Unless you have a functional product in the form and shape of a Minimum Viable Product (MVP), which you test and iterate with real users in real-world scenarios over time, it is hard for a 0-1 startup to achieve product market fit.
Simply put, you achieve product-market fit when:
Once you have an MVP, you can then start measuring PMF. There are plenty of frameworks out there to measure it. We won’t get into that. At this point, you might ask, how do you decide what goes into an MVP? That’s the crucial first step that we’ll discuss in this article.
Of course, no entrepreneur would try to fit a square peg in a round hole. In all probability, you have an idea about who your customer is, what specific customer needs you want to address, and which market segment you want to tap into. Lets assume you also have an idea about how you can differentiate your product offering from the competition and what your USP is.
At best, all of the above is slightly better than a hunch until you validate your assumptions.
At this early stage in your thinking, documents, PRDs, concept notes, elevator pitches, slide decks, and Excel sheets can only impress investors, if at all. What you need are concept sketches, storyboards, task flows, and customer journey maps that will move the needle towards envisioning an MVP and seeking validation from your stakeholders, your investors, and most importantly from your key customer personas. Validation of value pre-position, validation of functionality, and eventually, validation and prioritization of the MVP feature-set.
Heres where enlisting the help of designers can work wonders. If you have a fledgling design team already, great! If not, get hold of an experienced hands-on Principal Designer who will essentially be responsible for the core product design thinking along with you, as you define the functionality, concept-testing objectives, and scope for the MVP.
Where to start? Ask any designer and they will say, start with discovery. Grab a stack of post-its, and markers, walk up to a whiteboard, and begin with sense-making.
And that’s what we did at Hyperbots because it made sense (pun intended). We started by first running a brand strategy workshop with the main internal stakeholders the founders. The brand strategy workshop helped us with these objectives:
Next, we set up the Design Partnership Program with our main customer personnel the CFO. Without input from CFOs, how were we to claim that we are building the best AI for finance? How could we stay true to our stated mission to free up humans from mundane and repetitive finance tasks with AI without getting an intimate understanding of the CFO’s objectives, pain points, and challenges?
Product thinking and design happened in rapid whiteboarding sessions where we fleshed out the core functionality, mocked up screens in Figma and got them ready for the CFO sessions over Zoom.
We enlisted participation from 1, then 3, and eventually from 12 CFOs for conducting weekly remote co-design sessions with them. We gathered crucial insights about finance processes like accounts payables, expense processing, and procure to pay. Together, we delved deep into how finance operations differ in different organizational contexts. We learned about business logic, task flows, finance roles and their needs, the importance of control, audits, and compliance from a CFO’s standpoint, and in turn, we demonstrated through our designs how AI can transform finance operations with natively developed AI assistants to bring new strategic advantages for their companies if they choose to adopt AI-led automation. We showed them how invoice processing, and accruals, can be completely automated, how analytics can be accessed instantly through a conversational AI chatbot, and how it all can be configured to suit their specific business needs.
What’s more, we encouraged participation from the rest of our product, design, engineering, and sales teams as observers during these sessions, so that they hear directly from their customers and learn about the finance domain.
In parallel, the product and design team continued to refine, and sharpen the way our value proposition was being articulated and product described on our new website. It wasn’t easy and it’s still hard to explain the difference between AI-led automation and traditional finance automation.
At last count, we had 70 plus video recordings of the co-design and review sessions, amassing a ton of feedback. Based on this feedback, the design team continues to iterate towards a well-thought-out MVP feature set that can be adopted right off the bat by CFOs and their teams.
As we develop the MVP feature set, we are confident we have a strong product USP and clear differentiation not just from a technology and AI standpoint but also from a UX perspective. With the Design Partnership Program, we were able to validate our hypotheses with enough rigor. We deeply appreciate the close collaboration from our CFOs and are proud to say that they literally designed the AI assistants for finance ground-up with us!
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.
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, whats 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 CFOs office processes that are still woefully manual.
The core work that the Hyperbots AI Assistants do are:
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.
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.
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!
