The most obvious factor to consider is the credibility of the product source and the source risk tolerance of the buyer. In general operators tend to keep infrastructure longer than enterprises, so they have to worry more about whether their vendor will stay in business and keep supporting their stuff. Of the 88 operators who have commented on this topic, all say that they require a financial/business-stable source. Of the 411 enterprises who offered comments, only 69 say that, and 104 say that it’s a “secondary” factor for them.

The “why” of this is important, though. Operators say that one of the goals of open technology is to multiply the number of sources, where enterprises presume that their source for open technology will be a vendor they’re already familiar with. Thus, enterprises’ presumption is that there will be no new source for open technology and operators think developing one is a major goal for openness. They measure open offerings to conform to that.

Even that begs a “why?” and here again we find a difference in operator/enterprise view. Operators want new sources because they have believed for years that it’s the only way for them to reduce capex, and every operator says they need to manage capex. In contrast, only a bit more than a third of enterprises even mention that, and so while enterprises may also believe that vendors overcharge them, they’re comfortable with competition between their vendors, or with the notion of beating them up over pricing.

The final “why?” comes in here. Enterprises are in general looking for an open technology as a point product. A server, platform software, whatever. Operators are looking for an open solution, usually to a new standard or regulatory requirement, that will mandate multiple product elements. If all these elements come from a pool of sources, there’s a massive integration requirement that operators simply don’t want to deal with. Enterprises rarely even mention this issue, again perhaps because they presume that their current vendors will be the source of open technology. Think Red Hat.

Another factor in the openness challenge that some operators point out is that a solution that doesn’t require integration, or that has less integration risk, is necessarily based on some widely recognized model, like a standard. Operators have generally faced a standards-driven upgrade requirement every five to ten years, and some (as I indicated in yesterday’s blog) have begun to take an interest in long-cycle planning for infrastructure changes even absent a driving standard. In contrast, very few enterprises talk about any preemptive modernization of their IT systems. The need for that has been largely erased by decades of stable client/server and virtualization thinking. The architecture of applications these days still has questions regarding the hybrid use of the cloud, but the framework is evolving in maturity even there.

There are also some supply-side considerations to think about here. For several decades, almost all network operator infrastructure change has been driven by mobile standards, given that mobile services have for decades been the bright spot in profits and the focus of competition. This space has been dominated by a small number of vendors (Ericsson, Huawei, and Nokia, primarily, with Huawei being replaced in many areas due to government pressure). Enterprise IT, in contrast, has a dozen major vendors or more. The chances of a “new” vendor entering the enterprise IT space is far better than the chances of a new vendor entering the mobile infrastructure space. In most cases, Open RAN players not one of the big vendors are partial-solution players, getting buyers back to the integration problem.

The most significant new factor in the picture differs for the enterprise and operator communities, too. For enterprises, its the cloud and AI, perhaps more generally the role of as-a-service versus in-house. For operators, it’s the determination to push 6G standardization as a software-only strategy, which would tend to make it easier for new players to enter the space.

What operators are asking for is an infrastructure model that, in the end, is more like that of the enterprise. Give us a stable, long-term, equipment model and then augment it with the necessary feature advances using software and perhaps the introduction of feature-as-a-service. In all, it could create a revolution in operator infrastructure, which is why perhaps the whole Open RAN or open-model 5G should have focused on that goal from the first. Which is also why 6G needs to follow operator demands here. Which it may, or may not, and we can see some hints of why that is by looking at Open RAN and 5G.

One contaminating issue was that the 3GPP 5G specifications didn’t really think about creating a universal hardware framework, one that could even support 5G much less endure beyond 5G. There was an unhealthy mix of trying to preserve the general 4G-LTE model and trying to converge with wireline infrastructure. We all know the old saw about serving two masters, and making it three might well have stalled 5G for years more.

That was the second contaminating issue. The whole 5G standardization process was simply too lengthy, and that’s typical of these international standards initiatives. Another old saw is “The IQ of any group of people is equal to the IQ of the dumbest, divided by the number of people.” Consensus is not only time-consuming, but it’s an enemy of innovation.

The third issue was Wall Street and the media. The Street values stocks by quarterly earnings, and something that’s aiming at 10-year relevance is hardly going to come to fruition in three months. Something whose value is boring financial credibility isn’t going to generate any clicks on stories either, and those clicks not only help ad-sponsored media but also the stock price of vendors and operators.

All of these issues will impact 6G evolution as much as they did 5G, and you also have to wonder whether the vendors, who tend to dominate all standards processes by the simple mechanism of staffing them with their own people, would accede to a standard whose goal was to reduce operator spending on equipment. That’s something we should watch as 6G evolves, but we probably won’t know the final picture on 6G until around 2029.

There are two components to operator cost; capex and opex. Capex has two basic sub-components—sustaining budget and project-based new spending. Keeping current networks running is the first and things like 5G upgrades examples of the second. Opex has many elements, but most of the attention goes to what I’ve called “process opex”, which is the cost of the processes that sustain proper operation of the network and operators’ overall business, which is the OSS/BSS/NMS stuff.

Operators tell me that, historically, they’ve handled the financial management of their businesses in much the same way that enterprises do, meaning that they will assign a capex and opex budget based on current services, and any increases will have to meet company ROI targets. The only differences operators contend with is the fact that some operator technology has a longer (sometimes much longer) period of expected useful life. In most cases, operators report that new projects that could justify increases in capex or opex are projects designed to raise revenues, meet regulatory requirements, accommodate changes in standards, etc. Here, there’s a difference with enterprises as well; most new enterprise projects approved target an overall reduction in costs somewhere; slightly less than a quarter of enterprise projects are justified by higher revenue levels.

If you have long-lived infrastructure, the general expectation would be that you have a correspondingly long planning cycle. That used to be the case for operators, and some projects (like accommodating a new wireless standard like 5G) will still take a long view—ten years or so, in contrast to a three-year period typical for enterprise planning. It’s relatively rare, say operators, to be looking at long-term network trends or technology trends to “optimize” networks; this sort of thing would more likely accompany another driver, again like 5G. In between these drivers, the presumption is that the framework of the last major project is to be sustained.

Today, some operators are questioning that. Of the 88 operators I chat with, 28 say that they believe that there should be a review of longer-term network evolution done at least every two years, and that this review should aim to identify cost-saving projects that likely involve some reshaping of the network/business framework, before the next major-project initiative resets thinking. Their reason is simple; their market is set by short-cycle planning of other organizations, and they need to regularly refresh their framework vision to correspond with these OTT trends.

What sort of things are those 28 operators looking at, or proposing to look at? The comment that comes up most often is simplification, followed by open. The technologies most often mentioned are optical and white box. DriveNets, whose cluster-modeled core network devices were introduced in an AT&T win and who recently won a deal with Comcast, shows up in spontaneous comments from operators for the first time, as an example of an open approach. The original core-centric mission is perhaps less a focus than something a bit broader.

DriveNets builds up network nodes from a cluster of white boxes running advanced Broadcom chips and custom software. One of the operators in my group of 28 said they thought the ideal network model would be an all-optical core, DriveNets nodes as edge-of-core, and then the same white boxes distributed outward, connected with the fastest possible fiber and, in the inner layer of the access network at least, multi-homed to the edge-of-core elements. This would give them a common pool of spare parts and a lot of capacity to play with.

The approach obviously simplifies, both by reducing the number and variety of boxes, but also reducing the features needed for capacity management. We’ve had many features and protocols that aimed at handling overloads and failures, and the more features you have in a device the more things can go wrong in setting them up. As user connection speeds rise, fiber becomes essential further out toward the edge, and operators are starting to realize that this can be leveraged to boost capacity to the point where congestion is less likely to happen at all, making all those management features redundant.

The question is how you evolve to this sort of model, whatever vendor you pick. Vendors, operators say, tend not to advocate something like this because they fear that the selling cycle would be too long and the risk of competition too high. Even the operators who mentioned a specific vendor in their comments suggested that vendor was not pushing an extreme do-over. Interestingly, the operators didn’t suggest that they wanted one immediately, only that they wanted to articulate a long-term model that they could then evolve toward.

This, to me, is interesting because it suggests that the operators trying hardest to do the right thing in the long term are still being hampered by short-term thinking, but on the part of their vendors more than their own management. Is this a problem with vendor sales management, the sales force itself, or higher company management? It’s hard to say from the operator comments, but I’d say that the comments suggest that it may be all of the above. Setting, and more importantly emphasizing, quarterly quota goals tends to focus everyone on their feet. A sales force will sell what, and how, they’re commissioned to sell. What I’m seeing, or at least seem to be seeing, is that setting quarterly goals to meet makes a sales force reluctant to look at longer-term evolutionary goals their buyers might be trying to address.

One particularly interesting point here is the 28 operators’ reaction to MWC. All of them, using one term or another, characterized the focus of the show as being either too “tactical”, meaning vendors treated it as a product bazaar, or too “theoretical” meaning it talked about things like “Open RAN” or “AI” without linking the terms to any specific evolution of infrastructure. “You can’t buy a standard,” one operator noted.

It’s really difficult to assess the extent to which vendor short-term quota and financial reporting obsessions interfere with long-term operator infrastructure planning, but I think that the comments they offer suggest that the impact is significant. The question is whether anything can overcome that influence. One candidate that’s now emerging is the use of AI by online network publications to deliver insights gleaned from their history of coverage. I’ve tried one that’s in beta, and unfortunately all it delivered was 404 errors, so I can’t say whether there’s any useful insight in there to be gleaned, or if the mechanism is workable. I don’t think there is really any media drive to support long-cycle tech planning in any case, and it may be that such a drive is the only way we can help operators balance all the forces that complicate their businesses.

Tech spending by enterprises falls into two categories—maintenance and modernization, and benefit-driven new applications. The first category sustains technology that current business operations depend upon, and the second deploys new technology to address new ways of doing business, subject to meeting corporate requirements for return on investment. Over the last three decades, we’ve seen the second of these contributions decline, because new applications of technology that can meet ROI targets are harder and harder to find. As I’ve noted, the “low apples” in project opportunity have been picked. To get a big upsurge in tech spending, we need a big upsurge in projects, which means in benefits.

The difference between the low apples we’ve picked and the higher, unpicked, ones isn’t apple variety or even ladders, it’s desks. The workers we’ve empowered up to now are largely (60%) working at desks, at least regularly. They’re involved in planning, analysis, transaction processing, and other activities we’d normally associate with office work. The ones that have been missed (the other 40%) are out in the real world, doing real-world stuff. If we want to make them more productive, we have to somehow give IT systems a window into that real world, and in real time.

We’ve used industrial/mechanical tools to augment or replace humans before, and according to my expert the places where this has worked are places where the “job” includes “tasks” that are amenable to simple autonomous treatment. You can replace a bunch of shoveling people with a backhoe, move goods on conveyors or assembly lines. We’ve largely done those things at this point, and the 40% are doing things that remain, jobs that require more judgment and insight than a simple system can provide. So we need something that can do at least a bit more.

What my AI expert says is that the thing we need to do that isn’t artificial general intelligence (AGI, like “Hal” in the movie 2001) but what’s coming to be called a “world model”. There are two forms of world model emerging. One is a pure virtual world, something like an AI generated alternate reality, and the other is a visualization of digital twin technology, meaning that it’s a virtual world that’s synchronized at some level to the real world. Each of these has its mission in empowerment.

A world-model-as-digital-twin can model the way our missing 40% actually works, potentially offering guidance to make it more efficient, or even tying in automated movement of goods and tools to worker movements. It could warn them of impending problems, including safety risks. For public safety workers, it could tell them where to go in detail, whether they’re policing a street or fighting a fire.

The pure virtual world model might seem like defining a live version of Dungeons and Dragons, but it’s actually essential in training autonomous systems, even training its digital-twin companion applications. Autonomy relies on mimicking human vision, because vision is the most effective way to gather information about surrounding conditions. Feed a world-model vision of a situation, including a risky one, and you can test how the an autonomous system would respond to it.

The insight my AI expert brought me is recognizing that AI systems that operate off digital twins will have to be trained to work in the real world. Yes, you could create a set of digital-twin model inputs to drive a simulation of something, but how well would that translate to real-world behaviors. “You can train a digital twin of an assembly line by creating the right sequence of sensor inputs, but if you want something that operates autonomously in a real-world situation, it will surely depend on visual analysis, so you have to train it on visions.”

This creates a really interesting division in the strategies for empowering our missing 40%. In some cases, what they’re doing is linked to an automated process, like a worker on an assembly line. The context of the line itself sets the worker behavior expectations. In other cases, the worker is setting the context by how they work, and so it’s essential to be able to “see” the worker to understand how to help the worker.

It gets even more complicated if you look at the first case in a more detailed way. Yes, we could model the movement of a car, for example, down an assembly line, establish where it was, what was supposed to happen there, and so forth, with bunch of sensors. But should we do that? Should a digital twin of an assembly line be able to spot something like a vehicle coming loose from the line, spot an inconsistency in the appearance of the vehicle at a given point and the part being presented there for attachment? Can this be done without visual analysis? If not, then we should assume that both the cases benefit from visual analysis, meaning the ability to analyze a video of the work environment. If that’s true, then we’re saying that world models for both operations and training will be needed for almost any of our new empowerments.

How does this visual-centric approach deal with other sensor data that might be incorporated in a digital twin? How does it deal with the intrinsic context of an automated-mechanical system like an assembly line? My expert proposes that these are then integrated with a world model via policy statements. In addition to an AI model of the workplace virtual world, we should expect that some standard language would be used to introduce these policies. Training would generate policy recommendations, perhaps, that would be offered in this language, and if accepted on review would then help govern the worker/environment interactions.

What I’ve learned from the conversation I had is that while I’ve arguably been on the right track with regard to digital twins and worker empowerment, I’ve not dug down as far as I should have. For example, my expert makes a point that the best way to judge a system designed to empower a worker in real-world activity is to look at the limiting case, which is to presume that a possible goal is to introduce an autonomous device as a “helper” or even as a replacement for the worker. If you can define what such a device needs to do, you can convert that to a combination of automation steps and human actions in any mix that’s appropriate.

World models seem essential both for the empowerment of the 40% we’ve missed, and the support of a new level of autonomous processes that might, in the end, replace some of them. My own modeling, which is hardly conclusive, suggests that about 15% of that 40% could in fact be replaced with autonomous elements, meaning 6% of the workforce overall. This is a far cry from putting everyone out of business, I know, so if it’s true you can bet it won’t get much publicity. But we get to an optimum future by facing reality, not telling stories.

Broadband policy, like most of public policy, is complicated by the general truth that getting products and services costs money, and that money is easier for some to part with than for others. For decades, it’s been the dominant global policy to establish some form of “universal service”, wherein those who can’t afford the market price for communications are able to obtain it for something they can afford. In recent years in the US, the implementation of this policy has started to shift from imposing a charge on service to create a subsidy pool that then makes up the difference in price, to requiring that ISPs offer broadband packages below market rate to qualified customers. Needless to say, this move has generated pushback from operators who already, in some global markets, are asking for broad subsidies, even for market-priced services.

Those who (like me) are historically minded will realize that what’s happening here is a collision between the established concepts of public utilities and of public-stock corporations. Telecom used to be either a regulated monopoly (a public utility) or even a government depart (postal, telegraph, and telephone or PTT). Today, it’s almost always more like any other stock company, and in fact utilities in general are drifting away from the monopoly model. The question is whether broadband Internet access should be made available at an affordable price, below market price, and if so whether the mechanism to lower prices should be based on subsidies paid to the operators, by operators accepting a lower profit on some customers, or by some combination of the two.

What California is debating is whether operators should offer low-income customers Internet access below the current $30 per month rate. If we look at the report data, it’s focused not on unserved customers, but at low-income customers (those at or below 200% of the federally set poverty line) who are already getting broadband, either at the $30 per month 100/20 Mbps Affordable Connectivity Program rate or by paying market rates for a faster connection. Those groups represent 500,000 and 850,000 customers, respectively. Since the report indicates there are over 5.8 million low-income households, meaning that almost four and a half million households are unserved.

What the report concludes is that the reduction in revenue that would result from mandating a $15 per month service is likely less than one percent, and would be only 2% if all currently served eligible households, including those now electing market-rate plans, were to shift to the new $15 plan. The report goes on to say that many households say that any price at all would deter them from using broadband Internet, and proposes a phased introduction of subsidies from 2026 through 2034, that would eventually address all 5.8 million eligible households. The cost of these subsidies would depend on the mandated operator price of the low-income service tier. The report goes on to say that the benefits to the state of having a broader broadband participation rate could justify these subsidies.

Obviously, taxpayers and voters at any level set “public policy” and I’m not going to comment on what the right policy is, or even if there is one. The technology issue is, IMHO, to frame services, infrastructure, and businesses so that the promised benefits are achieved to the greatest extent, while mitigating any negative impacts as much as possible. Obviously, you can’t mandate profitable operation, and so operators must be able to earn enough to invest, or we’d face a necessary return to a public utility model and perhaps even to a future where broadband services would have to be made a part of the government, as they were in some countries in the past (remember the PTTs?).

To me, this starts with the recognition that universal broadband will require very efficient access infrastructure. Access represents a third of the capex and 42% of opex, according to what I get from operators. To deploy specialized infrastructure to serve low-income households makes it more costly, so as much common infrastructure as possible is essential. This is particularly true if it’s a goal to set relatively high levels of performance on even basic service plans; the 100/20 Mbps service level, for example, is hard to achieve over random telephone twisted-pair.

Both CATV infrastructure and FTTx can provide a framework for delivery of a fairly wide range of services. For example, you can hang an FWA node off a fiber that could also support FTTH, and since many low-income households are concentrated in urban areas where 5G cellular broadband would be efficient, it would be smart to encourage this sort of infrastructure versus trying to make-do with in-ground copper loops.

Opex here may be a larger issue. Operators tell me that, on the average, low-income households are more likely to require support. This is particularly true for senior citizen households, since in general seniors are less tech-savvy. Might it be prudent, as part of an affordable broadband plan, to mandate an AI support chatbot, or at least offer financial/tax encouragement to the use of one? Might a state want to set standards on support chatbots, or even provide a baseline solution that operators would supplement with their own service-specific data?

A similar level of planning is essential in ensuring that the value to the state/government entity created by universal broadband is maximized by planning services delivered over broadband to citizens. Things like broadband telemedicine, for example, are useless if they’re not offered, and if public health programs don’t cover them. All the services likely to be beneficial are also services involving a lot of personal data, and so a mechanism to secure them reliably is critical.

There’s an unfortunate tendency to try to offset real costs with hypothetical benefits. It’s just as dangerous to offset real costs with benefits that you don’t try to maximize, or even assure. I personally think that there really is a benefit to universal broadband, but I don’t think it’s going to fall into our laps. Governments, operators, and enterprises are all subject to economic reality in the end. Dealing with that at a technical level really isn’t difficult, but it is an explicit step that has to be worked for.

The way that projects get launched has evolved through three distinct phases since the dawn of IT. In the first phase, which lasted from the 1950s through the 1970s, tech projects were normally driven by analysis of current processes and the way they could be improved. The technology to do the improving was known and often even already in use, and so we could call this a “harnessing” phase. The second phase, which ran from roughly 1980 through 2000, saw project opportunities known to exist (often by having been analyzed in the first phase) exploited when a missing piece in the tech puzzle came along. We could call this the “completing the puzzle” phase. The third, and current, phase involves project opportunities that require a rethinking of the way the business is done, a rethinking profound enough that it hadn’t been done in past phases. Call this the “business analysis” phase. This phase evolution, we’ll see, is critical to where we are in tech evolution overall.

My personal experience as a software architect spans all these phases. In the first phase, for example, it was common for companies to let IT organizations pick targets of “automation” because they understood tech capabilities, and so could visualize how they could be applied by observing current operations. Tech capabilities were understood, and applied. In the second, when something new came along (like the IBM PC) the new capability could be evaluated in light of past assessments, and it was easy to see where something could be done. In both these phases, we were technology driven, and we could assume that understanding the technology could quickly lead to recognizing how to apply it.

Not so the current phase. The problem here is that when a technology comes along, say “cloud computing”, the application of the technology will require a re-examination of the entire business process and technology framework to get a handle on a project approach and frame a business case. This, to me, suggests that the organization of a project team needed to change. In the first phase, companies had separate “programmers” and “system analysts”, behavior that in the second phase started to erode into a single “programmer-analyst”. Have we seen a change to accommodate today’s situation?

Not according to enterprises. In fact, until 2024 (at the end of the year), I didn’t see any statistically significant number of enterprises thinking about reorganizing the way they did projects. Since then, a dozen or so enterprises have talked about what one called a “cloud team”, a special group of IT professionals whose job was to frame projects for implementation via hybrid cloud. These teams, led by an experienced software architect and staffed by people with specific cloud development training/experience, did the heavy lifting that their companies had come to realize was needed for successful use of the cloud.

What sets the cloud team concept apart is that it presumes that the project to be assessed will be disruptive both in technical and business terms. It starts by cataloging where the cloud is different, then where the “differences” are likely to matter most in a business sense. I think it’s a great idea, but keep in mind that cloud computing is almost 20 years old (AWS launched in 2006), and we didn’t come up with the cloud team concept until 2024. Even now, use of a formal cloud team is down in the single-digit-percentages in enterprises. Interestingly, all my cloud-provider contacts say that, to some extent at least, they use the concept themselves in feature planning. None say they promote it to buyers, but of course there may be some influence exercised by cloud provider people I don’t interact with.

Where I have heard of more widespread use of a “team” concept is in industrial IoT. In fact, most enterprises in the manufacturing vertical, and about half of those in transportation and utilities, say that they develop applications with a team very similar to the cloud teams, but with the important addition of some number of operations professionals from the line organizations involved. I’ve worked myself on several of these teams in IoT applications in transportation and facilities management, but I’ve not personally seen them used in more general business applications.

Why hasn’t this concept spread more quickly, more broadly? Here I do have some personal experience. The user company I worked for the longest was, when I was first hired, letting centralized IT manage development and operations of data centers and networks. About 18 months in, they divided IT development up by major line of business, because line departments found the central group unresponsive. They went back to centralized when the divided IT proved inefficient, and ended up going back and forth several times. The point, I think, was that you have to view a “team” as a collection of people for a task, not as a permanent organizational element, and since both the line and IT sides of companies are organizational in structure, there’s a tendency to weaken teams in favor of organizational separation of tasks. Enterprise comments over the last several years, including comments from those with cloud teams, support this view.

AI seems to be a technology in desperate need of a team, and so is the empowerment of those missed 40% of workers I’ve blogged about many times. In one case, we have a specific technology in mind, and need optimal applications. In the other, we have a target mission, but not a specific technology. I wonder whether the role of “enterprise architect”, but strengthened, and in some cases offered co-leadership with the role of software architect. It got started at the right point, in the early 2000s when the concept of workflows, service-oriented architecture (SOA), and enterprise service bus workflows came along. Enterprises say it didn’t get, and doesn’t have, broad traction, and that where it exists the mission the role plays is inconsistent. It seems to me that enterprise architects should be the designers of business processes, and they should work with software architects to manage the automated side. All this would add up to what a modern project should look like.

Most enterprises can create a team to do a project, but not to consider one or to assess potential opportunities. Logically such a team should be under the CIO, and all the cloud teams I found were in fact done that way, but keep in mind that a cloud team has perhaps a more technical-assessment role, and those in place tend to look at projects already being done inefficiently. For my enterprise readers, I’d love to hear your thoughts and experiences on this topic.

I think that the teams concept is critical. There is no single formula for tech project success, because we’re not back-filling technology into recognized opportunity areas. With things like AI, we’re not just shooting at a moving target of opportunity, we’re shooting from a moving technology vehicle. Both, arguably, are moving randomly. It’s going to take a new level of cooperation.

What happens if we don’t get a team approach, or something else, that changes our magic ratio in a favorable direction? That would favor two things, incumbency and industry consolidation. Absent funding to justify major displacements, budgets tend to simply replace aging gear in kind, which favors whoever provides that gear. The fact that requirements aren’t changing because nothing is changing them makes feature differentiation more difficult, which means price differentiation and commoditization is likely. That situation in turn drives consolidation of vendors for efficiency.

The industry would be more exciting if we could raise our ratio. Jobs would be more interesting, stock prices would likely be higher, and in all good things could be expected. But it might take time, and that introduces a qualifier into our “could be better”, the qualifier being “in the long term”. Right now, long-term thinking means to the end of the current fiscal year. Is that long enough for this kind of progress to be made? Probably not, but we’ll see.

The biggest question vendors, and the tech markets, face today is probably one that’s almost never asked explicitly. That question is “How is buyer decision behavior changing in the next year or so?” If we expect the future of any technology to be different from the present, there has to be a pending change in decision outcomes. The model says that there’s a chain of truths that have been operative for decades, and it’s that chain we have to pull to get to some answers.

One thing clear from decades of history is that technology change is brought about by spending change. If you look at the residual value of a given class of technology (network gear, servers, etc.), you find that the pace of change we’ll actually see in a year depends on the ratio between the budget available and that residual value. When that ratio is large, enterprises entertain changes in technology direction, changes in vendor commitments, and so forth. When it’s small, they tend to stay the course.

Let me offer some specifics here. Over the two years of Andover Intel’s information-gathering, we’ve had a small ratio in play. We have that as 2025 opens, for three reasons. First, the number of new projects whose business case opens new budget contributions is at a historic low. Of the enterprises who offered comment here, the average contribution of new projects to IT and network spending is less than 10%, when over the last 30-plus years it’s averaged 34%. For fifteen years, it ran just around 50%, with four years when it went as high as 65%. Second, the asset base has grown over time, making it harder to displace stuff not yet fully depreciated, and finally the years and years of tech investment has leveled the peaks and valleys of asset depreciation, so there’s no single point where “modernization” is facilitated by having a lot of stuff aging out at once.

The result of this low-ratio condition is that any real opportunity to gain market share is generated by specialized places in the IT picture where new projects (that less-than-10% contribution) impact on a narrow front. Think AI and network equipment, for example. IT deployment in house depends largely on creation of “IT clusters” that generate more horizontal traffic, thus creating a specialized data center network that doesn’t have to conform to past technology choices or vendors. Why do network vendors love AI hype? Because it gives them a chance to gain market share, a chance that in 2025 isn’t offered by anything else.

Another interesting point is that the model says that new projects generating higher ratios and thus more transformational opportunity will take slightly over a year from approval to the delivery of a distinctively higher budget, and the budget/ratio good times will last (on the average) slightly less than 2 years, then trail back to baseline over another two to three years. That means that if we want 2026 to be a great year, a year of vendor opportunity, we’d need to identify something to drive it in 2025, and in early 2025 at that.

This truth creates another interesting model prediction. The value of startups is greatest during a period when a prospective driver has arisen, and projects that exploit it are being framed. This puts incumbent vendors under pressure to quickly prepare to exploit the gains in budget dollars, and thus facilitates an exit strategy for startups. Good exits rarely occur during periods when ratios are very low, absent that new driver, and also rarely occur during periods when the ratios are really high, largely because the budget largess has already driven users to make their product decisions. Only revenue will justify a good exit at that point.

A third point is that buyers, whether they’re enterprises or network operators or cloud providers, don’t actually believe the hype. Taking our current AI hype as an example, the tone of published material on AI was overwhelmingly positive in 2024 and is the same in 2025. It was like the cloud—everything is moving to it. Enterprise technologists never believed in the universal cloud, and they don’t believe in universal AI either. And here’s an interesting point; the cloud drove cloud spending, but didn’t drive a revolution in enterprise IT capex. It increased the expense budget only. Right now AI is doing the same thing.

Point four is that the greater the budget contribution, the greater the new-project benefit pool, the longer the positive impacts will sustain, but the longer the time it will take for the peak to be reached. What I’ve characterized as the “three cycles of IT” in the past (in the 1960s, the 1970s, and the 1980s, roughly) happened because of a transformational shift in technology application that had broad impact. Interestingly, each of these cycles got shorter and each contributed less to total spending than the one before. Since then, because of what my model identifies as the “ratio problem”, we’ve not had a major cycle. I submit that this is because it’s harder these days to transform.

Enterprises universally characterize the benefits of IT and networking as “improvements in productivity”. There’s only so much improvement possible in anything, and the most attractive targets are things that offer a lot of gain with minimal cost and risk. ROI, in short. We’ve done them, or at least the attractive and easy ones. What’s left is actually very large—the potential untapped productivity benefit pool is larger than what’s already been realized—but also very complex, and requires a larger “up front” investment. We’ve done what we’ve done by exploiting things familiar and in many cases already in place, but what remains will require breaking new ground.

The big take-away from all of this is that vendors who are looking for better profits had better starting to look for better business cases. Even stealing market share as a strategy will be difficult in a time when our ratios seem to be stuck at a low value; the safe choice and the easy choice is to stay the course. But that gets them to breaking new ground. The obvious questions are “where is the new ground” and “will it be broken”. I’ve pointed out in past blogs that roughly 40% of the workforce, the group not tied to desks, are obvious candidates. However, they’ve been that all along. The real question is whether we’ll uncover something to lower the barriers to reaching this non-empowered group. Since we’ve never been in ratio hell for as long as we have now, models can’t answer that one, but I did get some insights from enterprises that I’ll share in the next blog.

To get an important point out of the way, what company CEOs say at conferences is propaganda, and NVIDIA is under pressure on Wall Street right now. They need to look confident about AI opportunities because they need the Street to see their future market as growing. Thus, you have to assume that no public forum is going to miss a chance to convey an optimistic vision. The question here is whether AI is really under- or over-hyped, meaning whether the future holds more and more of it, or if it’s a flash in the media pan. Huang can’t make an opportunity real, only realize one that’s already waiting in the wings. Reality here, as usual, depends on real buyers.

I chat with 88 operators, and of that group I’ve had AI comments recently from 67. Every one indicated a belief that AI had value in their business, but here’s where we have to make an important point, which is that an application for any technology isn’t the same as a justification for it. You can use a brick to hammer in a nail, but hammering is a poor justification for buying bricks. To make it even more subtle, you can demonstrate that AI has “value” in creating a research report, but is the overall value enough for you to be willing to pay for AI, and pay enough that providing it is profitable?

The story says “Nvidia is working with 150 telcos around the world, including 90% of the top 50, and they are rapidly adopting AI across their business for internal productivity, customer experience and improving performance and performance-per-watt on the wireless network….” I agree; I’ve chatted with some of them. The question remains whether these applications of AI are really going to transform their business, or even end up justifying continued AI spending.

The top application of AI today, in every single vertical is the “assistant”, which uses AI as a personal productivity tool. Almost two-thirds of the people who use assistants admit to me that they’d never pay for what they get; either they’re using a free tool or their company is picking up the tab. A quarter the comments I get on the technology suggest that people actually hide assistant use because they don’t believe their management would approve it, and other surveys published recently suggest the same thing. Every operator told me they use assistants somewhere, but none said it was transformational.

The second-place enterprise AI application is the support chatbot, which 58 of the 67 operators said they used. This application, at least, gets a positive check from operators’ executive suite, so while 44 of the 58 said that support chatbots had proved more expensive than expected, had not generated as positive a customer response as had been hoped, or both, nobody said they were dropping the plans. But 38 CFOs, when asked what percent of bottom-line growth they could attribute to adoption of this application, could not respond with a number, and 3 said it was “minimal”.

Spectrum efficiency, bandwidth conservation, and network reliability all get positive marks, but again none of them were cited as offering any significant improvement to the bottom line. Even proposed opex reduction attributable to AI was an application CFOs were unwilling to say would generate a significant benefit. Add all the applications cited by NVIDIA together, and you get what a few CFOs said could be “a percent or two” of improvement.

But is this enough? It may well be enough to justify operator AI interest. The operator CFOs admit that they were approving AI projects whose ROI was far lower than their target. Two said that they had or would approve a project with a single-digit ROI. The reason is that cost reduction, if provable, is a major target for operators who have little faith in new revenue opportunities.

Enterprises are also eager to find ways of reducing the cost of network equipment and services, and in particular to reduce the number of operations errors that can impact QoE or security. CFOs of enterprises are likely (by a 2:1 margin) to accept a project ROI lower than their normal target for network AI that’s directed at these missions. A slightly lower percentage feel the same way about AI chatbots applied to their own pre- and post-sale support missions.

This leaves us at an important point. AI reality isn’t necessarily, or even likely to be, AI transformation or revolution. Yes, there are good things that operators, and enterprises, can do with it. Many will be done, but will they justify the level of investment already made, the almost-a-hundred-billion boost in cloud capex, for example? Much less, justify its increase in the future? Not so far. People won’t pay enough for what they’ve proved AI can do. But it can do more, can be transformational. We have to learn how to make that happen, and just a pretty song at a conference isn’t the answer.

A little about the 52 is in order here. Of that group, 41 offered comments on their role or background, and it was surprisingly diverse. The largest group were software architects, then developers with a DevOps role, then team leaders. There were no CIOs in the group, or development managers. Whatever the role, all had experience with AI tools, and 37 had some formal AI training.

Let me start by saying that none of the 52 said that AI had already transformed their business, but 47 said it had a transformational impact on some part of their operation or another. All 52 experts say that AI is potentially a business-transformational technology, meaning that it has the potential, but only 13 of 52 (both 13 experts and 13 enterprises represented) think that the public-LLM-generative-AI stuff that dominates the news is transformational. For that type of AI, there is in fact only one application that the 52 agree is valuable, and that’s the “support chatbot”. Almost every enterprise has pre- and post-sale support missions that have traditionally required human call centers, and about three-quarters have already (pre-AI) gone to some form of automated support. Most have also tried offshore call centers, but as of now that strategy is being questioned because of pushback from callers. Even with a human support agent, though, there’s still the challenge of getting the right answer delivered. The 13 enterprises who believe that generative AI chatbots are transformational are all companies that have large interactive support needs.

The other 39 experts from 35 enterprises tell me that to transform their business, AI would have to integrate into their business processes in multiple places. Just as no single traditional software application alone could transform a business, these 39 say no single AI application could do so. Getting AI into any single point in a business process, it turns out, is more complicated than it seems.

One obvious way to achieve AI introduction is to link AI to individual workers already involved in the target business process. The 39 experts agree that there are places where an AI “assistant” or “copilot” could have a positive impact. They believe this would not be the result of broad empowerment (integrating AI with document development or email for a large body of employees), but by introducing a more specialized AI model to a small number of high-value employees whose work product and effectiveness is highly valuable. In my terms, assistant technology’s value depends on the unit value of labor of the workers being assisted. They believe that some of these assistant missions might benefit from training with public data, but none think that it would require training on data with the breadth of the Internet. A high-value worker typically has a specialist job, and requires specialist assistance.

If you’re not going to rely totally on AI integration via an assistant-to-worker bond, then you have to introduce AI as a component of a business process flow in its own right, which would mean as a part of an application workflow. Enterprises have long recognized the need to pipeline applications (or their components) together in a workflow, and things like the enterprise service bus (ESB) and business process execution language (BPEL) were foundations of the original IBM Service-Oriented Architecture (SOA) designed to do this.

ESB/BPEL is only one example of the broader need to integrate business process elements together in a way that reflects their real-world context in the business. You can also bind them explicitly (each calls its successor and passes the needed data), you can use a publish-and-subscribe event processing approach, you can use a digital twin model, a service mesh…you get the picture.

So, apparently, do IBM and Oracle, the two vendors that enterprise AI experts recognize as playing a positive strategic role in getting AI organized into a business process. Both companies have told their strategic accounts that AI is essentially an application component to be integrated, and that the same rules and policies that guide the orchestration of application workflows have to work for AI. Thus, there may be different approaches taken depending on the specific nature of the business process, the way workflows are currently steered, and the role AI is to play.

Where AI is used for forecasting, modeling, planning, or other analytic missions, enterprises tend to think naturally about having AI integrated into existing analytics tools, which would likely mean one of the explicit or static mechanisms of binding it in. Where AI is to be used in processing business data, commercial transactions, it’s very likely it would simply look like an application component linked in via whatever mechanism was employed (an ESB, a broker, etc.).

Using AI in event-driven applications, which would include both control of process systems and IT or network operations, is an application that all 52 of our experts think would be useful and 50 think could be transformational. It’s also the area where there’s the most variability of viewpoint on the nature of integrating AI into the picture. Industrial process control, transportation systems, and similar real-world control missions seem to 39 of our experts as being specific applications where digital twins should provide for AI integration and context control. For AI and network operations support, the optimum solution (according to 46 of the experts) is more similar to the technique used to integrate AI into business planning. This likely reflects that IT and network operations is visualized as a planning and supervisory task, where industrial process control involves actually involving AI in the work itself. So far, none of the experts is suggesting that AI would actually take control of every aspect of network routing, for example.

While most (46 of 52) AI experts say that their organizations have a handle on the integration of AI with their business processes to the point where significant business improvements could be generated, they all agree that the issue needs to be addressed in a more effective way to impact the broad market. All 46 said that their CIO was integration-literate, but none believed that any other C-suite people in their company were even modestly AI literate, and 49 thought that was a problem because it made framing a project and getting approval more difficult.

Chatting with this group has demonstrated, to me at least, that there’s a big difference between the way that transformational AI is visualized. The majority of enterprise management/executive personnel think of it purely in chat-generative terms, but the people responsible for transformational projects think of it almost as a programming language. This difference, I think, isn’t just a matter of exposure, but surely it’s a result of a longer and more formal assessment of AI value. Are we all headed in that direction? Maybe, but one somewhat demoralizing point is that 51 of the 52 said they believed that vendors were more focused on the hosted-chat model of AI. Said one, “The amateurs are winning.” That would be bad for those who hope for optimal AI adoption any time soon.

Tech today lives in a climate that favors, even loves, bubbles. Stocks going up is always good news. Companies are rewarded for saying things that make their stock rise, and for saying things that limit its falling. Ad sponsorship of nearly all tech news (and most other news, too) means that we tend to see or hear what those who place ads think will help them. And let’s face it, we all click on stuff that’s interesting, and the more clicks something can generate, the better coverage it will get.

Back in the 1980s, I was doing semiannual market surveys as we transitioned networking from paid subscription to ad sponsored. At the time this started, we had about 14 thousand organized points of network equipment procurement in the US, and the reader base for primary network publications was about the same. A decade later, I found we had increased the reader base by a factor of ten, and had gained less than 500 new points of purchase. If you added up the budgets that people said they were responsible for on those reader service cards, the total exceeded the total capex budget, and approached the US GDP. The point? Readership was now dominated by network amateurs. What do you think online tech news has done?

AI has been around for decades. In the 1990s, I did consulting in the field, in fact, and at that point I don’t recall much interest in the topic, even in tech publications. You could read about “knowledge engineers” and “subject matter experts”, and anyone who wasn’t pretty well-grounded in the topic had no idea what any of that meant. Today, we’ve solved that problem. We have stories about how AI is going to steal your job or make mankind extinct, and millions read them. Consumability generates clicks, not insight. Search engine optimization (SEO) targets the masses, and the masses don’t good technology project decisions. Sadly, most of what non-technical C-suite people read falls into this mass-click category.

Generative AI is consumable, at two levels. First, we can read about how it’s advancing, enhancing, and eventually coming for us. Apocalypse Now, or maybe Apocalypse Z. Second, we can play with it online for free, and get subscriptions for AI-as-a-Service. The second of these lets AI bypass the usual capital ROI controls a CFO would apply, so AI can sort of sneak into businesses. Business case, ROI, who needs it? As a manager or executive, or maybe just a senior person, I can sign for up to a hundred bucks of AI service a month, and everyone is doing it so my decision will probably never be questioned.

We’ve created a hype and bubble industry that used to be tech.

Or, well, we’ve created a hype and bubble industry on top of tech. What’s most likely to be published is what search-engine optimization determines is most likely to be read, which favors mass readers over those whose information needs drive actual tech progress. We still need all that tech stuff, the stuff that the 14 thousand professionals read about, but how they get the things they need, how they learn about the aspects of AI that aren’t sensational enough to get noticed, is all pushed into the background. It’s the tortoise in the race, with the hype hare out in front by a mile.

And that, my friends, is where we are right now with AI. The sad truth is that AI is a revolution, something that could add billions to IT spending because it could generate more billions in benefits. How? You’ll probably never know until it’s already happened.

Last week’s stock market wasn’t really AI’s fault, but AI played a role. The stock market isn’t what you think either. You probably picture it as professional investors looking for value, but the great majority of tech stock trades aren’t made by investors at all, but by traders, and the majority of those are the hedge funds you hear about. Investors can make money when their stock goes up, but traders can make money when their stock goes down, too. What they can’t do much with is stocks going nowhere. When something like AI comes along, they love the upside it generates. When AI uncertainty raises its head, they love the downside too, and trading professionals have tools that actually let them break a market. When things start looking dicey, they trigger those tools and the market dumps. A couple days later, the market goes back. Every up, every down, wins. These swings are the easiest to see, and to cause, in market areas where hype has raised expectations unrealistically, like AI.

The real value of AI, a value that companies like IBM saw from the first and that I’ve blogged on for a year or more, is that it can generate functional elements that can be introduced into business processes just like we already introduce software elements. These functional elements are what enterprises have been telling me are “AI agents”. As this piece points out, though, the concept of the agent has already been contaminated. We’ve made it into, paraphrasing the article, generative AI with an instruction manual. It’s really a software component to enhance a business process.

That’s the reason why AI isn’t all hype. What’s hype is our conception of its value, a conception created by the way we learn about technology advances. The real value of AI will take time to develop, perhaps more time because focusing on the wrong thing invariably leads to delays in recognizing the right one. Read through the article, though, and you’ll find comments from Anthropic and Salesforce that seem to straddle hype the reality, at least. A year ago, we didn’t even see that much. When reality starts to dominate, we’ll see another AI wave, and while it probably won’t be as exciting as the current one, and may not even create the same Wall Street boom, it will surely deliver new winners.

How will enterprises get to the real AI value proposition? The answer lies in two factors. First, there are some vendors who enterprises believe are delivering strategic AI advice—IBM and Oracle are the leaders in terms of enterprise comments. These vendors, where they have influence, can help management frame AI projects optimally. Second, some enterprises are creating special AI teams, combining technical people with AI skills, technical people who understand current business process and workflows, and decision-makers. It’s a bit early to call these teams a success, but the early signs are positive. Their greatest benefit may be their ability to translate AI technology into business-consumable form, something that getting AI projects approved and making them successful surely demands.