Today many organizations are applying AI and GenAI to automate or augment existing processes, eliminating steps in image creation, content creation, customer interactions, and coding automation. While these tactical outcomes scratch the “efficiency” itch, they don’t enable larger transformations. AI and GenAI can help us completely reimagine how work is done. That reimagination starts with intent – defining a much larger end goal.
A current typical AI/GenAI project follows a pattern. For example, a customer service center dealing with large volumes of tactical inbound questions. The interaction process steps are analyzed. AI consumes a range of scripts and product manuals. A chatbot is deployed for automated responses. The seven-question process is trimmed to five before a customer gets to an agent.
The result? Some customers are efficiently served and never reach a live agent. However, many of those that get through are frazzled, requiring agents to spend extra time reviewing cases and even more time getting the customer over their frustration and to some relative satisfaction.
What if, instead of only automating the interaction front end, call center leaders considered a larger goal or intent based on value? They might focus on what drives NPS scores above 80 since they know it leads to higher customer retention and revenue. This entirely reframes how they would use AI or GenAI.
What leads to a stronger NPS score? If ten things normally drive scores, how many can be augmented? If five can be automated and the other five enhanced, the service team could direct more interactions to value-add consulting for retention and upsells. While changing how the technology is used, the intent approach also aligns project measurement with bottom-line value instead of only cost savings.
And a focus on intent leaves ample room to incorporate human domain knowledge and expertise. With intent-led design, automation enhances the human in the loop’s ability to achieve a larger goal instead of only improving efficiency.
In a recent engagement, we helped a power generation organization reimagine its customer interaction process. 80% of their inbound calls were about billing issues. Together, we defined the program’s intent: getting service agents into energy use conversations vs. answering billing questions. We took a multi-pronged approach, which included a chatbot to manage some front-end inquiries, and overall billing process changes to create more up-front transparency. Complaint call volume significantly decreased, and the team now has deeper discussions with customers about sustainable and renewable energy options, a main corporate goal aligned with strategic revenue opportunities.
How do you move beyond tactical AI and GenAI to a more strategic and intent-led approach? First, you must have a strong data foundation in place. Access to trusted and relevant data from structured and unstructured sources is central to delivering individualized user experiences that drive AI and GenAI adoption and success.
Second, consider the desired outcome and all the processes involved. Don’t focus on just eliminating or automating steps. Think through how AI/GenAI and data can enhance the journey to that goal. Design thinking is a great way to reimagine the path to intended results.
At the same time, you need to provide staff with a personalized upskilling strategy based on roles and use cases. This helps employees feel comfortable when using AI and GenAI while also applying their domain expertise to help reimagine the journey.
AI and GenAI can enhance and improve every product, process, and experience. Understanding and building strategies based on intent aligns resources, technology, and human skill sets for the greatest impact.
