We are happy to have interviewed Ben Lamm, serial entrepreneur, co-founder of Conversable and Hypergiant on their pragmatic approach to help enterprises leverage AI and machine learning.
You could possibly be the only one leading 2 AI companies. Can you briefly tell us why you founded Conversable and Hypergiant and the advantages of leading them both?
I noticed that when it comes to AI it’s still very shrouded in darkness and there are a lot of companies selling AI snake oil that has caused serious scar tissue.
Put simply, AI has been over-promised and under-delivered for far too long. It has massive potential to change the way we do business, but not if we bury it in false hope.
Both Hypergiant and Conversable taking a pragmatic approach to using AI and machine learning tactics to transform business at the enterprise level. The advantage is that I always have a holistic view of the business, and what customers need, not just want the industry is offering.
Conversable is a platform focused on bridging brands and customers through intelligence on major messaging platforms. While messaging and conversational intelligence are important to brands strategy right now, it is only one piece to the larger AI puzzle. I founded Hypergiant to help pragmatically solve broader AI issues for brands.
Hypergiant is offering an AI strategy/implementation service to enterprises. As you know, many consulting companies including your previous employer Accenture are also trying to do that. What sets Hypergiant apart?
I try not to focus on what other companies are doing. Given the feedback we have had in market, it seems no one is approaching the space in the same way we are. The services component of Hypergiant is just one of our divisions. When we designed the company, we intentionally looked at the gaps we were hearing from companies that current providers were not filling and completely redesigned the offering model to fill those gaps.
Where we stand out is that a lot of consultancies are not delivering on the promise of the technology. Instead, they are focusing on selling in multi-million dollar strategies when what their customer really needs is a simple and easy way to on-board AI and begin experiencing real business value. It feels a lot like “Big Data 2.0” in the marketplace – laced with high-ticket data warehousing projects and big promises. We favor getting started today. Our growing customer and partner portfolio seems to prove our thesis.
Big picture, we are blending new concepts, methods, and models in the implementation of machine intelligence. Rather than selling technology-first intelligence in point-solutions, we commit to business outcomes.
Tell us a bit about the project hand off at Hypergiant. Leading edge tech projects tend to be hard to hand over. This can be even more challenging in the case of AI projects where a significant share of modern algorithms lack explainability and can fail to perform with changing data as highlighted by Gary Marcus’ recent work: https://arxiv.org/ftp/arxiv/papers/1801/1801.00631.pdf
Part of our success is that we build any intelligence-driven product alongside our customer from start to finish. We are deeply committed to making sure that our product is in lock-step with our customer’s business, and that requires real-time collaboration.
We don’t just build and then hand-off, we help companies identify a problem, develop the solution, and then create an interface that makes it easy for them to use or train from the deployment well into the future. While we get customers started small and fast, we carefully manage expectations that this is a journey, not a destination. Companies need to look at these projects as a fundamental and philosophical shift in the way of looking and utilizing data in their organizations – not as technology solutions.
As you know, pricing for large enterprises tend to be quite opaque. Would be great to have an idea of how Hypergiant and Conversable approaches pricing. For example. learning the price of a typical chatbot, serving a single use case like e-commerce, focused on a single channel could really inform companies.
Conversable charges an enterprise annual software as a service license fee to its customer that is based on the number of deployed conversational channels and backend connectors. In addition to the annual software license fees, Conversable charges professional service fees for the strategy, conversation design, bpo, and system integration.
You work with Fortune 500 with both of your companies. Where do you see gaps in their approach to AI in terms of strategy, people or implementation? How can they get better?
Machine intelligence and AI-driven systems are some of the most transformational technological advances we’ve made as an industry, but in the current landscape it’s too difficult and risky for enterprise companies to get in on the action. They need solutions and platforms, done the right way the first time, and faster than it takes the to create in-house.
The cultural shift that needs to happen is that many assume that implementing AI is going to disrupt everything about the way they currently run their business. That’s simply not true. With the right strategy that takes into account measurable business goals, AI can start having major impact today.
How can we do better?
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