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Choosing Your First Generative AI Use Cases
To get started with generative AI, first focus on areas that can improve human experiences with information.
As AI tools evolve at a breakneck pace, they hold the potential to revolutionize how leaders handle change within their projects and across the organizatio
In discussions about AI, leaders often focus on the role of AI in provoking change. They invest time and resources in imagining, developing and delivering AI-based solutions to gather and analyze the data that will tell them how their people, processes and technology need to evolve. With AI, the insights that are critical to that decision-making process are more accessible and they are delivered more quickly.
AI, however, is more than just an impetus for change—it is also highly effective in enabling change. But even within the change management world, it can be hard to understand how to leverage AI effectively. In fact, in a recent survey by Prosci, many change managers say their use of AI is being held back by uncertainty over how to implement and use it.
Overcoming this uncertainty will require understanding where AI is already proving its value—and the areas in which it can only be used to supplement, not replace, humans in enabling change.
Changes to technologies, processes, or organizational structure often prompt organizations to take an intentional approach to managing change. Across these initiatives, change leaders are working toward three primary goals:
Getting to the desired state more quickly. Change takes time; how can you compress the time it will take?
Improving the success rate of a change initiative, which is often lower than companies would like (up to 70% of change initiatives fail, according to some sources). How can you improve the likelihood that your initiative will succeed?
Ensuring long-lasting capability. Once a change has been implemented and change leaders have moved on to the next project, how do you ensure that you’ve created a resilient workforce with a capability that’s here to stay? What can you automate to move away from people-based processes, with knowledge concentrated in a few individuals, working to more process- or capability-based ways of working?
AI can be an enabler in each of those three areas.
The beauty of AI is it doesn’t have to be complicated to be effective. You don’t need a lot of technical assets to be able to run AI programs or technologies—if you have a contract with Azure, Google, or a similar service, you probably already have access to the tools and solutions you need. In much the same way, you don’t need an exhaustive data set to be able to put AI to work—data can be structured or unstructured and it can be as simple as feeding a guidebook into the tools or setting up a private chatbot to get results in hours, not days. In short, there’s no technological hurdle big enough for these change management use cases that would require additional investment in technology.
With the barrier to implementation so low, we’re already seeing organizations leverage AI within their change management practices, with three common use cases helping accelerate, improve the success rate of and sustain change:
Communications
There’s an almost excessive need to communicate during a change initiative. Ideally, you will not only explain and provide context for the change but also personalize messages to each individual based on their personalities, where they are in their career paths and more. Doing this manually requires you to prepare dozens or hundreds of templates in advance, which is time-consuming and challenging.
The amount of customization that is required and the saturation of all organizational communications – not just change communications – provides an opportunity for AI to deliver a more personal experience and alleviate pressure on the people who are charged with managing the massive funnel of messages. With large language models, you can tailor communication to a persona or to an individual quickly and easily. You can even use AI to test messages with different groups to determine the communications that are most effective.
AI can also help you deliver these messages and keep channels of communication open. With an intelligent agent or chat bot, you can provide always-on HR or change management support based on your existing documentation, answering questions like who’s in charge of this new process and where do they sit? Who should I contact if I have a question? What are the new details of our travel policy? AI chat bots have been proven to provide high-quality—and even highly empathetic—responses, even in sensitive settings like healthcare.
Stakeholder Engagement
As organizations move through a change initiative, leaders are hit with a lot of questions: What feedback are we getting? What are people thinking? What questions are people asking? Understanding what’s on people’s minds and the fears and concerns they are facing, is a critical part of engaging stakeholders in the change process.
AI complements human efforts during the stakeholder engagement process by providing data-driven insights, automation and adaptability. Change managers are often charged with listening on the ground and reading a room—activities that are all very human. But humans are only listening from 9 a.m. to 5pm, which gives an AI-driven chatbot an advantage in being able to collect and ingest feedback around the clock.
A chatbot can also analyze the questions it receives as well as employee sentiment. This allows you to monitor any trends in the types of questions employees are asking or in how they’re feeling about the change. Armed with this information, you can then produce targeted new content or schedule a town hall to address widespread concerns. This AI-enabled analysis can also help you identify hotspots of concern or resistance to change within the organization, for example, concentrated within a specific team.
Employee Training
AI allows you to move from dry, formulaic training modules to highly interactive—almost human—instruction. Instead of a one-way delivery of information, training becomes a back-and-forth conversation that engages and speeds adoption. Compliance training is an obvious example; many companies rely on software like e-learning solutions or learning management systems to create quizzes and other interactive content, but it’s a highly manual process. AI can be used to automate rote training, accelerating not only the process of creating training but also the lasting behavioral changes training is designed to foster.
Again, AI-enabled sentiment comes into play here as well. Rather than relying on surveys or rudimentary metrics (like whether someone has completed a training module), AI can help you more effectively gauge adoption and behavioral change.
AI plays a key role in provoking as well as enabling change. But while the technology and the use cases are evolving at a breakneck pace, AI has yet to develop to the point where it can replace a change manager. There’s simply too much nuance involved and too much emotional intelligence and judgment required. By using it to supplement, not replace, this critical function within your organization, you can help your teams work more efficiently and effectively and ultimately keep change on 24/7.
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