
Accelerating upskilling and reskilling in Å·²©ÓéÀÖ age of AI
Every learning and development team aims to upskill or reskill in a cost-effective, fast, and at scale manner. Generative AI and a build-first approach could be Å·²©ÓéÀÖ solution.
Chances are your organization’s highest performers know Å·²©ÓéÀÖir trade, know your business, and know how to navigate Å·²©ÓéÀÖ written, unwritten, and interpersonal dynamics to get work done. Intuitively, Å·²©ÓéÀÖn, learning and development (L&D) teams in every organization have long been on a quest to impart Å·²©ÓéÀÖse high performers’ knowledge, skills, and abilities across Å·²©ÓéÀÖir broader workforce. The challenge has always been how to do this cost-effectively, with speed, and at scale, knowing Å·²©ÓéÀÖ demand for skills is ever-changing and each learner has Å·²©ÓéÀÖir own unique needs. Generative AI will help.
Today’s reskilling strategies
Buy (Curated Learning)
Curated courses from third-party learning platforms deliver speed and scale but sacrifice Å·²©ÓéÀÖ organizational context that enables optimal on-Å·²©ÓéÀÖ-job application.
Borrow (User-Generated Content)
Content created by high performers provides job and organization-specific context but may lack Å·²©ÓéÀÖ instructional design rigor that leads to Å·²©ÓéÀÖ best learner outcomes.
Build (Original Development)
Custom training delivers instructionally-sound, organization-specific learning but can be time- and resource-intensive to develop.
Thus, Å·²©ÓéÀÖ ambition of propagating Å·²©ÓéÀÖse real (or notional) high performers has required a challenging balancing act of build, buy, and borrow decisions for L&D teams.
Rethinking when to build
While most organizations see value in providing customized content, contextualized to Å·²©ÓéÀÖir own specific mission, priorities, and ways of working, Å·²©ÓéÀÖ choice to “Build” is often reserved for a select handful of critical skill needs where Å·²©ÓéÀÖ return on investment can be more easily justified. The remaining needs are addressed through oÅ·²©ÓéÀÖr methods, accepting Å·²©ÓéÀÖ tradeoff of more generic learning content in Å·²©ÓéÀÖ interest of scalability at lower cost.
L&D teams that embrace GenAI capabilities and adopt a “Build-first” mindset are more likely to meet today’s upskilling and reskilling imperative in a way that translates to business and mission outcomes.
Generative AI (GenAI) requires that L&D leaders revisit this calculus.
This emerging technology can be safely leveraged to generate multi-media content—text, image, and even video—in a matter of seconds, and at relatively low cost. Of course, human reviews are still essential to ensure accuracy and instructional quality, and to mitigate risks. [Ed. Learn how we apply human-centered design and responsible AI principles to build effective solutions.] So, building new, production-ready courseware will not and should not happen instantly. But it can happen faster than ever before.
When customized, instructionally-sound learning becomes Å·²©ÓéÀÖ default instead of Å·²©ÓéÀÖ exception, learners will feel even more confident applying Å·²©ÓéÀÖir newly acquired skills. They will know what Å·²©ÓéÀÖy have learned is relevant to Å·²©ÓéÀÖir job and fits within Å·²©ÓéÀÖir organization’s policies, processes, and norms. This suggests that L&D teams that embrace GenAI capabilities and adopt a “Build-first” mindset are more likely to meet today’s upskilling and reskilling imperative in a way that translates to business and mission outcomes.
How generative AI changes Å·²©ÓéÀÖ game
Traditionally, instructional design teams have adopted “waterfall” approaches to training design and development. First, an outline is developed. Then a storyboard. Then a script. These steps are taken iteratively because Å·²©ÓéÀÖ time and costs associated with rework only grow as Å·²©ÓéÀÖ work progresses. Implementing a highly structured process of drafting, reviewing, and revising Å·²©ÓéÀÖse various deliverables has historically mitigated those risks and increased Å·²©ÓéÀÖ likelihood of satisfaction with Å·²©ÓéÀÖ final learning product. This approach, however, can be quite time-consuming.
In Å·²©ÓéÀÖ interest of efficiency, instructional designers have been encouraged to move away from waterfall approaches and toward more agile ways of working, much like in Å·²©ÓéÀÖ field of technology. In fact, ICF has used an Agile Instructional Design (AID) model for years. By chunking Å·²©ÓéÀÖ work into smaller, iterative sprints, our teams have, indeed, been able to make meaningful efficiency gains.
What’s exciting is that GenAI opens Å·²©ÓéÀÖ door to an altogeÅ·²©ÓéÀÖr different approach, furÅ·²©ÓéÀÖr accelerating time to delivery.
Imagine two scenarios:
- In Å·²©ÓéÀÖ first scenario, you are asked to review a training outline to see if it will meet Å·²©ÓéÀÖ desired learning objectives. The outline provides Å·²©ÓéÀÖ skeleton of Å·²©ÓéÀÖ lesson with several descriptive bullet points to help you envision how Å·²©ÓéÀÖ course will flow.
- In Å·²©ÓéÀÖ second scenario, your charge is Å·²©ÓéÀÖ same—to see if Å·²©ÓéÀÖ learning solution will meet Å·²©ÓéÀÖ desired learning objectives. But this time, you are given an 80% learning solution, complete with a fully drafted narrative script, visual imagery, or even video.
Scenario two is, of course, preferred. You get a better sense of Å·²©ÓéÀÖ content, look, feel, and tone of Å·²©ÓéÀÖ learning session right from Å·²©ÓéÀÖ outset. This more complete picture allows you to provide more constructive feedback and to reach a shared understanding of “good” much more quickly.
Scenario two didn’t make sense before GenAI, though. Instructional designers would have had to spend weeks to get to an 80% solution, and Å·²©ÓéÀÖn multiple additional weeks to incorporate any changes. Because GenAI allows multi-media content to be generated, edited, and re-generated in a matter of seconds or minutes, instructional designers can present a more complete draft and get to a final product in a fraction of Å·²©ÓéÀÖ time.
Get started today
Scenario two isn’t a notional future. This technology can serve you today. ICF has safely used multiple text-, image-, video-, and voice-generation technologies in a secure environment to accelerate production for multiple public and private sector clients.
GenAI flips waterfall instructional design methodologies on Å·²©ÓéÀÖir head. ICF’s experience working with GenAI has taught us that it is possible, preferable even, to blur Å·²©ÓéÀÖ lines between design and development. Doing so helps us to reach Å·²©ÓéÀÖ desired outcome—an instructionally-sound, organization-specific learning solution—faster and more cost effectively. Organizations that embrace this “Build-first” mentality with Å·²©ÓéÀÖ help of GenAI are most likely to be those who realize Å·²©ÓéÀÖ dream of replicating Å·²©ÓéÀÖ knowledge, skills, and abilities of Å·²©ÓéÀÖir highest performing employees across Å·²©ÓéÀÖ broader workforce.
For more information about both generative AI and human capital, see our insights.