These are exciting times for anyone involved in employee learning. As generative AI becomes mainstream, the learning content authoring possibilities seem endless. However, cool content tools don’t guarantee successful learning outcomes. So, what should organizations keep in mind, going forward? Before we look to the future, let’s briefly rewind…
Do you remember Eliza – one of the very first chatbots? I vividly remember interacting with her on my Dad’s computer when I was a girl in the late 1970s. That’s when my Dad started working as a computer programmer, so I was regularly exposed to software programming and early digital games like Eliza. In my young mind, she seemed brilliant, engaging, and magical.
But my, how Eliza and I have changed and grown over the years! Now, Eliza is like the great-great-great-grandmother of the latest generative AI applications. Meanwhile, I’ve become a human resources professional, focused on building and transforming organizations – including groups responsible for talent management and learning.
AI at Work: Are You Ahead of the Curve?
As many have noted, natural language processing is rapidly changing how we work. This technology is already so pervasive that businesses, governments, and ethics philosophers are urgently debating its potential effects on our society.
Just like the internet, biotech, stem cell therapies, and bitcoin, AI is a disruptive force. It is causing organizations to fundamentally redefine all kinds of business activities. But it is becoming pervasive much more swiftly than previous breakthroughs. That means those who want to gain an advantage from AI can’t afford to wait.
This is an opportunity for those of us in people-focused functions to lead change by helping our organizations rethink work and benefit from AI-driven innovation. In particular, learning teams should be at the front of the line.
Organizational Change and Learning
Recently, a client asked me to help transform their learning organization. The team was small and highly siloed, with traditional development and facilitation roles. But this team and its leader are perceptive, engaged professionals. So they knew change was required 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 fully reimagine the learning function. By leveraging recent AI advances, this transformation could not only address corporate learning needs but also solve an age-old organizational problem.
Challenge: Learning Content Exposes Process Gaps
Recently, I talked 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. Plus, the process itself isn’t fully defined.”
Unfortunately, this lament is all too common among corporate learning teams. When developing learning content, they’re usually at the front of the line, investigating untested workflows and decision paths. As they figure out how these processes come together, they uncover gaps and disparities.
Often, when organizations ask for a new learning program, the details of underlying business processes and content have yet to be worked through. This means learning professionals become a critical link to process documentation. In fact, sometimes the first communication employees see about a change comes from a learning program.
Here’s the problem: Work is usually defined by multiple functions – process engineers, internal communications, HR, and business owners. Each group drafts content to suit their particular needs, whether it’s process documentation, policies, procedures, or training. Coordinating this content can be difficult, requiring multiple sign-offs and resulting in messaging differences.
Any change in an existing system or process also requires the same complex coordination among multiple groups. This can lead to bureaucratic bottlenecks that slow down transformation.
Solution: Learning Content Coordination
To avoid content disparities and duplication, organizations can harness valuable skills and tools available from their learning teams. Specifically, by relying on AI and adjacent tools, it’s possible to develop complete learning programs while simultaneously creating related policies, procedures, and communications content.
In this new age of AI, we have a head start like never before. This means the traditional instructional designer/learning consultant role can merge into a multi-purpose business specialist who aligns learning strategy with desired organizational outcomes.
Next-generation learning consultants will need to understand and stay abreast of ongoing technology advances (such as generative AI and simulation tools), focusing on how and when to apply these tools to improve learning experiences and business results. In addition, they’ll need to focus keenly on translating business strategy into agile, adaptive learning solutions.
This means they’ll also need to anticipate, recognize, and address potential process gaps and challenges. Ideally, they should be responsible for creating phenomenal employee experiences that foster a culture of learning and innovation.
A successful learning consultant will need to be skilled in relationship building, change management, and translating business strategy into measurable learning outcomes. In addition, they will need to know how to gather, analyze, and interpret meaningful data to communicate the effectiveness and value of their programs.
Ultimately, this role can become a central, strategic player in upskilling and reskilling the workforce.
Next-Level Coordination: The Content Center
Although publicly available AI tools are helpful, the need for coordinated, company-specific knowledge creation and sharing will continue. 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 this kind of centralized function, you can ensure that content is created once for repurposing wherever it is needed. AI can transform core content into learning programming, policies, procedures, and internal communications deliverables. This significantly simplifies the process of coordinating information to be sure it is complete, accurate, and consistent.
A successful content center will depend on AI literacy and competency. It can be staffed with one or more professionals who are also skilled at 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, whether the source is company-specific or generated from an external AI engine.
This kind of center offers multiple benefits:
- Reduced Costs – There will be no need for numerous employees to write about the same subject in different modes. This should significantly reduce staffing requirements over time, or free those resources for other projects.
- Decreased Duplication – No need to write and edit training content, procedures, communications, and other materials multiple times. Write once and rely on AI to develop diverse deliverables built on common messaging. A content center could perhaps be subject-matter-focused. However, the primary goal should be to reuse the core content for multiple purposes.
- Increased Consistency – Because content is generated from the same source, the need to check for consistency will be negligible.
What’s Next for Learning Content?
As expected, my old friend Eliza isn’t much help. Not so with modern AI. Innovation in AI is continuing at breakneck speed. It’s causing us to ask valid questions about how to create learning content more efficiently and effectively, and how to 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 nimble in defining our roles and redesigning our organizations for the future of work. It is always a work in progress.
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