Accelerated world: How can learning keep up with data and AI?

“There is an inherent competitiveness right now with the AI race: who is going to apply it in the savviest ways to make their business most successful and profitable. If you’re not engaging in that, you are ultimately going to get left behind…The fact is that AI isn’t just helping us do things better and faster — it's going to ultimately change the way work is done.”

This quote, from learning strategist Lori Niles-Hofmann, came from our recent Data Unlocked webinar ‘Building Workforce Capabilities for an AI-Proofed Business’, also featuring Has Dosanjh, CEO, NED, and Trustee of various AI Tech businesses, and Prasad Gollakota, Chief Content Officer at xUnlocked.

The webinar covered a wide range of themes, with key discussion points including:

1. The democratisation of AI: AI has moved from being a background technology to something anyone can easily use. This accessibility has fueled its prominence.
2. Evolving skill sets: There is a need for both technical data and AI knowledge alongside "human" skills like psychology and communication. There's also a shift from needing specialised roles (eg, Chief Digital Officer) to data and AI skills becoming embedded across all roles.
3. Data as a foundation: Data is a critical component of AI, and managing, labelling, and using data effectively is essential. Businesses need to focus on using data for specific purposes and addressing data gaps to improve AI applications.
4. Cultural transformation: Organisations need to undergo a cultural shift to fully embrace data and AI. This includes leadership understanding and driving strategy, upskilling employees, and fostering collaboration.
5. Measuring Impact and ROI: Measuring the impact and return on investment (ROI) of AI initiatives is challenging but crucial. Businesses need to find ways to quantify the benefits, focusing on specific outcomes and cost savings.

In this piece, we explore some of these themes more deeply with Lori, particularly focusing on how today’s learning programmes, such as those delivered by our on-demand learning platform Data Unlocked, can support the development of AI and data skills across all roles, and how to ensure the use of AI is aligned to business strategy and outcomes.

Where to start: The AI-upskilling journey

In 25 years of working in learning and development (L&D), Lori has seen a lot of change in learning programmes. From the days when companies would manually send out books and CD-ROMS, through e-learning and its familiar ‘click next’ to continue buttons, to the personalised digital apps of today, learning has faced the constant challenge to remain relevant and evolve.

But learning now faces its biggest test yet: to upskill the workforce in data and AI, when the pace of technological advances threaten to outpace the ability of learning programmes to keep up.

“The advice I give to learning leaders is to ensure everyone has the fundamentals,” says Lori. “Be prescriptive about that, to ensure everyone reaches a certain competency level, like understanding Large Language Models (LLMs) and Agents. Then, it gets more complicated because specific courses, like ‘AI for project managers’ might become outdated quickly. That’s when I would encourage people to join LinkedIn, or other, communities, where you can talk with experts. There are also Slack channels and informal WhatsApp groups where things move fast,” she says.

The value of short, focused learning

Lori also advocates learning via micro-credentials, which is a flexible way to learn new skills without having to commit the time that would be needed for a traditional educational course. “It’s structured and usually has an accumulative element, like accreditation at the end. The training is done in smaller chunks, like two, four, or six-week increments. The advantage is that it’s accessible, flexible, and can keep pace with changing times,” she says. Lori points to further advantages of micro-credentials, including:

1. The ability to drill down into what’s important to your industry and job.
2. Being able to pivot quickly to learn new skills due to the shorter time invested in each element.
3. Employers are more interested in micro-credentials because they are faster to build, and easier to curate.

She adds a warning, however; such an approach does not make learning faster. “Learning takes time, no matter how you approach it,” she says. “You can make learning more efficient, but the brain can’t learn faster. You have to give people time to learn.”

Potential pitfalls: Overcoming learning blocks

It could be tempting to approach AI learning through a generational lens, assuming older people lack confidence in data and AI compared to younger digital natives. But Lori advises against acting on the basis of sweeping generalisations. She has worked with many older people who are enthusiastic and knowledgeable about AI, while she’s met younger people who are nervous about what the AI future might hold for their careers.

Instead, Lori suggests learning leaders should be aware of the many different pressure points that might impact learning effectiveness. “In times of economic uncertainty, people are worried about losing their jobs, and when stressed, they won’t learn as well. Those in leadership roles may also feel pressure to use AI to gain competitive advantage as quickly as possible, which creates a stressful environment in which they feel there is no time to learn, take stock or think strategically,” she says.

To address this, Lori recommends learning leaders acknowledge potential blocks and respond with understanding and open communication that creates the right learning culture. “AI should be seen as a positive that enhances the way things are done, and there should be appreciation that everyone is on their own learning journey to achieve that. Don’t make competition part of the coaching cycle, make learning a normal part of what people do day-to-day. Making your company a safe place to learn is important,” she says.

Another mistake Lori often sees is business leaders thinking that if everyone knows the basics, they are ready to apply AI. “You need a strategy for how AI will be used. People might know what an LLM is, but not know what to do with that information in their job. You have to connect the dots, lay out how you’ll use the learning, or you’ll create more confusion and anxiety,” she says. This also means that business leaders need to develop their own data and AI skills, or the risk intensifies of AI becoming siloed, with tech professionals expected to come up with solutions in isolation from business strategy.

Best practice learning: Examples of success

During her career, Lori has worked with many different industries developing effective learning programmes. In Lori’s view, the companies that have the best approach combine knowledge management with learning. “These are two buckets of data that have always been separate. When you bring them together, magic happens,” she says. “I see this in large consulting firms with robust knowledge and learning. Bringing together content and good learning practice, opens the way for GenAI to be used for bespoke learning experiences based on your data.”

In addition, Lori’s work with pharmaceutical companies showcased the benefits of taking a more collaborative approach. During the pandemic, pharmaceuticals made their research open source, meaning they shared their data, methodologies, and findings, with other scientists and researchers across the world, to accelerate vaccine development.

“Opening things up changed the mindset to share rather than own the learning. In other industries, learning is more proprietary,” says Lori. “There are still areas where pharmaceuticals keep things closed for competitive advantage, but even here they combine knowledge management and learning, using tools to scrape both to bring them together. You can have a huge amount of knowledge and research, but if you can’t locate and make meaning of it, you’re not using it effectively.”

AI helps with this in the pharma sector, providing tools to compare similarities and differences in trial results, for example. “It enables them to identify patterns that were difficult to see before. These are data-driven industries that were already using some forms of this, but it shows how applying collective knowledge can make a big difference.”

In contrast, some other sectors, especially highly regulated industries, restrict the use of AI tools, to minimise risks of data-privacy and confidentiality breaches. Lori appreciates the concerns but thinks the block on AI often comes down to a misunderstanding: “In regulated environments, you need to be careful about operating procedures and have human oversight. But there are tools that keep the information within your company, it’s not like everything is going out publicly on ChatGPT. There needs to be education on that.”

Banks like ING have led the way here, becoming one of the first banks in 2023 to use Generative AI (GenAI) to develop a customer-facing GenAI chatbot, while maintaining stringent data-privacy protocols. To address the potential risks associated with AI implementation, ING established a 20-step evaluation process, assessing 140 distinct risks, ensuring robust risk management and adherence to data-privacy standards.

Such examples make it harder for business leaders to justify sector-based blanket bans, while accentuating the need for learning.

Leading the learning revolution

Lori has a final piece of advice for business leaders: don’t attempt command and control, but instead give direction on how to enable and use AI. “There’s a value in allowing everyone to play with the tools to encourage innovative thinking. But it can get confusing: ensure there is also the strategic direction to align AI and learning programmes,” she says.

For Lori, this is an exciting time requiring imagination and bravery in the learning world — to abandon some of the preconceived notions about what was considered good, but was purely based on intuition or tradition, rather than real evidence. As she said in the webinar, “this is the time for every industry to abandon some of the ways we always did things, if the data validates doing something else”.

AI is moving at such a pace that there isn’t time to wait for employees and business leaders to absorb new skills organically and over time. Timely training, delivered in a responsive and flexible way, will not only meet the needs of people’s busy lives, but enable them to seize the burgeoning opportunities of today’s data-centric world.

Introducing Data Unlocked

Data Unlocked is an on-demand, expert-led learning platform, offering a comprehensive approach to data and AI literacy, covering topics from foundational skills to more advanced data analytics — allowing people at all levels of the business to engage confidently with data and AI tools in their daily roles.

Examples of Data Unlocked videos include ‘Implementing AI in your Organisation’, in which Data Scientist and Engineer Elizabeth Stanley takes learners through the AI project lifecycle; while the AI Foundations pathway, with Maurice Ewing, Managing Director of ConquerX, and teacher of data analytics at Columbia University, breaks down AI’s strengths and limitations.

Data Unlocked equips the entire workforce with vital data and AI skills, integrating easily into a business’s daily workflow, and enhancing quality, productivity, cost-efficiency, innovative thinking, and employee satisfaction.

To delve deeper into the importance of data and AI literacy, and how organisations can empower their teams to thrive in the data-centric age, read our report, Today solved: Data and AI skills that redefine tomorrow.

Lori Niles Hofmann is a senior learning strategist with over 20 years of LD experience across many industries, including international banking, management consulting, and marketing. Her specialisation is large-scale digital learning transformations, helping companies navigate through the ambiguity of change. After leading and completing numerous EdTech implementations, she has developed the data-based methodologies and frameworks to empower L&D teams to move from business support function to strategic business driver. Find out more at loriniles.com