In conversation with… Jamil Dewsi
Jamil Dewsi is the Head of Growth at DarwinAI—the KW-based company that’s been turning heads, winning globally renowned reference customers, and earning plenty of press as an emerging leader in the fields of explainable artificial intelligence (XAI) and responsible AI.
These technologies help engineers, data scientists, government regulators, etc. understand how AI systems work. In addition to accelerating development timelines (which comes in very handy during a pandemic) and increasing performance density, DarwinAI’s GenSynth helps to spot errors and identify implicit bias within the dataset and outcomes—both of which are crucial to building trust in the algorithms and solutions that have increasing influence over our lives.
(shout out to anyone who’s read the very accessible Weapons of Math Destruction)
Jamil joined DarwinAI in 2019 after a few years in technology investment banking, so he brings an interesting background to his role as Head of Growth. I wanted to learn a bit more about what’s behind DarwinAI’s success so far, because it’s rare for such a young company (founded in 2017) to come out of the gates with such momentum
First, let me start by congratulating you and the whole DarwinAI team on your success so far. In the last year, especially, the company seems to have popped up everywhere!
Thanks, Lee. Credit goes to the co-founders and the rest of our team. They’re working tirelessly to solve seemingly unsolvable problems with AI.
We were glad to see The Economist publish a report on The business case for responsible AI. Responsible AI is just as important as the predictive capabilities of AI itself and should be seen as a source of competitive advantage. We’re starting to see more enterprises adopt this vision.
You’re the Head of Growth at a company that’s vying for leadership in an emerging—and potentially massive—market. What does your job entail, and has it changed even in the relatively short time you’ve been with DarwinAI?
I spend a lot of my time planning and executing our go-to-market strategy. This includes direct sales and global strategic partnerships.
When I joined DarwinAI, we didn’t have these GTM processes in place—most of our sales efforts were founder-driven—so I spent a lot of time learning about our target audience and how to best engage with them. As we grew the team, we created playbooks to make our GTM efforts more repeatable and scalable.
I spent a lot of time learning about our target audience and how to best engage with them. As we grew the team, we created playbooks to make our GTM efforts more repeatable and scalable. I think the best way to stay ahead of the competition is to learn fast, fail fast, and iterate rapidly.
You mentioned an important point—we operate in a massive market, which has welcomed a lot of competition. I think the best way to stay ahead of the competition is to learn fast, fail fast, and iterate rapidly. I also believe in making data-driven decisions and telling stories with data. We continuously collect data about the market, A/B test different GTM strategies, and gather customer feedback through interviews. This data tells us whether we are focusing on the right problems and whether we are solving problems better than the competition.
I’m a big fan of “just enough process” to keep things on track without being bogged down, especially when you need to move quickly. For me, that means having steps to follow, checklists so I don’t need to remember every little thing, and other guardrails that allow for repeatable successes but don’t prevent needed customization or improvisation. Is that similar to the playbooks that have allowed you to scale your go-to-market efforts? Could you provide an example?
Precisely. These playbooks make it easier to repeat a process with predictability of a desired outcome. In other words, it takes the thinking out of tactical execution.
Looking back, there are two other key benefits. First, playbooks help you clearly articulate the steps you’re taking to achieve a particular objective. Second, as you iterate your playbook, you can pinpoint exactly what causes either positive or negative outcomes. We can then use this information to improve our processes further.
One example is our playbook for engaging with customers. This includes everything from selecting the right channels, qualifying opportunities, and handling the logistics around contracts. Our processes have changed significantly over the last 12 months, but those were deliberate and calculated decisions based on our learnings from the market.
Our processes have changed significantly over the last 12 months, but those were deliberate and calculated decisions based on our learnings from the market.
And now circling back a little bit, you mentioned direct sales and strategic partnerships. DarwinAI has some very impressive references already. References can be hard to come by for start-ups, especially globally recognizable brands like BMW, Audi, and Honeywell, and technology leaders including Lockheed Martin, Intel, and Red Hat. First, congratulations on securing those references. Second, how did you secure them? And third, what advice can you give to start-ups looking to reach the same level of reference success?
Thank you, it’s always a team effort. Our brilliant R&D and product teams developed industry-recognized software. Our operations team created a seamless process for onboarding customers. Our professional services team works around the clock to ensure that customers are successful. Our growth and marketing teams are finding opportunities where we can provide differentiated value. We also have incredible partners in our ecosystem.
Lastly, we go into every engagement with a learning mindset—you need to listen to the customer if you want to deeply understand their problems and provide differentiated value. We view all our customers as partners, and it has been a privilege to work so collaboratively with them.
We are all still learning, but my advice would be to listen to the market, be empathetic with customers, and make decisions quickly.
Selling technical solutions can be challenging, especially when you have to appeal to different personas (e.g., buyers versus users, economic evaluators vs technical evaluators). How have you navigated this terrain, and what changes have you made along the way?
Everyone has unique requirements and consumes information differently. We have to adapt our process to fit the customer’s buying process. It also helps to create and align on a structured process, with a balance between discovery calls and presenting solutions.
We get a lot of common requests—information about our offering, value proposition, ROI, case studies are table stakes—so it helps to have succinct and distributable overviews available.
That said, we get a lot of common requests—information about our offering, value proposition, ROI, case studies are table stakes—so it helps to have succinct and distributable overviews available.
And finally, without giving too much away, what does the rest of 2021 and beyond hold for DarwinAI as the team continues to grow and the addressable market expands beyond innovators and early adopters?
I’ll just say that responsible AI is becoming more important to enterprises’ AI strategies.
For example, we’re seeing strong demand from global manufacturers to unlock new efficiencies with AI, but AI adoption is highly dependent on how much the workforce ultimately trusts AI solutions. We’re focused on solving the problem of “trust”.
We have a lot of announcements coming up about how we’re enabling organizations to leverage AI more responsibly, so stay tuned.
I look forward to seeing them! Thanks so much for your time Jamil, and all the best to you and the rest of the DarwinAI team—as algorithms and AI play an ever-increasing role in all of our lives, we’re counting on you to make sure they behave!
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