Conversational User Experience (UX) for AI in Finance

Introduction

Conversational UX is gaining traction in tandem with rapid advancement in AI tech. It seems intuitive that humans would want to communicate with AI agents or bots as naturally as possible. Nothing about conversational UX is new, of course. We just happen to be at a tipping point where various AI technology trends are pushing it into prominence. Substantial research and successful application of that research for real-world scenarios over the past decade have made conversational UX ubiquitous and ready for primetime where the best is yet to come.

Start of an AI-volution

The new age of sophisticated applications using generative AI demands that designers dig deep into the art of conversation. This is an exciting time to explore possibilities to make a dialogue between humans and bots, natural, meaningful, fun, and engaging. On the B2B SaaS front, we are just scratching the surface.

With AI infused in all kinds of finance process automation, there are a ton of possibilities to make conversational UX a key part of such applications. It begs a radical question. What if there is no traditional UI layer in finance applications? Can business outcomes be achieved through good old easy-going conversations between humans and AI solely through a chat window with no regular app interface to speak of? How can designers design and orchestrate the creation of these environments? Designers must investigate, for instance, what’s the equivalent of a casual business meeting in a cafe versus a mission-critical exchange in a conference room about budgeting between a CFO and their AI assistant.

At Hyperbots, designers are exploring ways to create a humane, relatable avatar for the powerful AI capabilities of our platform addressing the automation needs of finance processes like Accounts Payable and Expense Processing for the CFO’s office – processes that are still woefully manual.

The core work that the Hyperbots AI Assistants do are:

• Automating tasks like processing expenses which today involves accountants manually matching invoices with Purchase Orders (POs) and Goods Received Notes (GRNs), flagging invoices with discrepancies or surfacing out-of-policy employee expenses for review and routing these through appropriate spans of control in the company for approvals.
• Onboarding users via conversation and helping them with configuration, setting up workflows, and managing day-to-day tasks with ease.
• Providing real-time assistance and surfacing pertinent financial information instantaneously as against using traditional UI to navigate to the desired content in response to queries.
• Pulling up data analytics around cashflow trends, budgeting, ageing accounts payables and so on. CFOs currently have to depend on the finance team members to get access to data analytics. With the AI assistants depolyed, consequently, the finance team is releived of such mundane tasks and can focus on higher value tasks.
  

Accountant queries can be as practical as asking the AI assistant about invoices that can be safely bulk-approved or the ones that need their manual review.

The AI assistant reponds with real-time actionable data about pending invoices that need manual review.

Analytics critical to business decision making can be easily pulled up with a simple query.

These are sneak peeks into the early work that’s emerging as part of the conversational UX design charter for the design team at Hyperbots. Within broad chatbot categories that exist today, here’s where we might fit in.

Begin here

Designers at Hyperbots know that if they want to create distinct AI Assistant identities, they need to focus beyond the visual elements of an avatar or the UI layer of a dialogue box. They must ask the question what makes a dialogue meaningful? Especially between a machine and a human. They must dive deep into the science of Human-Computer Interaction and the art of conversation. So far our secondary design research has pointed to some seminal work already in the public domain like the recent ethnographic study by NNGroup into usage patterns of ChatGPT, Bing, and Bard users suggesting there could be 6 different types of conversations with generative AI.

1. Search queries
2. Funneling conversations
3. Exploring conversations
4. Chiseling conversations
5. Expanding conversations
6. Pinpointing conversations

Conclusion

These provide a great basis for brushing up on fundamentals and taking the right first step. What should follow is arriving at a solid hypothesis of what specific approaches might work for ur CFOs and their teams and then testing these hypotheses with rigor. 

We are early in our exploration of conversational UX at Hyperbots. We are more than convinced this space cannot remain untapped if we are to create a groundbreaking experience for our customers grappling with legacy applications to conduct their finance operations central to the customer experience we want to build for our CFOs and their teams. As they say, watch this space!