Generative AI is poised to bring a new level of speed, simplicity, and personalization to enterprise customer support - and to enterprise products and solutions to prevent problems from happening in the first place; it has the potential to redefine customer experience by hiding complexity and providing simple natural language interfaces that interact with humans in a conversational way.
Complex large language model configurations and data sets can give life to well-recognized avatars that people can chat with individually or in group chats. These companions can offer advice to customer support and success teams, enterprise customer end users, and professionals working to provide best results for companies in sectors such as finance, energy, and transportation.
Generative AI enables customer support and success teams to provide better results faster
Imagine the phone rings at Hitachi Support. It’s a customer calling with a synchronization issue. This customer can’t synchronize an external bucket to its HCP for cloud scale bucket. The synchronization times out, so this customer keeps getting a weird X27BB12 error code.
However, the Hitachi Support representative on the call has never seen code X27BB12. The representative’s next step typically would be to search through documentation for answers.
But now - through the power of Gen AI - the human Hitachi Support representative can just have a conversation with a Hitachi Support companion trained on support and configuration data like product documentation, the last 17 years of support tickets, and petabytes of logs.
The companion can immediately point out that the error the customer has been seeing has been fixed on a previous patch, share the location of that patch with the Hitachi Support representative, and provide links to other relevant information. After verifying the suggestion, the support representative can share the solution with the customer. Poof! The solution has worked, and the customer reports that the error is now gone.
A little later, however, the phone rings again. The patch worked, but the system is now underperforming. This time, the Hitachi Support companion can’t identify an answer with a high confidence rate, so the companion summons other Hitachi Support companions to enter the chat. One of the companions suggests that the customer scale up by adding more nodes to the system. Another companion suggests that the customer implement an elastic data plane.
The human Hitachi Support representative passes the information to the customer, who says that his company was considering adding more hardware anyway, so the rep also shares that news with the sales team. Our team and the customer work together, and the error is gone.
Embedding Gen AI companions into products can prevent errors from happening in the first place
We are building Gen AI-enabled companions within Hitachi Support in this way right now. But, over time, companies like yours and mine could use companions in a much wider variety of ways.
As we build confidence in our AI models, we will enable a direct line of communication between our companions and our enterprise customers to go from 'reactive' to 'proactive' mode by preventing problems and offering pro-active remediations. By building these capabilities as a reusable framework, we can embed companions into our products. Progressively packaging companions into all our products will provide a range of benefits - improving user experiences, reducing support costs, and identifying upsell opportunities.
As McKinsey writes, “with the proper guardrails in place, generative AI can not only unlock novel use cases for businesses but also speed up, scale, or otherwise improve existing ones.”
Some of companions can be trained with specific data and address well-defined needs. Training others using broader data sets will position them to tackle more strategic use cases.
- A companion that’s focused on configuration could recommend some configuration changes based on the latest white paper on blueprints for a storage solution.
- A different companion could alert customers when a mandatory patch for their particular IT product is available and offer to schedule a time to install that patch.
- A companion with broader knowledge could identify an opportunity to optimize a customer’s storage. This companion might inform the customer that migrating to the latest version of a gateway, for example, will double its speed and capacity, save 28% on service costs, provide them with free auto-upgrades, and reduce their carbon footprint.
Businesses can also build companions to meet the unique needs of specific industry verticals
By building and training a squad of companions who specialize in different industry sectors and continue to learn, businesses like ours will be able to provide added value to priority verticals.
In banking and finance, for example, a companion could flag potential fraud and update a rules-based security system. A different companion could work to safeguard client investments, and anticipating a drop in the U.S. dollar, suggest that clients update their default trade currency to Euros. A third companion might advise a financial services firm to consider adopting a data catalog to enable it to get insights on its most beneficial Euro assets. American Banker reports “banks are eager to adopt generative AI” and notes a KPMG report indicating financial institutions are ramping up investments and searching for the best ways to apply generative AI.
Using generative AI to address risk, build wealth, gain new insights, reduce costs, and serve up exceptional customer experiences is a journey. But we believe that companions will soon make their way into IT hardware, such as storage, to solve problems that have not yet been solved by understanding individual IT infrastructure issues and suggesting how people can address them.
A recent Harvard Business Review article authored by Harvard Business School and Boston Consulting Group experts notes that, "we are now at the point where competitive advantage will derive from the ability to capture, analyze, and utilize personalized customer data at scale and from the use of AI to understand, shape, customize, and optimize the customer journey.”
We absolutely believe that to be true - for small to mid-size to large enterprise customers.
