Software is starting to feel like something other than software.
Ever since the days of Lotus Notes and Windows 1.0, software has perennially wowed us with all the new things it could do. Now it feels like a shift is happening. Software evolution today is less about new functionality and more about how that functionality is performed.
You already know what’s driving this: agentic AI.
As agentic AI becomes more prevalent, we’ll be less focused on handling tasks ourselves and more focused on setting goals and overseeing the work. It’s a lot like moving from an engine room operator to the captain of a ship. But that’s not what matters most.
What really matters is how agentic AI will change the competitive dynamics of the software industry itself.
Why? Because agentic AI requires a fundamentally different approach to how software is structured. This has huge ramifications. Power dynamics will shift, economies of scale will grow in importance, barriers to entry will rise, and the lines between software categories will start to blur. There’s a lot to unpack.
That’s why I’m devoting my next newsletter series to this topic.
I don’t have all the answers, but I can give you a better understanding of what’s happening now, so you can be better equipped to make the right choices for this next chapter.
Today’s goal?
I simply want to share why the shift to agentic AI is such an important factor in the industry’s dynamics. Understand this, and the changing landscape will become much clearer.
Agentic AI is Just Like Football
The easiest way I can explain agentic AI is with football.
In the NFL, a football team can’t win simply by having talented players. It needs to run the right plays at the right time. When a play is called, it means every player knows what his teammates are going to do – even before they start moving. It’s this coordination that tells the offensive line to create a hole in the defense, that tells the quarterback when to make the handoff to which running back, and when and where that running back should run.
For a team to run plays well, it needs a unified understanding of the plays themselves, the players and their respective roles, and the goal at hand. They also need visibility into what’s happening on the field so they can make real-time adjustments. (Engineers will undoubtedly find plenty of flaws in my analogy, but for our purposes, it’s close enough.)
AI Agents Need a Unified Platform
For AI agents to execute a business process, they need a shared understanding of the goal, the agent’s role, how the process itself works, and what else is happening in the business. Now, you don’t need to be a technical person to imagine what this might mean for how software needs to be structured.
Without unified context and data, AI agents cannot take intelligent, correct action on your behalf. Here’s a more technical overview:
Agents operate in parallel, and they need the same understanding of customers, products, events — everything. Otherwise, you get contradictory decisions that only show up after damage is done. A unified, identity-resolved layer [is] what keeps agents grounded and lets them collaborate instead of stepping on each other. Without that shared memory, agents “learn” different realities, and your system becomes incoherent fast. It’s a different mindset from traditional software design, closer to designing ecosystems than applications. (Source.)
Companies like Salesforce already understand this. And they are acting accordingly. Here’s what their EVP and Chief Scientist, AI Research, had to say:
...today’s AI leaders will be those who focus on deploying digital labor within a deeply unified platform that connects AI to real-time data, logic, and workflows. For a deeply unified platform to truly scale, it must integrate data, trust, and agentic AI into cohesive solutions that are context-aware and scalable. Instead of simply generating insights, these models become seamlessly embedded into business workflows, driving maximum impact and efficiency. (Source.)
What may not be obvious is that traditional SaaS simply wasn’t designed with an agentic AI architecture in mind. Many legacy software companies, especially those that have only recently started to take action, are at risk of getting left completely behind.
Agentic AI is Creating an Inflection Point in the Software Industry
Why does this matter from a strategy standpoint?
Because agentic software requires a deeper and broader connection to a customer’s data, software platforms have a distinct advantage over niche players. And from a customer’s perspective, assembling a “best of breed” collection of point solutions may no longer be a viable option.
This will have second-order consequences, too.
Buyers may face greater switching costs, while software providers may compete more heavily for that first sale. Cost of customer acquisition may go up, but so too may lifetime value. Niche players, and especially those “middle ground” software providers that are neither point solutions nor platforms, may have less room to maneuver.
What may not be obvious is that traditional SaaS simply wasn’t designed with an agentic AI architecture in mind. Many legacy software companies, especially those that have only recently started to take action, are at risk of getting left behind.
This is just a preview of what we’ll cover.
But for now, remember this: agentic AI isn’t just introducing a new set of features and functions; it’s creating a category-level inflection point in the industry. Old software categories will collapse. The boundaries of existing software categories will be pushed. And new categories will surface. Are you ready?
In the next issue, we’ll unpack why this consolidation will happen, and what it means for you. See you then.
As the founder of Flag & Frontier, John Rougeux partners with executive teams to align on their strategic narrative, build belief in the market, and win the next chapter of their business. You can chat with John here or connect with him on LinkedIn.
