Where will network changes be taken by AI? Where money takes it. Do AI agents boost profit by expanding sales? That’s always hard to prove, and currently enterprises say that most IT projects are justified by cost reductions. Then, where does the savings from AI agents come from? That’s a question that I’m eager to answer, and one Wall Street is also very interested in. How would enterprises answer it, versus the Street, versus me? AI deployment has costs, so to offset those it has to deliver benefits, which really means cutting costs somewhere else. Let’s look at how AI agents can lower costs overall, because only the sum of the missions that can do that will impact network technology and spending.
In order for any IT tool to reduce costs for enterprises, there has to be some specific cost or costs it reduces. In general, enterprises say there are two possible sources. The first is labor cost reduced by either cutting worker numbers or facilitating the replacement of higher-cost workers with lower-cost workers. The second is cutting IT costs in some way, an approach that’s initially been focused (in the media, at least) on software in general and SaaS in particular.
Enterprises largely believe that the only way to justify AI cost is by “enhancing productivity”, which means getting more from a given unit of labor. There are some studies that bear this out, too. The problem with labor cost reduction as a goal is that enterprises have not reported any significant job cuts associated with the use of hosted AI services, and that any such goal generates significant public policy risk, proportional to the scope of the impact but real at almost every level. However, enterprises have consistently reported business benefits derived from one of the three AI agent models (interactive, workflow, embedded) that they recognize. These benefits are sometimes linked to the ability to reduce additional staff, but (so far) rarely to the ability to cut staff. In fact, AI agent use so far seems more often justified by improved decision-making, which may be due in part to the fact that many applications are currently linked to business intelligence.
The one area of exception to this is in the customer support area, where many enterprises seek to reduce call center personnel through the use of AI-provided interactive chatbot services. Enterprises say that call centers employ anywhere from virtually none to as much as five percent of their workforces, so this can be a significant gain, and it’s surely the low-hanging fruit of the whole AI interactive agent game. In addition to the potential reduction in labor costs, application of AI to support roles could improve user satisfaction, since call center delays and offshoring-created language barriers are a continuous source of complaints.
Wall Street tends to love the notion that AI is going to disrupt the software space, creating winners and losers that hedge funds can exploit with “long” and “short” bets on their stocks. There is some limited indication that AI is impacting software purchase decisions; enterprises are trying to work through the question of what AI could/should do versus what they’ve traditionally done with software tools.
Enterprises have mixed views on whether AI saves software costs in the long run, largely because the applications of AI agents have a varied relationship with current or prospective software expense. There are four primary sources of cost; development costs, software purchase, SaaS usage expenses, and hosting/network infrastructure costs, and all of them get mixed enterprise reviews.
Enterprises generally say that while AI can be used in coding missions, they have not actually cut costs significantly through this application. Most say that AI can generate basic functions, simple microservices, with acceptable accuracy, it requires more software-architect and development review, which limits the net savings in human resources.
There’s similar skepticism on AI reductions to software purchase. Part of this is due to the fact that enterprises are currently most likely to view AI agents as a supplement to existing software or a way of doing something that they have not been able to do in a satisfactory way with traditional software tools. There’s also a widely held view that AI will generally require more hosting resources than traditional software, in which case the TCO of an AI solution might be higher even if less software were to be purchased.
SaaS, or any form of expensed software, and in particular software tools used for “citizen empowerment” are a different matter. Most enterprises believe that AI agents and even AI services (many of which, I’d note, are positioning themselves as “agents”, compounding the general terminology challenges of the space) will impact other “personal” tools delivered as SaaS, and over a third say that’s already happening for them, though so far the impacts are reported as small. The question, says a large majority of enterprises, is whether the SaaS services are themselves augmented with AI, which most agree is already happening. They expect SaaS applications not involving data governance requirements to become “AIaaS” applications. Interestingly, this is what most see as the “agent-as-a-service” target that the current AI giants could exploit. However, to exploit it they’d have to come in cheaper than traditional SaaS, and I’m not convinced that would be a profitable business model for the provider.
The impact of an AI move against traditional software most often runs afoul of this tension between hosting costs and data governance. Enterprises with actual AI experience say that AI agents are like any sort of software, meaning that where usage is highly erratic it’s not economical to self-host, and so if AI services are ruled out because of governance issues, AI may not be an option at all. Even where that’s not a factor, there are AI agent hosting issues to work through.
Most software projects are cost-justified on a narrow basis, and AI is no exception. The problem is that like software in general, totally distributed AI introduces inefficiencies in hosting. That means some sort of AI clustering, usually in the data center, is essential for economical AI deployment. But if AI agents are narrowly cost-justified, how do you deal with the need to aggregate AI hosting to achieve overall cost efficiency?
This seems to be the overall dilemma that CIO/CFO types are trying to deal with. On the one hand, AI agents can generate real benefits if they’re applied to missions that involve a company’s core business data. On the other hand, to serve these missions with cost efficiency, you need an AI resource pool and not a bunch of independently deployed and operated AI servers.
One obvious question raised by this is how to interpret the massive layoffs being reported, particularly with tech companies, and attributed to AI. What I hear from insiders in all these companies is that the layoffs have relatively little to do with AI, at least in the sense being reported. They say that their companies are under profit pressure from Wall Street, and tech revenue growth hasn’t kept pace with expectations except where it’s related to AI futures. The lack of revenue growth means that profit growth can only come from cutting costs, but if you have to increase AI spending, you need to cut elsewhere, whether AI is in any way facilitating this.
Time to analyze, I think. Based on what I hear from enterprises, it will be very, very, difficult to justify AI-as-a-service based on reduction in labor costs, except where call center labor is the target. However, it would be possible to justify AI replacement of SaaS services, but only if AI is cheaper, which makes it an unlikely growth driver for the AI space. Thus, I remain convinced that massive growth in AI services will require a driver we currently don’t have.
AI agents, particularly those that operate on company-private data, are another matter. I believe what enterprises tell me is true, which is that these applications can make a business case in multiple ways. I also believe that this potential can be enhanced by facilitating an AI-resource-pool model that enterprises can be confident in deploying. There is, then, an AI self-hosting opportunity for chip and server vendors to exploit, but it will be what every seller hates to see—an educational market. You’ll have to teach the right way of doing this, because it’s very unlikely that the media/analyst space will address it because of the lack of click potential. So, the wait for AI to boom in truth and not just in clicks, continues.
What all this means for network evolution is simple; AI isn’t yet a revolution, and so its impact on networks is likely to be expansion of the “horizontalization” trends already established by service-oriented software componentization. But in every evolution, there are leaders, so there will be early adopters of AI agents on a large scale who will face network changes early on. We’ll be watching what they find, and do.
