How are organisations using generative AI for workforce development?
What’s next for corporate L&D as we enter a new wave of edtech innovation: AI-powered learning
“AI is going to impact overall human potential. So let’s think of areas that will be important for that: skills development, job matching, helping people build resumes and navigate their way to next-gen careers, personalised learning content with meaningful learning objectives, translation of content for a global audience… It’s so rich to think about the opportunities for AI and L&D in this space.”
Tamar Elkeles, Open Sesame
On a wintry afternoon in central London, 35 senior leaders from innovative global organisations gathered for the first ever in-person meeting of the Emerge WD edtech leadership group, chaired by Donald H Taylor, to talk about the future of learning and development in an AI-powered world. Their goal: to act as the sector’s innovation radar, finding the hottest new trends and championing edtech solutions that really make an impact for businesses.
Here’s a snapshot of our discussions.
Read on for …
- The biggest problems in workforce development that require innovation to solve
- How AI can help solve those problems — and which areas are ripe for innovation
- How you can get involved in our research
What are the biggest problems in WD that require innovative solutions?
In a wide-ranging discussion, our HE board identified the biggest problems in workforce development right now that are not being met by edtech companies or other providers, and that require innovation to solve:
- Future of work → reskilling at scale and at pace using skills foresight data.
- Skills assessment → reliable, quantitative validation to address skills gaps.
- Content → Everyone can now create content but changing learner expectations (social learning, gamification, edutainment) and harnessing the ubiquity & variety of content into meaningful outcomes remain challenges, esp. for SMEs.
- Connecting learning to business impact → create a common culture of learning purpose.
- Metaskills → cultivating curiosity, learning to learn and growth mindset for real behavioural change.
- Interoperability / data aggregation → seamless integration of tools underpinned by joined-up data and connected communities of practice.
- Navigating the edtech landscape → support to separate “must-haves” from “nice-to-haves”.
“I was not surprised to see metaskills featuring so strongly on this list — we’re seeing and hearing a lot about the skills needed to develop skills. So-called ‘soft skills’ or ‘human skills’ create openness to develop technical and competency-based skills.”
Natasha Davidson, Group GTI
How is AI disrupting things?
We asked our HE edtech leadership group, how have your feelings about the role of AI in L&D changed in the past 12 months? A clear majority (69%) said they feel more optimistic and another quarter (25%) felt about the same after the innovations, debates and controversies of the last year. In fact, not a single respondent said they felt less optimistic than a year ago.
So how is that feeling reflected in practice? We asked for our WD board’s impressions about how pioneering or on the back foot their organisation is in relation to its peers when it comes to adopting AI as part of L&D so far. We then captured the most common barriers to adoption of AI and examples where it is successfully being used in workforce development already.
“There is so much FOMO. The word I hear all the time is ‘overwhelm’ — ‘we can’t keep up’. For me, that’s what this speaks to. If we’re not sure where we are, we naturally mark ourselves down a bit or calibrate our status against the FOMO status. Right now everyone’s doing boom-box stuff — the old things, bigger. Next we’ll start doing old things in new ways. Then we’ll do new stuff, but we can’t predict that yet.”
Donald H Taylor
From these barriers and use cases, it’s clear that a co-pilot or “human in the loop” approach is the right one for most organisations right now. This means AI is a faster way to do what we do today, rather a totally new way of doing things.
Finally, we asked for our group’s reflections on the potential benefits that AI could enable for workforce development, as well as the risks that it could pose. The responses we received were clustered around three core areas: organisational efficiency, learning, and workforce planning.
“The first shift in automation was ‘hands’; the second will be ‘heads’. Frontline workers will be the winners of AI, not the ‘cognitive classes’, and what’s big now are the caring professions because that will be the special thing that’s impossible to replace.”
Josie Cluer, partner, EY
Where will AI make a big difference?
At Emerge, we have identified eight high-level trends — what we’re calling “engines of opportunity”. These eight “engines of opportunity” capture our ideas about how AI is being used to drive better practice and outcomes in HE, now and into the future.
They fall into two main categories:
- Making learning more engaging: solutions that scale high quality pedagogy at low cost.
- Making teaching more efficient: solutions that save educators and organisations time and money.
Our WD leadership group took a deep dive into three categories that we think will be transformative for workforce development. Here are their thoughts.
Automated assessment
What are you most excited about?
- If we can crack this it will enable the move to genuinely skills-based organisations, which have the promise of being more equitable and inclusive. If skills can be reliably assessed, that unlocks talent mobility because people can be up for promotions or lateral moves in a zigzag approach.
- There’s a big opportunity in hiring, more specifically the ability to hire based on skills and potential, not CV and experience.
What are you most wary of?
- To date, this category has mostly been focused on academic integrity in universities. This is easier because it involves individual feedback against a specific question and set of criteria. It’s harder to assess human skills and teamwork. What does good look like and what are we measuring?
- GenAI allows stealth assessment — in a good way, as a continuous process. How willing are workers to be monitored and assessed all the time? Some worry that it’s not okay to fail. A huge mentality shift has to happen among workers to overcome fear of raising head. Will workers try at all costs to show they’re okay at something because their career progression, security or pay is dependent on it? But workers might feel more comfortable being more vulnerable with tech than with humans, depending on results, such as if only “the robot” knows and it’s giving feedback on how to do your job better. We need to then rethink the role of performance reviews, line managers and even L&D as a function.
Always-on feedback
What are you most excited about?
- We now live in a world where more and more of our work lives are being recorded, whether through browser extensions, video call recordings or even screen recordings. This means we have the data to enable generative AI to capture our work products, evaluate our performance and provide us with detailed feedback on how we can improve.
What are you most wary of?
- Always-on feedback can be used for improving performance feedback and performance management systems, but AI can miss critical “offline” conversations. This provides a great opportunity that’s best used for roles where performance can be easily measured using online systems.
- Caution about “feedback overload” and making sure that feedback is balanced, with a focus on personalised development and improvement. Coaching might be required to help people know how to manage feedback and implement behaviour changes. It’s also important to provide opportunities for people to practise and apply the feedback before providing additional feedback.
AI knowledge graphs
What are you most excited about?
- If you think about how we access our data today, as individuals or as organisations, you quickly realise that it’s broken: it’s stored in very fragmented systems, and so it’s almost impossible to find what you need or to get insights out of our data. Enterprise knowledge management can enable companies to capture unstructured/fragmented data, categorise it, and make it easily searchable or queryable through natural language processing to drive efficiency.
- Efficiency of knowledge transfer (which is not the same as skills development, but is rather the infrastructure underpinning it) can help break down silos across teams / information sources.
- Use cases could include using all of a company’s performance management data to train a company-specific LLM for skill assessment, hiring and performance coaching.
What are you most wary of?
- ‘Garbage in, garbage out’ — can the knowledge graph properly weight different sources (documents vs Slack conversations, old vs new) in terms of quality?
- AI is not good at handling ‘quirky’ information — for example, the weather, which can be both factual and a forecast.
- Feedback loops; how can we be sure that these are adequate, since “knowledge” is some way away from business outcomes?
From enrolment to offboarding: the user journey
Over the coming months, we will explore these AI edtech categories in-depth in relation to every step in a learner’s journey with an organisation — from discovery, application and interviews, through onboarding to upskilling, soft skills, career progression, mentoring, coaching, reskilling and beyond.
We will publish frequent research pieces on these topics, including market maps to plot the existing and emerging players at each step in the user journey — and we will publish case studies showcasing innovative practice at organisations from around the world. In each case we’ll explore what’s working and why — and what other organisations can learn from it.
We want to hear from you
- We’ve got some ideas of our first few case studies but we want to draw on your expertise and contacts — which organisations would you like us to approach? Have you come across great examples you would like us to look into? Please share!
- We’d also love you to recommend exciting edtech companies that you have seen for our market maps.
This research will underpin Edtech 20:20 vision — the list of top global emerging edtech companies that we publish every July. You can see the 2023 list here.
At Emerge, we are on the look-out for companies (existing and new) that will shape the future of workforce development over the coming decade.
If you are a founder building a business across any of these areas, we want to hear from you. Ultimately, we believe that these are the businesses that will play a critical role in solving the skills gap, and our mission is to invest in and support these entrepreneurs right from the early stage.
So if you are looking for your first cheque funding do apply to us here: https://lnkd.in/eWi_9J5U . We look at everything as we believe in democratising access to funding (just as much as we believe in democratising access to education and skills).
Emerge is a community-powered seed fund home to practical guidance for founders building the future of learning and work. Since 2014, we have invested in more than 80 companies in the space, including Colossyan, FutureFit AI and SkillsTrust.
Emerge Education welcomes inquiries from new investors and founders. For more information, visit emerge.education or email hello@emerge.education, and sign up for our newsletter here.
With special thanks to all members of the WD edtech leadership group, led by Donald H Taylor.
Thank you for reading… I would hugely appreciate some claps 👏 and shares 🙌 so that others can find it!
Nic
