Investor Deck is a six-article series presented by Sage, offering tips for SaaS startups from Canadian VCs. You can read the previous installment here.
For the past 18 months, artificial intelligence has been the drumbeat of the tech world, and it’s triggered an identity crisis for many SaaS startups.
As investors have redirected their coffers toward AI opportunities, many startups have decided they need to become AI companies.
“I think there’s this perception, sometimes real and sometimes exaggerated, that either because of a talent gap or a knowledge gap, AI remains inaccessible, and I think in general, that’s not true.”
Scott Loong, Panache Ventures
But Scott Loong, partner at Panache Ventures, said this pivot is often unnecessary and unsuccessful. He spends much of his time figuring out if companies are AI-native or AI-enabled, and said it’s a distinction more founders need to consider.
“No one needs to be convinced that these products are powerful,” he said. “The motivation to adopt is extremely high.”
But amid the sudden scramble by companies to pivot to AI, Loong believes SaaS leaders need to think about what they can plausibly do with the resources on hand.
“What a lot of startups are wondering about now is what they have access to, given their existing team of engineers,” he said.
Loong, who knows this area well—having founded and exited his own AI and insurtech startup—believes that startups who don’t offer AI as a core product shouldn’t try to reinvent the wheel to stay competitive.
“I think there’s this perception, sometimes real and sometimes exaggerated, that either because of a talent gap or a knowledge gap, AI remains inaccessible, and I think in general, that’s not true,” Loong added.
Building and running large language models are incredibly expensive endeavours, requiring specialized chips, large amounts of computing power, and highly skilled engineers. While creating a tailor-made AI solution from scratch might seem to offer a competitive advantage, Loong believes that for most startups, it’s simply not necessary.
For most seed to Series A startups, off-the-shelf AI tools are sufficient to achieve their goals, and Loong said purchasing or licensing these solutions can be more practical and cost-effective than building them from the ground up.
“Take your typical B2B SaaS company—they’re not going to have either the resources or the need for very, very sophisticated in-house AI knowledge,” he said. “In those situations, they should be looking for the low-hanging fruit in their operations that are currently being done by a group of individuals in a repetitive drudgery sort of way.”
Eric Sleeth, Senior Product Marketing Manager at Sage, said the rise of AI has created a hype similar to that of the Cloud ten years ago, and agrees with Loong that leaders should focus more on the business problems that AI can solve.
“AI is a resource multiplier.”
Eric Sleeth, Sage
“Startups thrive on innovation and efficiency, and AI catalyzes both,” Sleeth said. “AI is a resource multiplier; enhancing productivity and reducing operational cost, allowing startups to scale rapidly without proportional increases in workforce.”
Accounting and finance teams, for example, often spend countless hours on tasks like data entry, budgeting, reconciliation, generating reports, and analyzing data to make future projections and business decisions. These are all functions that can be easily automated by AI, yet according to a 2023 report, more than 60 percent of finance executives aren’t yet using it.
Sleeth noted that finance teams are particularly bogged down by a rigid finance system and lengthy processes, which make them ripe for AI transformation. “A SaaS startup’s superpower is the ability to be nimble and innovate,” he said, adding that AI can enable finance teams to operate more like startups.
Sage designed its AI-powered Copilot solution to help finance leaders, accountants, small businesses, and HR leaders automate the drudgery tasks and free up time of leaders to focus on improving the business.
The solution also uses adaptive learning algorithms to analyze each business processes and decision-making patterns. It’s also built with natural language processing capabilities, making it ideal for SaaS startups without in-house technical expertise.
Sleeth, for his part, sees finance teams “begging” to do more value-added work, rather than being overwhelmed with manual, time-consuming tasks.
“AI can free up human resources, and this is just scratching the surface,” Sleeth added. “We will see more advanced finance teams start to use AI to enhance human capabilities like monitoring compliance, anomaly detection, and providing deeper insights into financial performance, customer behavior, and market trends.”
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Feature image provided by Scott Loong.
