These are exciting times for everyone involved in employee learning. As generative AI becomes mainstream, the possibilities for authoring learning content seem endless. However, cool content tools do not guarantee successful learning outcomes. What should organizations take into account in the future? Before we look to the future, let’s rewind briefly…
Do you remember Eliza – one of the very first chatbots? I vividly remember communicating with her on my father’s computer when I was a girl in the late 1970s. Then my father started working as a computer programmer, so I was regularly exposed to software programming and early digital games like Eliza. To my young mind she seemed brilliant, captivating and magical.
But my goodness, how Eliza and I have changed and grown over the years! Now Eliza is like the great-great-grandmother of the latest generative AI applications. In the meantime, I have become an HR professional, focused on building and transforming organizations – including groups responsible for talent management and learning.
AI at Work: Are You Ahead?
As many have noted, natural language processing is rapidly changing the way we work. This technology is already so widespread that companies, governments and philosophers of ethics are urgently debating its possible effects on our society.
Like the internet, biotechnology, stem cell therapies and bitcoin, AI is a disruptive force. It causes organizations to fundamentally redefine all kinds of business activities. But it is becoming ubiquitous much faster than previous breakthroughs. That means those who want to benefit from AI can’t afford to wait.
This is an opportunity for those of us in people-centric roles to lead change by helping our organizations rethink their work and take advantage of AI-driven innovation. Learning teams in particular should be at the front of the queue.
Organizational change and learning
Recently a client asked me to help transform their learning organization. The team was small and very isolated, with traditional development and facilitation roles. But this team and its leader are astute, committed professionals. So they knew change was needed if they wanted to help their business grow and succeed.
As we explored ways to improve, it became increasingly clear that this was an opportunity to completely reimagine the learning function. By leveraging recent AI advances, this transformation could not only meet the learning needs of businesses but also solve an age-old organizational problem.
Challenge: Learning content exposes process gaps
Recently I spoke with a frustrated instructional designer who was struggling to define a key term. “Everyone I talk to has a slightly different definition, and that changes the learning content. Moreover, the process itself is not yet fully defined.”
Unfortunately, this lament is all too common among corporate learning teams. When developing learning content, they are usually at the forefront, exploring untested workflows and decision paths. As they figure out how these processes come together, they discover gaps and differences.
When organizations request a new learning program, the details of the underlying business processes and content often still need to be worked out. This means that learning professionals become a crucial link in the process documentation. Sometimes the first communication employees see about a change comes from a learning program.
Here’s the problem: work is typically defined by multiple functions: process engineers, internal communications, HR, and business owners. Each group creates content that meets their specific needs, whether it is process documentation, policies, procedures or training. Coordinating this content can be difficult because it requires multiple signatures and can cause discrepancies in messaging.
Any change to an existing system or process also requires the same complex coordination between multiple groups. This can lead to bureaucratic bottlenecks that slow down the transformation.
Solution: coordination of learning content
To avoid substantive differences and duplication of effort, organizations can leverage valuable skills and resources available within their learning teams. In particular, by relying on AI and adjacent tools, it is possible to develop complete learning programs while creating related policies, procedures and communication content.
In this new era of AI, we have an advantage like never before. This means that the traditional role of instructional designer/learning consultant can merge into a cross-functional business specialist who aligns learning strategy with desired organizational outcomes.
Next-generation learning consultants will need to understand and stay abreast of ongoing technological advancements (such as generative AI and simulation tools), focusing on how and when to apply these tools to improve learning experiences and business outcomes. Moreover, they will have to focus sharply on translating the business strategy into flexible, adaptive learning solutions.
This means they also need to anticipate, recognize and address potential gaps and challenges in the process. Ideally, they should be responsible for creating phenomenal employee experiences that foster a culture of learning and innovation.
A successful learning consultant must be skilled in relationship building, change management and translating business strategies into measurable learning outcomes. Additionally, they will need to know how to collect, analyze and interpret meaningful data to communicate the effectiveness and value of their programs.
Ultimately, this role can become a central, strategic player in the upskilling and reskilling of the workforce.
Coordination at a higher level: the Content Center
While publicly available AI tools are useful, the need for coordinated, business-specific knowledge creation and sharing will remain. This is why a dedicated content center makes a measurable difference as a single source of business process information. HR and learning leaders can work side by side to make centralized content development an organizational reality.
With these types of centralized features, you can ensure that content is created once and then can be reused wherever it is needed. AI can transform core content into learning programming, policies, procedures, and internal communications products. This greatly simplifies the process of coordinating information to ensure it is complete, accurate and consistent.
A successful content center will depend on AI literacy and competence. It can be staffed with one or more professionals who are also skilled in process mapping, process improvement, writing and developing clear organizational information.
Content center staff should be responsible for personalizing AI content and refining it for accuracy and human tone, regardless of whether the source is business-specific or generated by an external AI engine.
This type of center offers several advantages:
- Lower costs – It is not necessary for multiple employees to write about the same topic in different forms. This should significantly reduce staffing needs over time, or free up those resources for other projects.
- Reduced duplication – There is no need to write and edit training content, procedures, communications and other materials multiple times. Write once and rely on AI to develop diverse results based on common messages. A content center might be focused on the topic. However, the primary goal should be to reuse the core content for multiple purposes.
- Increased consistency – Since content is generated from the same source, the need to check consistency will be negligible.
What is the future for learning content?
As expected, my old friend Eliza isn’t much help. Not so with modern AI. Innovation in AI continues at a rapid pace. It causes us to ask valid questions about how we can create learning content more efficiently and effectively, and how we can help our organizations make better use of what we create.
We can do more. Learning teams and HR can be at the forefront of this change. We must remain flexible in defining our roles and redesigning our organizations for the future of work. It’s always a work in progress.
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