This is the second of a three-part series focused on the use of AI in corporate environments.
This second piece focuses on AI for training, whilst the first focused on AI for hiring and the third will focus on AI for enhancing productivity.
We will introduce the key areas of innovation in each article, before presenting a market map inclusive of differentiation angles and opportunities for startups.
This article works through:
To jump ahead, click the links above!
Without further ado...
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The evolution of training in corporate environments
Corporate training has undergone a profound transformation in recent decades, accelerating drastically in the last two years.
Once limited to in-person seminars, printed manuals, and rigid classroom sessions, the landscape of professional learning has been reshaped by digital tools, online courses, and data-driven learning management systems.
However, even with these advancements, training often remained static - built around pre-designed modules that failed to adjust to individual learning needs.
Today, AI is not merely enhancing corporate learning but fundamentally redefining how employees acquire skills, adapt to change, and progress in their careers.
Solutions meet learners where they are, both literally (in their cars, in their offices, at home...) and figuratively (regarding their starting point, the way they like to learn and the type of content they see...).
AI-driven learning systems now create personalised experiences, tailor content dynamically, and provide real-time feedback, ensuring employees receive training that is relevant, engaging, and aligned with their evolving roles.
This shift is crucial, given that one-third of all jobs are expected to be transformed by technology within the next five years and given that AI has the potential to automate up to 30% of current work hours by 2030 (World Economic Forum, 2025). These changes will necessitate significant workforce transitions. In response, companies are increasingly shifting from broad, compliance-driven training programs to hyper-personalised, skill-focused learning initiatives.
As Claudio Erba, founder of Docebo, notes:
“Learning platforms are still click-intensive and mainly focused on a few types of outputs, like videos, slides, or quizzes. But we are moving towards a future where learning is automated, immersive, and embedded within work itself.”.
This observation underscores a broader shift in corporate learning: the move from passive, consumption-based learning to interactive, experiential training that aligns with real-world job demands. AI-driven training is not just about efficiency; it is about enabling employees to develop in ways that were previously impossible- adapting their learning paths in real time, simulating real-world decision-making, and integrating seamlessly into daily work.
As noted in a recent McKinsey report, while nearly all companies are investing in AI, only 1% consider their organisations to be at a mature level of AI deployment, indicating a significant gap between potential and current utilisation.
But the momentum is clear- 35% of companies reported training their employees in the use of AI, so adoption/ deployment is only set to accelerate.
The 1% perception of maturity means there's scope to support 99% of companies with their AI deployment... an opportunity we at Brighteye hope to utilise in the coming years.
Part 1 - how is AI transforming training in corporates?
In this paper, we explore five ways AI is transforming corporate training, examining how companies are using AI to build personalised learning paths, automate content creation, track performance in real-time, reduce training costs, and identify future skill gaps.
1. Personalised Learning Paths: AI tailoring training in real time
Traditional corporate training follows a structured, linear path- employees complete modules, take quizzes, and move sequentially through a program. However, AI is making this model obsolete. Instead of static learning pathways, AI enables training to evolve dynamically based on each employee’s progress, knowledge gaps, and performance.
As Jon Lexa, President at Sana explains:
“We are just at the cusp of understanding how to interact with AI-driven learning. AI learning assistants don’t just deliver content; they analyse what a person knows, predict what they need next, and adapt in real time.”.
This represents a fundamental departure from traditional training methodologies, where one-size-fits-all approaches often resulted in disengagement or inefficiency.
Indeed, personalised learning paths have a direct impact on career mobility. AI-driven platforms like TalentMapper use this approach to help employees chart career trajectories, comparing their current skills to industry benchmarks and suggesting training to close skill gaps. This approach democratises career development, ensuring that employees, regardless of background, receive tailored recommendations to help them progress. Interestingly, because AI systems rely on aggregated data rather than human interpretation, they also tend to reduce unconscious bias in career advancement decisions, creating a more equitable workplace (as covered briefly in the 'AI in hiring' piece published last week as part of this series).
As Nicolò Santin, CEO and co-founder of Gamindo, puts it:
"I strongly believe that Generative AI is an extraordinary opportunity to create training content that is no longer the same for everyone but personalized based on the available time, preferred learning method, language, and personal interests. Imagine how much more engaging corporate compliance training could be if, for a basketball enthusiast, it were filled with references to basketball and the NBA. I would be so excited to take that compliance course!”
By shifting from a rigid curriculum model to an AI-driven adaptive learning environment, companies not only improve efficiency but also foster a culture of continuous, self-directed learning. Employees are no longer passive recipients of training but active participants in their own professional development.
Indeed, Lavinia Mehendintu, co-founder of Offbeat, commented
"I believe L&D leaders are generally cautiously optimistic (about the increasing role of AI within personalising learning experiences for employees). Prior to AI tools able to assist L&D professionals with assembling company knowledge and weaving in appropriate L&D support, their work was relatively manual."
2. AI-Powered Content Creation: rapid, scalable, and immersive learning
One of the most transformative aspects of AI in corporate learning is its ability to generate content at scale. AI-driven platforms can now create personalised learning materials, translate training into multiple languages instantly, and generate simulations or role-playing exercises that immerse employees in real-world scenarios.
Claudio Erba, founder of Docebo, highlights this shift, stating:
“AI is not just about creating more content- it’s about making content immersive. We’re now using AI to generate virtual role-playing exercises, where employees practice skills in simulated environments, like negotiating a deal or handling a customer complaint. It’s like a video game but for business skills.”.
This shift is particularly relevant for industries where soft skills- such as negotiation, leadership, and conflict resolution- play a crucial role. Instead of relying on theoretical training, AI-driven immersive learning environments allow employees to engage in active problem-solving. They are able to refine their skills in real-time.
Companies like UJJI AI are taking this even further, turning existing company knowledge into structured training programs. Instead of businesses relying on expensive instructional designers, AI can transform documentation, meeting notes, and best practices into interactive, bite-sized lessons, reducing content creation time by up to 30 times. This efficiency gain is crucial in fast-paced industries where knowledge needs to be updated constantly. For instance, compliance training, key in many sectors like finance and health, can now be continuously refreshed to reflect regulatory changes, reducing the risk of outdated or irrelevant training.
By enabling faster, scalable, and more engaging content creation, AI is not just making learning more efficient- it is making it more impactful.
3. Real-Time Progress Tracking and Feedback: AI as a learning coach
AI’s real-time assessment capabilities are revolutionising corporate training. Instead of waiting for periodic evaluations, employees now receive instant feedback on their progress, allowing them to correct mistakes and refine skills in real time.
Sana’s AI platform leverages this by integrating large language models (LLMs) and Python-powered analytics to track course completion rates, identify struggling learners, and suggest personalised interventions. The ability to collect, analyse, and visualise learning data in real time is giving companies an unprecedented level of insight into workforce development.
Michelle Connon-Roodt, Global People Consulting at EY, highlights another key advantage:
“Employees are often more honest with AI-driven learning assistants than with human coaches. AI feels like an ally- there’s no fear of judgment, which means employees can be more open about their learning gaps.”.
This shift has broader implications for corporate culture. Traditional training evaluations, especially those involving human trainers, often lead to employees underreporting difficulties due to fear of judgment. AI removes this psychological barrier, fostering a learning environment where employees feel safe to admit what they do not know and seek targeted improvement.
Additionally, AI-generated performance data allows HR and L&D teams to make more informed decisions about workforce training needs, enabling strategic talent development rather than reactive intervention...
4. Cost Reduction and Efficiency Gains: the ROI of AI-driven training
One of the most compelling arguments for AI in corporate training is its potential to significantly reduce costs. Traditional training is resource-intensive, requiring facilitators, printed materials, and time-consuming administration. AI streamlines this entire process.
UJJI AI reports that companies using their AI-powered training solutions have reduced costs by 85% compared to traditional methods. Verizon, for example, saw an ROI of 6.9x from AI-driven training, purely from efficiency gains in content creation and intellectual property development.
Furthermore, AI can free up employees’ time by automating repetitive tasks. Hannah Seal, partner at Index Ventures, notes that:
"AI handles 80% of routine work, while people focus on the remaining 20%- the high-value, strategic tasks that require human insight. This shift is reshaping how companies think about hiring, as AI delivers rapid ROI and saves hundreds, if not thousands, of hours of manual effort each month."
This shift is critical in an era where businesses must remain agile and employees need to continually adapt and upskill.
5. Continuous Skill Gap Analysis: preparing for the future of work
The future of work is rapidly evolving, with AI playing a crucial role in identifying skill gaps and preparing employees for emerging job requirements.
As mentioned, AI-powered platforms are helping companies move from reactive to proactive workforce planning. By analysing internal talent data and industry trends, these systems can predict future skill shortages and recommend targeted training initiatives. The solutions providing the skills analysis are not necessarily the same organisations providing the training, so this process is not yet as streamlined as it may become in future.
This shift is not just about efficiency - it is about survival. The European Investment Bank estimates that 60% of EU businesses are facing significant skill shortages, with AI-powered training seen as a key solution to closing these gaps.
Conclusion: AI as the Backbone of Corporate Learning
The integration of AI into corporate training is happening right now. AI is reshaping how employees learn, delivering hyper-personalised, data-driven training that is faster, more immersive, and more cost-effective than ever before.
As AI adoption accelerates, companies that embrace these technologies will build more agile, future-ready workforces. Those that fail to adapt may find themselves struggling with outdated training models, unable to keep pace with the rapid evolution of business and technology.
Learning and Development teams are clearly adjusting their practice to ensure effective delivery of training- Lavinia Mehedintu, co-founder at Offbeat, commented:
"The L&D role is evolving, becoming increasingly similar to roles more typically focused on change management, experience design and behavioural science. It used to be the case that L&D professionals would manage organisational knowledge but with the advent of knowledge management platforms, this aspect is solves, so professionals are required to evolve their skills to ensure the proper use, access and distribution of the knowledge."
Ultimately, AI is not replacing human learning. It is amplifying it. As Claudio Erba puts it:
“Humans must stop thinking their job is just clicking. AI is an assistant, but it is people who must think strategically, leveraging AI as a tool to drive results.”.
The companies that understand this dynamic - and act on it - will be the ones that define the future and succeed in our dynamic environment.
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Part 2 - market map and analysis of opportunities
Market Map Axis:
The market map categorises AI-driven corporate training startups based on two key axes:
1. Content Creation vs. Adaptive Learning (X-Axis)
This axis differentiates between AI applications that focus on generating learning content and those that emphasise adapting learning experiences based on learner behaviour.
Content Creation (Left)
Companies in this category specialise in automating and enhancing learning material production using AI.
AI is used to generate videos, animations, and text-based content, making learning materials more engaging and scalable.
Adaptive Learning (Right)
These companies focus on personalising the learning journey by continuously adapting content based on user progress.
AI helps track user performance, identify skill gaps, and recommend targeted learning materials.
2. Self-Paced Learning vs. Collaborative/Live Learning (Y-Axis)
This axis distinguishes between learning experiences designed for individual, asynchronous training versus group-based, real-time instruction.
Self-Paced Learning (Bottom)
Companies in this quadrant allow learners to engage with AI-driven courses at their own pace.
AI personalises the experience by providing recommendations, automated feedback, and tailored learning paths
Collaborative/Live Learning (Top)
These companies focus on AI-enhanced live instruction, group learning, or team-based collaboration.
AI can act as a virtual instructor, real-time coach, or collaborative knowledge assistant.
Quadrant Breakdown:
Each quadrant represents a distinct subset of AI-driven corporate learning solutions:
Top-Left (Content Creation + Collaborative Learning)
AI automates the creation of live learning content and facilitates real-time, immersive learning experiences.
Top-Right (Adaptive Learning + Collaborative Learning)
AI adapts live learning experiences based on participant engagement and real-time feedback.
Bottom-Left (Content Creation + Self-Paced Learning)
AI automates content creation for users to learn at their own pace.
Bottom-Right (Adaptive Learning + Self-Paced Learning)
AI adjusts self-paced learning materials based on individual progress.
🚧 This market map is in draft and will be finalised by the publication of the final piece on 19th March. If you have comments, feedback or would like to be added, please get in touch with rs@brighteyevc.com! 🚧
Avenues of differentiation
As AI transforms corporate training, companies must develop defensible competitive advantages to differentiate themselves and sustain long-term success.
1. Personalisation & Adaptability
Proprietary adaptive learning - algorithms that improve over time and personalise learning better than competitors.
Data network effects - the more learners engage, the more refined the AI recommendations become.
2. AI-Generated & Immersive Content
Exclusive AI content-generation models - trained on industry-specific datasets.
High switching costs- once companies integrate AI-generated training into workflows, they become dependent on proprietary content tools.
3. Real-Time Collaboration & AI Coaching
Enterprise integration- AI-assisted training embedded into platforms like Microsoft Teams or Slack to enable real-time collaboration.
Unique AI coaching models trained on large conversational datasets, allowing the AI to provide feedback on speech patterns, communication style, and confidence.
4. Specialised Technical & Compliance Training
Government & Enterprise Contracts - compliance-heavy industries (e.g., finance, healthcare) require strict AI governance.
Accreditation & Certification Partnerships - if a company partners with global accreditation bodies, their AI-powered certifications become industry standards (e.g., AWS, Google Cloud AI certifications).
5. Data & Skills Mapping
Exclusive workforce data insights- the more historical workforce data a company collects, the stronger its AI-powered career mapping becomes.
Proprietary AI talent analytics models - models that enable predictive workforce planning, making their insights more valuable over time.
Where is the white space and where are the opportunities?
Below are 6 opportunities where AI-driven startups could differentiate and capture unmet demand.
1. AI-powered emotional intelligence & leadership training
AI-driven role-playing and feedback systems to train leaders in empathy, conflict resolution, and emotional intelligence.
Opportunity:
Traditional leadership training lacks real-time, personalised feedback.
Emotional intelligence (EQ) is a top predictor of leadership success, but most programs are static and generic.
AI-powered conversational role-playing can offer real-time feedback on tone, sentiment, and body language.
Growing demand from enterprises investing in soft skills for mid-to-senior managers.
2. AI-powered workforce agility platform
An AI-driven skills intelligence platform that helps companies predict, map, and dynamically adjust workforce skills in response to technological change.
Opportunity:
75% of businesses in 21 European countries report critical skill shortages, yet most companies lack dynamic workforce planning tools.
Traditional HR tools focus on past skills rather than predicting future skill needs.
Companies struggle with internal mobility & redeployment, leading to higher hiring costs.
3. AI-powered compliance & regulatory training with risk detection
AI-driven automated compliance training that adjusts in real-time based on user mistakes and evolving regulations.
Opportunity:
Most compliance training is generic and arguably ineffective, leading to low retention and high legal risk.
AI can analyse real-world employee decisions and flag risky behaviour in real-time.
Industries like finance, healthcare, cybersecurity, and legal services require constant compliance updates.
4. AI-driven career coaching & internal talent marketplace
A personalised AI career assistant that guides employees through internal career progression & skills development.
Opportunity:
Most corporate L&D platforms focus on training, not career outcomes.
Employees often don’t know which skills to develop to get promoted or move into new roles.
AI-powered internal career coaching could drive retention & internal mobility, reducing hiring costs.
5. AI-powered training solutions for niche & high-skill sub-industries
AI-driven training tailored for complex, high-skill industries (e.g., pharmaceuticals, aviation, legal, manufacturing, logistics, energy, cybersecurity), where general corporate training doesn’t meet specialised needs.
Opportunity:
Most corporate AI learning solutions are generic, while many regulated or specialised industries require industry-specific knowledge.
AI-driven micro-learning & simulations can reduce onboarding time and improve workforce competency in complex, fast-evolving industries.
Industries like renewable energy, aerospace, fintech, legal tech, and pharma require precise, evolving compliance and process knowledge.
High willingness to pay: these industries spend billions annually on workforce up-skilling due to compliance, safety, and innovation demands.
Examples of industry-specific AI training models
Industry | AI Training Opportunity | Potential AI Model |
Pharmaceuticals & Biotech | AI-driven compliance training for clinical trials, drug regulations, and wider approvals | AI-powered micro-learning, regulatory chatbots, clinical research simulations |
Aviation & Aerospace | AI-powered flight safety & engineering simulations for pilot & technician training | AI-driven real-time simulation environments with adaptive learning |
Legal & Compliance | AI-assisted contract review, compliance training, and case law research | AI summarises regulations, generates training materials, and adapts to changing laws |
Manufacturing & Logistics | AI-powered real-time equipment training & workflow optimisation | AI teaches maintenance protocols, detects errors in real-time, and automates safety training |
Cybersecurity & IT | AI-driven hacking & cybersecurity threat simulation training | AI creates real-world security threats, evaluates responses, and adjusts training difficulty dynamically |
Renewable Energy & Climate Tech | AI-powered workforce training for wind, solar, and battery tech | AI tracks skills shortages & personalises training in energy transition skills |
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