Imagine a young professional named Zoiee who has just transitioned into a leadership role. She’s excited but overwhelmed, her calendar is packed, and she barely has time to attend traditional training sessions. One evening, while commuting home, she opens her company’s learning app. Instantly, the app greets her with a 10-minute micro-learning module on “Leading Hybrid Teams,” tailored to her current challenges. It even suggests a podcast for her weekend jog and a quick simulation exercise for Monday’s team meeting. Zoiee feels like the platform knows her better than she knows herself. That’s the magic of AI-driven hyper-personalisation.
Time and technology wait for no one. While the advent and ready adoption of artificial intelligence (AI) have unveiled new avenues for growth, it’s also lifted the veil on skill shortages among the workforce and the inefficacy of traditional corporate learning programs to keep pace with the changing landscape. In a world where roles are evolving every day, and opportunities beckon at every step, businesses must adapt quickly, or risk getting left behind.
Hyper-personalisation isn’t just about knowing who the learner is - it’s about understanding what they’re doing right now. AI-powered systems that integrate with workflow tools (like Teams, Slack, Jira, or CRM platforms) can:
- detect context: Identify tasks, deadlines, and priorities to recommend relevant learning.
- reduce friction: Deliver learning without disrupting productivity - embedded in the tools employees already use.
- boost relevance: Align learning with immediate challenges, making it actionable and timely.
there are a thousand and one reasons to upskill employees – and do so en masse. Employees today require access to learning content that is relevant to the job at hand, in the right format, and at the right time.
In this blog, we explore how AI can deliver hyper-personalised learning to upskill employees, the key L&D metrics to evaluate outcomes, and how businesses can leverage data analytics to fine-tune personalised learning pathways for the workforce.
Wide-scale investment in upskilling has the potential to boost GDP by $6.5 trillion by 2030, according to the World Economic Forum. With such high stakes, it is imperative that businesses continue to invest in learning and development programs and – more importantly – leverage AI to hyper-personalise training modules for their employees, upskill employees more effectively and efficiently, and capture emerging opportunities.
the role of AI in hyper-personalised learning
Unlike traditional systems with uniform training programs, AI and ML systems can create a learner’s profile for every single employee, analyse their skills and training patterns, and curate content that is personalised specifically to them. These hyper-personalised learning systems encourage a diverse set of learning styles, assess intelligently, monitor performance in real-time, and allow individuals to learn on the go. These assessments can also be gamified/simulated using design elements and delivered as ‘fail-and-learn’ scenarios for a highly-enriching engagement.
By identifying learners with similar courses, behaviours, goals and interests, etc., AI-powered L&D programs provide intelligent course recommendations, helping employees discover meaningful courses and trainings that would help bridge gaps in their skills. Advanced L&D platforms even allow simulated live experience in immersive virtual environments, allowing employees to reimagine the nature of their work and get the sandbox experience before they take on the real thing.
key L&D metrics for hyper-personalised learning
While feedback from training sessions is useful in determining the effectiveness of learning, there are also a variety of key metrics that are designed to inform and improve hyper-personalised AI-based learning programs. These are as follows:
- skills acquisition: The most important measure for any learning program is the acquisition of skills relevant to the employees’ roles. While there isn’t a single metric to determine whether or not learning and upskilling have occurred, several markers combine to reveal a holistic picture. Movement within the company, improved performance, increased engagement, and retention of relevant content are all key indicators to measure the success of hyper-personalised modules.
- learning experience satisfaction: Learning yields better results when the experience itself is engaging, relevant to their role, and personalised to their preferences. It is necessary then to also know the learner’s satisfaction levels to determine how the module can be improved further. Besides traditional feedback, pulse surveys and sentiment analysis tools are some ways to gauge the satisfaction levels of employees and ensure the learning experience remains engaging.
- learning ROI: From the business’s viewpoint, it is necessary to know the true value of investing in L&D. But like skill acquisition, determining the ROI for upskilling employees is a composite metric that combines a variety of indicators, such as improvements in performance, retention rates, sales generated, and business impact etc.
- AI-powered personalisation metrics: With personalisation being a major element of AI-powered learning modules, new measures are needed to assess how effectively the AI is tailoring learning paths to individual needs. Rate of engagement, course completion rates, and skill mastery are direct indicators of how well AI is helping businesses upskill their workforce.
- managerial support for learning: Managerial support is essential to enable, encourage, and monitor an employee’s personalised learning pathways. Through regular check-ins, feedback, and follow-up discussions, managers serve a key role in the success of hyper-personalised modules as well as aligning the learner’s goals with business goals.
- learning support utilisation: This metric measures the engagement of employees with the broader learning ecosystem, including LMS platforms, mobile apps, and social learning tools. Tracking engagement and usage across these channels enables businesses to identify the learners’ preferred formats and learning styles. Such insights allow L&D teams to tune their learning delivery methods to the learner’s preferences, ensuring hyper-personalised experiences that lead to faster upskilling and business impact.
- reskilling/upskilling impact: The direct impact of upskilling employees is a critical metric that highlights how personalised learning programs are preparing employees for evolving roles. Businesses can gauge the real-world value of their L&D efforts by linking learning outcomes to internal mobility, promotion rates, and role transitions. Businesses can further utilise this information to improve hyper-personalised learning systems, close the skill gaps, and support the long-term career progression of their employees.
- learning, equity, and inclusion: Learning programs of any kind must ensure that all employees get equal access to training, regardless of role, background, and location etc. To build a truly inclusive hyper-personalised learning program, businesses should evaluate participation rates and engagement across a diverse group of people.
leveraging data analytics in tracking measurements in L&D
Hyper-personalization thrives on data-driven insights. Without analytics, personalization is guesswork. With it, learning becomes precise, contextual, and impactful.
In the age of AI-powered hyper-personalised learning, data analytics has become an increasingly powerful tool that can enhance the effectiveness of corporate learning strategies. To get the best out of data analytics in tracking L&D measurements, a multi-layered approach is called for, allowing businesses to move away from passive, surface-level tracking and toward actionable insights. The following are the four layers – or four questions – that guide businesses in using data analytics to track learning measurements:
- descriptive analytics: Answering the question “what happened?”, descriptive analytics provides an overview of basic learning metrics, such as courses completed, engagement level, participation rates, time spent, etc. These indicators help establish a baseline and monitor daily performances.
- diagnostic analytics: Uncovering the reasons for what happened, that is, “why did it happen”, diagnostic analytics allow L&D teams to identify learning behaviour, engagement levels, and gaps in learning pathways.
- predictive analytics: Anticipating “what is likely to happen”, predictive analytics uses historical and behavioural data to determine the need for particular skills, identify gaps, and map employees who are most likely to benefit from training initiatives.
- prescriptive analytics: Building on previous layers, prescriptive analytics suggests ‘what should we do about it’. Insights gleaned from data analytics become the basis for targeted interventions, such as changing the content delivery format, nudging the learner in a way that facilitates learning, and recommending specific hyper-personalised training modules.
When considered together, the four layers of data analytics help track measurements and continually refine and fine-tune hyper-personalised learning pathways, ensuring that the learning experience remains relevant, purposeful, data-driven, and aligned with business goals.
hyper-personalising learning for a brighter future
In the age of rapid transformation of the workplace, traditional learning models cannot keep pace with evolving job roles and emerging technologies. They way forward lie in AI-powered hyper-personalised learning, delivering the right content, at the right time, in the right format, and tailored to the needs of individual learners. In conjunction with data-driven insights and advanced analytics, businesses can personalise learning at scale as well as track its effectiveness in upskilling their workforce.
Moving forward, businesses that embrace AI-driven personalisation in learning will be better positioned to upskill employees and capture opportunities that keep them ahead of the curve.
how Infosys BPM can help
At Infosys BPM, we help businesses move beyond conventional learning management systems to hyper-personalised training programs that combine AI with data analytics to elevate the learning experience. Our L&D services include intelligent assessment, gamification and enterprise services, and smart virtual event hosting services which provide comprehensive corporate learning pathways, ensuring long-term career progression for the workforce and exceptional business value.
Get in touch with our team today and transform your L&D efforts for a skilled tomorrow.
