Who Has The Agentic AI Moat?—Anyone?
How Agentic AI Changes the Game for SaaS Vendors and How to Compete
Conference season is over for the first half of the year. Every software company (large or small) is working on Agentic AI or has made announcements in that department. As in previous hype cycles, marketing leads ahead of product by at least 6-9 months in many cases.
How can you tell: product pages talk about visions, white papers mix old and new concepts (digital transformation and agentic transformation), videos show stick figure animations vs real UI or at least Figma screens, value propositions are generic, statements and testimonials are directional (“…is exploring…”)… the list goes on.
In front of that backdrop, I’ve recently received an interesting question: » How will AI change competitive dynamics between vendors? « Reason enough to pick it up for this article, take out the crystal ball, and ponder who’s got a moat. Let’s start with the technology foundation.
A Commoditized Technology Foundation
Over the last 2.5 years, SaaS vendors have been adding Generative AI into their products. Recently, many have expanded into agentic capabilities, spanning Agentic AI-enhanced workflows and real agents that autonomously work based on a goal. At the end of the day, though, most vendors are using the same basic technology (LLMs by the same handful of vendors) that have been trained on the same or similar data. LLMs as a basic technology have quickly become commoditized. SaaS applications typically solve a particular, yet commoditized set of problems in a business.
Take recruiting, for example: posting job descriptions, screening resumes, selecting candidates, scheduling interviews, and sending out offer letters/ contracts. All of these tasks share a similar scope and flow, regardless of whether your company operates in manufacturing, retail, or healthcare. Now, true differentiation lies in the data that these apps use to generate results. But it’s the customer’s data, not the vendor’s.
In previous technology cycles, like Machine Learning, vendors would select different parts of a business process to apply this technology to, driven by customer demand, feedback, or additional revenue opportunities. Generative AI has quickly leveled the playing field for software players: e-commerce software now comes with AI features that write product descriptions, customer service applications allow users to pre-fill responses with AI, HR apps summarize candidate information for hiring managers or help them prepare for interviews. That’s why a vendor’s differentiation in a category won’t come from the best models built by the best talent alone anymore.
Not only is the technology commoditized, but the AI capabilities that vendors are adding are commoditized, too. Consider using AI to extract information from a resume to populate fields in the applicant tracking system or to rank resumes, helping recruiters create a shortlist of candidates based on how closely the content of a resume matches a job description. There’s only so much you can automate and only so many tasks to which you can add AI to improve a highly standardized recruiting process before you need to rethink it from the ground up to add real differentiation (think: startups), not add the n-th AI feature.
For the last few years, everyone has been cooking with water, and it's water from the same well. Well, now, everyone is also cooking the same soup. So, how to stand out in a crowded market?—Agents to the rescue!
The Big Agentic Rebranding
While the Generative AI hype was starting to plateau (or even decline) at the end of last year, Agentic AI emerged as a viable successor. What had been “generative” a few weeks earlier quickly became “agentic,” even if that was (or still is) a mid-term vision for many vendors. Category leaders in previous hype cycles are now rebranding their entire company as being “agentic.”
Naturally, there are three sides to satisfy:
Investors who want to see a return, ideally higher than the competition’s,
Customers who want to see their license or subscription fees converted into R&D investment and additional features/ benefits, and
Competitors who should recognize that the company is keeping them on their toes.
What makes this situation even more peculiar, though, is that the AI agents vendors are building will take over even more of the standardized, routine tasks, thereby driving even further commoditization. From a customer’s point of view, this maximizes the old management ideology of focusing on core competencies. Unless a process is a core differentiator of a business, it becomes a commodity, and AI agents will be available to deliver that commodity outcome. If the promised productivity potential plays out as advertised, there will be fewer users interacting with applications directly and in general.
A Race to the Finish Line (or Bottom?)
To see what effect commoditization has on software vendors, you don’t need to look far. AI labs are a prime example of this race to the finish line. One AI lab announces or releases a new model, and within a few days, another one follows suit, oftentimes even outperforming their competitor’s product. The software industry as a whole is quickly shifting from differentiation based on features to differentiation based on time-to-market.
But time-to-market alone is not the end, and it won’t be enough. It’s just the beginning. Proof points are next. Who is using the AI features or agents, and what are they achieving because of them? While incumbents can rely on their installed base, courting new customers and logos is as critical as ever. The thing is that companies being founded right now rely on agents to scale. Will these future (or frontier) firms still use commoditized software for commoditized processes?
So, which factors will influence competition between vendors then, if the hottest innovation is a commodity? I believe mid-term, we will see vendors resorting to tried and tested methods to build their competitive edge:
Differentiation in their core products (non-AI features)
Integration with other products in their portfolio
Open ecosystems to expand the own footprint by playing well with others
Brand and reputation building trust with customers and prospects
Jumping onto the next hype topic (e.g., quantum computing?)
But for the time being, I cannot wait to see the potential of AI and AI agents be turned into products and into practical results.
Conclusion
Artificial Intelligence (including generative and agentic AI) is quickly becoming commoditized. SaaS vendors embedding the same technology from a handful of vendors are doing so for already commoditized processes and tasks. While many vendors have been quick to rebrand their products or company as being ‘agentic,’ it will take at least 6-9 months for many vendors to actually ship robust, enterprise-grade agentic AI products.
Lately, differentiation in SaaS (and PaaS!) is mainly driven by time-to-market as seen by a constant flow of releases by AI labs (and their competition catching up within days or weeks). To drive lasting differentiation, vendors will need to look for additional aspects that enhance their competitive edge, such as non-AI features, integration, latching onto the next trend, etc.
As many readers of The AI MEMO are vendors or buyers of SaaS applications, I’m curious to hear what you think competing will look like going forward.
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