should enterprises prioritise in their learning strategy to build an AI-ready workforce while also strengthening critical thinking, problem-solving and other human skills?
Building an AI-ready workforce requires more than teaching employees how to use AI tools. Employees must know where AI can add value, how to evaluate its output and when human judgement must take precedence. I would focus on three priorities: practical AI fluency, role-specific readiness and distinctly human judgement.
First, organisations should build AI fluency across roles, with clear expectations around responsible use, privacy and security. Second, they should develop role-specific capability through practice, simulations and assessments that demonstrate proficiency, rather than simply recording training attendance. Third, they should strengthen critical thinking, communication and problem-solving by asking people to challenge AI-generated outputs, recognise limitations and make informed decisions in real work situations.
AI can help employees access information, practise scenarios and receive feedback, but people still need to interpret context, exercise judgement and take responsibility for decisions. The question I would encourage CEOs to ask is simple: are our people better equipped to make decisions and deliver results than they were six months ago? That is a more meaningful measure of AI readiness than the number of AI tools an organisation has deployed.
