BY KANNAN KOTHANDARAMAN 3 MINUTE READ
Every company that sells technology promises magic. From the early days of email to the countless platforms and programs available today, technology has always promised something faster, easier, automated, or intelligent.
Through decades of innovation and transformation, countless products have inevitably fallen short of their promise. Today, the market is crowded and prospects are skeptical when considering new solutions.
As founder of an AIOps startup, I’m quite familiar with the promises AI and machine learning have made in the past. In business and pop culture, the magic of AI has promised to drive our cars, be our assistants, make medical diagnoses, and more. In reality, we’ve made huge advancements in the applications of AI and machine learning, but we’re a long way from HAL and the Terminator. Regardless of the technology you’re introducing, convincing customers your solution will meet their needs comes down to credibility. Those that get it right have three things in common.
TEAM IS THE FOUNDATION
The most essential element for success is the team. Before you can attempt to solve a problem, the team needs firsthand experience with it. Why does it matter to your customers? What’s been promised? What’s failed? What’s needed?
In the beginning stages, your team’s experience is a foundation of credibility for prospective customers. To that end, it’s crucial to build a team that has both technical expertise in data science and engineering, and domain expertise in the product category. They don’t always come from the same talent pools.
Top technical talent is in high demand and spread across a variety of industries. Don’t limit your search. Domain expertise, on the other hand, requires extensive experience in your vertical. Look for candidates with experience working on and managing teams that have been in the trenches of the problem you want to solve.
TECH FROM THE GOUND UP
Once you’ve identified the customer’s problem, find the white space. What are the gaps in the current solutions? Start small with the specific issues you address and focus on the architectural underpinning of the platform first. Broad applicability is crucial in technology, but it’s easy to overextend by trying to satisfy too many use cases.
That’s not to say scalability is unimportant. It’s crucial. Your solution should accommodate a variety of implementation scenarios and allow customers to add on as needed, but that’s only possible with the groundwork in place. Resist the urge to rush to production. A strong technical foundation from the start will pay dividends long into the future.
CREDIBILITY ISN’T BUILT IN A DAY
Regardless of how amazing and groundbreaking your technology is, if implementation doesn’t go well, you’re dead in the water. How do you ensure a successful implementation? Think “land and expand.”
Leverage the credibility you’ve established with your team and technology to land a proof of concept or small engagement. Once you’re in the door, remember your customer has tried to solve this problem before. Set realistic expectations of what your platform can deliver then aim to exceed them. It’s easy to over-promise in the initial conversations. Focus on demonstrating results with one or two outstanding implementations and the solution will upsell itself.
Don’t be afraid of taking a co-creation approach with key customers either. In the early stages, it’s essential to gather and quickly take action on customer feedback. With the right platform architecture from the start, expanding the product in collaboration with the customer is an ideal way to build credibility and expand.
No matter what you’re introducing, be aware that it’s been promised in the past. Hype cycles are exciting when they’re in your favor, but real customer credibility doesn’t come in waves. In the end, the right team, technology, and implementation is the only formula to prove the value of your product where others have fallen short.
Kannan Kothandaraman is CEO and co-founder of Selector, the AIOps platform for operational intelligence.