As you may have noticed, artificial intelligence is everywhere at the moment. However, some industries are going even more all-in than others. Software as a Service (SaaS) products are leaning into AI particularly heavily, focusing on deep learning features that help users understand as much data as possible and automate a range of tasks.
But what is driving this acceleration of deep learning in SaaS products? To put it simply, "AI hype" plays a huge part in fuelling the adoption of AI features. AI is everywhere, and everyone is talking about it, which leads to increased demand, increased competition, and everyone jumping on board in fear of being left behind.
SaaS companies are influenced by a range of different groups and circumstances when deciding where to invest their time and money.
AI tools are all about increasing efficiency and reducing costs, and as soon as people heard that's what they could achieve with AI, they wanted AI tools quickly. This creates a pressing need for SaaS companies to deliver quickly, as they risk losing customers to any competition that gets their AI features out quicker.
Since AI is such a fast-moving industry, customers also expect updates and improvements as more and more advanced models are released.
Shareholders of companies also push for AI adoption because they know it's making big money. They often prioritize short-term ROI, driving SaaS companies to take the shortest path possible (for example, simply integrating ChatGPT or another LLM into their product), rather than allowing the time for a more bespoke feature to be developed.
Risk can come in all sorts of forms for businesses, and AI features, as popular as they are, are not risk-free. With entire industries adopting AI, it becomes risky to ignore and let the competition gain the upper hand. However, at the same time, no one knows where AI will go next or how long it will stay popular, so big, long-term investments and innovative feature plans are also risky.
Right now, the sweet spot for AI investment is in short-term, quick-to-develop features that involve outsourcing from AI companies.
Right now, due to all the factors listed above and finite resources, SaaS investments in deep learning are largely short-term. This makes their longevity uncertain, because short-term investments are easier to abandon; we could see SaaS companies drop their AI features if the hype around AI takes a hit.
Rushed features can also rack up tech debt pretty quickly, making it harder to refine and improve them on a long-term basis. To fix this, some companies may realize they need some long-term investment to set the product up for the future, but not everyone will go in this direction.
Other companies will run into problems with infrastructure. There are various different ways to power AI features, and some are more flexible than others. With a GPU cloud platform, for example, companies can scale easily and keep costs down with pay-as-you-go pricing. TensorWave gives users access to the latest GPU models, continuous upgrades, and the ability to switch between different GPU types. With this degree of flexibility, it's much easier to test out new product ideas, pull back features that aren't doing well, and give more compute to premium users on a monthly basis.
Some companies, however, may have invested in on-site hardware with a fixed output, discouraging them from scaling down when necessary and making it harder to scale up as well.
There's no doubt that AI hype is fuelling the adoption of deep learning in the SaaS industry, pressuring companies to integrate LLMs as quickly as possible and plan for an AI-enhanced future. However, companies with only surface-level integration powered by outsourced tech may well be left in the dust eventually. To keep this trend going, executives will have to switch to long-term investments for bespoke AI features sooner rather than later.
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