There is a strong temptation to think of this structuring challenge as an AI challenge, and I think that if we looked far into the future we might find that it truly is, but that ignores the present, which is at best risky and at worst a business-case disaster. We cannot possibly believe that AI technology will displace current software quickly; the business disruption and asset displacement impacts of such a move are insurmountable. We have to evolve to the future, and that means a new kind of thinking, about AI and about applications. We have to think in terms of processes and of greenpatches.

A business is, functionally speaking, a complex mesh of processes. Each of these processes exists in two domains—the organizational/human domain and the IT domain. If you work for an enterprise and read my blogs, you are part of the CIO’s organization, and there’s an accounting organization, sales, marketing, payroll and personnel, and so forth. These high-level organizational elements are super-processes, divided organizationally into smaller elements, like “accounting” is divided into “receivable” and “payable”.

What organizes these processes today is really large an organizational/human activity. IT is largely directed at facilitating human activity. However, we do have (even today), workflows at the IT level that cross process boundaries. Those who remember early software componentization will recall the “enterprise service bus” that aligned IT services with cross-business-process workflows. While both the technology and the terminology are ancient by today’s software standards, the fact is that this is how businesses work, and how IT works within businesses, today. We could say that a business process is the unit of company activity, and an IT service is the collective support for each business process.

How would this evolve to optimally utilize AI? Rather on speculating about a future when the over-mind of AI calls out its AI minions to perform atomic tasks, seeing all, knowing, and organizing all, let’s be realistic. The easiest path of evolution would be to address the IT services within the overall process structure, or perhaps more accurately, to look at the more atomic levels of the business process hierarchy as our early AI targets. The most productive early targets would be ones where we had a large incremental AI benefit to claim, and a relatively low disruption/displacement cost—which is what I’m calling a “greenpatch”.

A greenpatch is a variant on the old “greenfield” concept. In IT, a greenfield project is one that is new, not displacing any existing technology solution or solution element. Think of a greenpatch as a special case, a place where either a new IT service can be framed via AI, or where a current framework can be rebuilt using AI, with a compelling ROI. The goal is to stick AI elements in where they’d do the most net good, keeping as much of the surroundings in place as possible to reduce bleed-through impact. When you draw back your bow, the goal isn’t to hit the moon, but to avoid hitting your own foot. Put in AI terms, this means finding either atomic IT services or current human processes and considering their implementation as AI agents.

This is one reason I’m so interested in the NVIDIA Cosmos models I blogged about yesterday. Obviously the greenest of the greenpatches would be areas where existing IT integration was minimal, as would likely be the case with the roughly 40% of the workforce whose activity isn’t deskbound, but rather is out in the real world doing real things. Cosmos is designed to model humans doing stuff, and so could open this area up to AI enhancement.

A real-world AI greenpatch system would likely start by having a Cosmos-like entity analyze videos of the human activity to derive a physical-system model. That would then be used to plan optimizations and monitor for efficiency and safety. If the AI system required integration with either people or other IT systems, the required information could be introduced or output in an appropriate form—think an “adapter-API” that speaks AI on one side and IT/human on the other.

Deep learning or simple machine learning (ML) could be used to perform deskbound tasks as well, and in fact we can build an example from one to illustrate. Suppose we have a group of people working on the corporate tax filings. There may be software in play here, or it may be largely a human process. Either way, we already have some foundation models in AI aimed at doing taxes of various kinds, so we could plant such a model in this process to facilitate it. To interface, we’d look at what information feeds the current process, and develop an “adapter API” that would format those sources to match the model input/prompt requirements. So, perhaps, we have a federal-AI and state(s)-AI models.

If, hypothetically, the only taxes we had were handled by these two models, then organizationally speaking, we have a superior process (Tax-Handling) with only AI-model systems as subordinates. Getting AI to “supervise” AI isn’t a giant reach, so we could assume we’d use a new supervisory AI model to assist, or even be, Tax-Handler. At any level, the way the AI is trained has to match the activity being supported; world foundation models like Cosmos would be used for human-action activity, for example.

This is probably not the way most people think of AI being used, and even enterprises don’t spontaneously come up with it. What they do is implicitly or explicitly reject the notion of some huge monolithic AI controlling everything, and I’m not seeing these objections grounded in fear of “Hal” misbehavior of that AI entity as much as in the practical problems of both achieving AI capable of such a scope of control, and evolving to its use.

Slow and steady, in other words, wins the AI race too.

In AI, a foundation model is a deep learning (which is in turn a subset of machine learning) model that’s pre-trained on a specific set of data and is usually then applied by being further specialized by the user. Popular public generative AI is an example of a foundation model trained on a vast array of knowledge—like the Internet. So is AI applied to programming, or tax accounting, or business analytics. Cosmos is a what NVIDIA calls a “world foundation model” designed to generate representations (models, images, videos, sounds) of the real world. In a very real sense, the goal of Cosmos it to build a digital twin.

The greatest challenge in integrating technology with real-world activity is modeling the real world in a way that enables an application to analyze conditions and generate appropriate responses. Such a model is usually called a “digital twin”, a term that’s perhaps a bit misleading because it’s not normally a complete real-world replica but rather a replica of a real-world process subset. NVIDIA, for example, says that Cosmos was trained on twenty million hours of video of people doing things. Think of it in NVIDIA’s term; “physical AI”.

Cosmos is a model set or family, with three members. Nano is a low-latency, resource-optimized, model designed for at-the-edge deployment. Super is the baseline model for centralized operation, and Ultra is the high-fidelity optimized version that would, for example, be used in the creation of custom models. All are trained on real-world activities involving people doing things, including moving, driving, and working. The models can be used to analyze video information (real-time or stored), build real-world models, predict future states based on training data, and run simulations for optimization.

It’s hard to overstate the importance of foundation models in general, and of Cosmos in particular. I’ve blogged many times on the importance of creating new tech benefits by bringing tech into our real world, on the role digital twins necessarily play in that, and on the fact that we lack a singular digital-twin framework. Cosmos can address all of that. IBM, the strategy leader in enterprise AI according to enterprises, has this take.

The goal of Cosmos isn’t to convey something as much as to represent the physics of an activity. That representation can, of course, guide the creation of a video so that it shows a generated example of the activity, but it can also be used to analyze, model, and influence the activity. Cosmos can provide as close to a complete solution to real-world tech integration as I’m aware of today.

In the workplace, for example, Cosmos could analyze the process of loading trucks with boxes, and from this could recommend the optimum approach, direct workers to apply the approach, or control robots to apply it. It could, in theory, do that for any physical process it had been trained on, or could be trained on. It could also assess the outcome of changes made to the activity, which means that it could potentially forecast whether an in-progress action could/would result in an unsafe or undesirable outcome.

In the real world, Cosmos’ potential is even more revolutionary. Obviously, autonomous operation of vehicles would be facilitated, perhaps to the point where true full-time self-drive would be safer than human operation. Even pedestrian movement, from shopping to hiking and mountaineering, could be changed forever, providing that the inputs could be provided.

Cosmos illustrates that video is the ultimate means of machine-learning the real world, which means that full-on next-gen applications will rely on cameras and AI analysis of the feeds. I think it’s clear from the three-level Cosmos model structure that NVIDIA presumes that we’d have some sort of AI hierarchy, with simple and (relatively) inexpensive Nano models doing local analysis and looking for patterns, and deeper Super and Ultra models mediating that information to derive information and forecast outcomes. The structure overall could be reasonably easy to deploy in work-related, facility-limited, applications, but the cost for society-wide applications would be considerable, and would raise the dual questions of funding and social/political acceptance.

Enterprise comments on Cosmos so far suggest that manufacturing, transportation, warehousing, refining, and similar sectors could deploy it profitably without a major effort, providing that some vendor or integrator did the specific modeling work and that the in-house expertise needed (which, so far, they can’t assess) could be acquired. Public safety and military applications are currently seen as the most fruitful paths toward a society-wide system, because of the need to pre-position the assets over a wide area for effective exploitation.

There are examples offered by enterprises for Cosmos applications that integrate a limited real-world video analysis with other data to generate something useful. For example, body camera video might be analyzed in combination with mapping data to guide first responders to a specific site, or plot a route for safe withdrawal. Jobsite video, vehicular video, and other “point video” sources could also be enough for some applications, and it may be that these applications would become the proving grounds for broader use. However, enterprises aren’t familiar enough with Cosmos for their views here to be authoritative in the near term; experience will be required, and those who have expressed early interest didn’t think they’d be doing more than kicking Cosmos tires in 2025.

I expect that they’re right, too. The challenges here are significant, even with Cosmos. As I’ve already noted, enterprises think they’ll need time to develop AI experience, and Cosmos alone demonstrates that enterprise AI is still a moving target. What does seem clear is that Cosmos and world foundation models, combined with the notion of AI agents (I dislike how “agentic” is getting spread out to things it’s not really applicable to), can create a way of building applications that are somewhat insulated from the technical evolution of AI. There’s already a good article available on how agent-oriented AI will impact APIs, for example, showing software architects are thinking about the challenge.

It’s been my long-held view that public, generative, AI aimed at personal productivity in document or email generation is an ROI dead end. Cosmos-like agent AI is not. Did the big cloud types know this was in the works, and so justified their capex on AI with the popular hype Wall Street loves, rather than trying to explain Cosmos and its timing and risks? Or did they believe the hype? I expect that we’ll find out pretty quickly now that Cosmos is out.

What is reality? That’s one of those important-but-imponderable questions that comes up regularly, and in this case has been debated for at least a millennium. It’s hard to reach a conclusion from such a mass of debate, but the general view is that reality is defined by consensus. What is “real”, then, is what observers would agree is real. The sky is blue, the sky is “up”, gravity makes things fall “down”, the world is round…you get the picture.

One problem with this nice approach is that some topics can’t be assessed by a random set of observers. For example, you probably wouldn’t want to have an operation based on the opinion of a hundred or even a thousand randomly selected people. From this emerges what’s been called “specialized consensus”, which is consensus drawn from a group actually capable of assessing the topic.

We’ve depended on the specialized consensus model for generations, for medical and financial advice, for governance, and even for news, and for technology news. However, for decades at least we’ve also tried to validate the specialized consensus views, because it’s clear that specialized consensus could lead to distortion if the group that provides it is somehow contaminated. However, surveys of views of many (randomly selected or specialized) are themselves subject to accidental or intentional bias.

All of this may seem philosophical, but it has two distinct paths of impact that are uprooting decades of technology evolution, and one of which impacts our lives overall. The latter is the rise of social media, and the other the increased difficulty validating new technologies. Both these have their roots in the populization of information that the Internet created.

Social media creates multiple virtual communities that defy many traditional geographic and cultural barriers. It makes it possible to gather information from, and distribute information to, an enormous segment of the world population. It promotes/allows both “consensus” and “specialized consensus” to be achieved, and at the same time erodes the influence of other information conduits. You can see this in the way that advertising investment is shifting from traditional media to social media. This combination is remaking sales, marketing, and entertainment.

The financial shift in adspend favors distribution multiplication over production. Video content is the soul of entertainment, and historically it’s been ad-sponsored on TV or audience-paid in theaters. Cable TV introduced premium-channel models where consumers paid for content, and streaming has multiplied this option. Time-shifted DVR viewing has been largely displaced by streaming. All of this raises questions about exactly how much content can be produced with the model, and what level of quality will be available. The new content production industry is more diverse, and that divides available revenue among more players, reducing the per-item budget.

Older content, then, will become more valuable in two ways, both of which are already impacting the market. First, rights to reuse material already viewed will become more expensive, which will make companies with long-term rights or their own libraries of material more valuable. Second, “remakes” of older material will become more popular.

New content trends are also already visible. “Reality” shows will increase in number and importance because they’re cheaper to produce. Animated or AI-generated material will also be favored for any new material, for the same reason. This is what’s behind the recent Hollywood strike negotiations regarding AI; obviously no actors would want to be replaced by their own avatars, but the same is true for those involved in production and editing. There will be a great appetite for reducing production costs, because of the competition for revenue and eyeballs being generated…anew.

This will, of course, also generate pressure on technology. Everything in networking, like everywhere else, has to be paid for. Both operators and enterprises tell me they’d love to see some sort of transformational technology come along, but even today their focus is on transforming costs. Unless something changes, that focus can only become more intense, at least with regard to consumer Internet and related technologies.

Ah, the inevitable qualifier! It’s common to think of barriers to progress here as being resolved by technology, but what if the barriers are something else. Like…us.

What single word characterizes tech progress from the arguable dawn in the 1950s to present? I submit that the word would be closeness. Tech has become valuable to us by becoming close to us. We have integrated with it, linked it with our lives and our work, and by doing so shared ourselves with and through it. Every major step in tech evolution has been a step toward closeness, and I think any steps we take from here will be so, too. Which is a problem, because with every step we’ve taken, we’ve raised risks and created back-pressure.

In my very first job as a programmer, I worked with a dizzying number of executives at a big insurance company, and some long-term insurance and accounting professionals. One day, one of the latter burst into the big bullpen-like room junior programmers shared, shouting “Your computer is making mistakes and you’re covering up for it!” As it turned out, the mistake was made by a company printing checks, who’d printed a magnetic-ink and text number that didn’t match, but the point is that tech got the blame automatically. Today, we’d automatically blame AI, perhaps. Or maybe we’d say we were hacked.

Tech can mislead us, lie. Tech can expose us. Tech can enrich us. It’s done all of these things. We had our last big tech wave in enterprise technology over two decades ago, when in the past we never went more than five years between them. Did tech lose its edge, or did our fears overcome our enthusiasm? One more step equals one step too many?

None of the above, I think. The problem we have today is largely one that involves the tradeoff of benefit and risk. There’s a lot tech can do for us, but we have to surrender more of ourselves to get to the next step. For example, we know from a combination of TV fiction and real experience in some locations that surveillance cameras in public locations can not only solve crimes but prevent them…at the cost of recording parts of our lives we’re not accustomed to having reviewed, and the risk of having the images misused. A video on a street corner could record a crime, or even one that’s about to happen. It could warn of an unwary step off a curb or into a hole, too, but it could also record an illicit meeting, a gesture behind someone’s retreating back, an embarrassing wardrobe malfunction. A video on a job site could guide a worker or spy on the worker.

Some of the risks we routinely take today would have appalled people five or ten years ago. The risks we’d have to take to get more of that closeness appall some today. We’ll either need to reduce perceptions of risk, or present benefits that make those risks worthwhile, and that’s likely more a challenge now because to reap more IT benefits we’ll have to cross a barrier—the barrier of the real world. We’ve allowed ourselves to use tech and accepted the risks that the use might create. To get to the next level, we’ll have to let tech out, let it see and hear what we do even if we’re not using those capabilities at the moment.

The consensus definition of reality plays here, in two ways. First, closeness demands a sharing of real-world information with technology, and that sharing has to be objective. We can’t run real-world systems based on subjectivism, unless each of us has our own real world. We can already see, in privacy debates, that the risk/reward picture for new technology has a significant element of subjectivism in it, which means that unless we can agree on steps to take, the broad integration of tech with the real world may not be able to gain public policy support. The second point is that even real-world conditions can be hard to objectivize. Is someone running to cross at a crosswalk a risk that something like autonomous vehicles should respond to, even when a sudden stop might create a rear-end collision? Whether those issues can be addressed is, at this point, impossible to predict.

I’ve worked with Cisco and Cisco customers through a lot of my career as a network consultant/analyst, and one thing Cisco insiders always worked to make clear to me was that Cisco wasn’t interested in being a leader in technology advance, but a “fast follower”. That, said my contacts, was because a major incumbent in any tech area has too much to risk exercising game-changing behavior. Wait till others have taken the risks and found the right path, then jump in through acquisition or simple exercise of market power, when it’s clear that the technology shift will succeed. I think Cisco has succeeded through that approach for decades.

But what happens when nobody leads, or when none of the leads play out? What happens if what the market needs is a major transformation, something that only a giant could really present to buyers? What happens if whoever does lead manages to keep the lead? Those are the questions Cisco has to answer in 2025, because the technology focuses it says it believes in for the future are defensive, speculative. Does Cisco even believe in them, or are they just washing with the tech-soap-de-jure to make Wall Street happy?

Networking a la Cisco had it easy for the first couple of decades after its founding in 1984. Enterprises at the time tended to adopt network architectures presented by their computer vendors, for the simple reason that the goal of networks was to connect workers to applications. The problem was that the “minicomputer revolution” of the 1970s and 1980s was breaking the lock that giant IBM had on IT, and proprietary network technology was a potential barrier to exploiting the new IT freedom. Most of us forget that Cisco’s first product was a NIC for DEC minicomputers, and much of Cisco’s early success was due to the fact that IBM’s System Network Architecture (SNA) was an expensive and “closed” model of networking, not well-suited to the more open Internet-centric model that included customers, prospects, partners, and so forth in the information processing chain. We had applications, and we had information. What was needed then was populist connectivity.

What is needed now? That’s the question Cisco needs answered in 2025. Past history says they’ll wait for someone else to answer it, then sweep in with power and majesty to take over. Past history, though, may be too far in the past, and too broadly rooted.

The proverbial Dutch boy who stuck his finger in a hole in the dike was “eventful”, and the outcome of his behavior depended on faults developing in the dike system manifesting themselves in a local, boy-finger-sized, hole. One of my friends from Amsterdam commented “What would we have called the boy if there had been a widespread dike collapse instead? Drowned.” We have to look at Cisco’s situation in 2025 in this light. When does security or AI, the dual touchstones of Cisco magic in 2025, create an opportunity like the DEC NIC card?

Internet security stinks, and you get all the proof you need of that point when you look at tech news (“Major Incident: China-backed hackers breached US Treasury workstations”) or at enterprise budgets, where for many security spending is beating all the rest of network capex combined. IBM’s SNA was highly secure, but the model wasn’t economically viable for open network use. Could Cisco be looking for a populist security model?

Maybe, but not in the near term. Nothing Cisco has done or acquired is really more than another layer of protection added to a cake with too many layers already. Some fundamental change is needed. Cisco may see this, and may realize that if they’re to seize it when it emerges, they’ll need to be credible security incumbents. So they play the traditional security game in the traditional game. Or, they may not see any good solution to security in the long term, but see short-term dollar signs in those layers. Security may be stagnating, commoditizing, but the rest of Cisco’s portfolio is doing both, faster.

I don’t think that there’s a security silver bullet for networking, simply because of the populist roots of networking today. When IBM SNA was king, a big enterprise network had about 85 thousand users. Today, it might well have a thousand times that. The total number of connected users /devices was roughly 50 million, when today there are billions on the Internet. Cisco didn’t displace SNA, it carried it on IP, because even displacing 50 million SNA devices was totally unreasonable. Try displacing billions of devices. No radical change to the Internet as we know it is economically possible.

How about AI? Everyone knows that AI is a major driver—nay, the major driver—of network traffic growth, right? Well, everyone says that, but do they know it? Enterprises tell me that they don’t see any significant impact of AI on network traffic except within the AI cluster or during training. Still, what Cisco rival isn’t touting AI as a source of new network demand? Cisco would be crazy not to do the same, but crazy if they mistake shared delusion for truth. Do they see something else?

What is network traffic? It’s data. Networks move data, between sources, storage and process points, and users. Data is traffic, and the value of data is the driver of business cases, the “R” in “ROI”. If you can get more data, and more data value, you can justify more network spending to move it around. If AI could create data and data value, even indirectly, it could drive network opportunity upward.

Can human intelligence create data? Yes, but it’s important to recognize the difference between creating data and getting or delivering answers. In the movie “2001: A Space Odyssey”, HAL is consumed by the command to calculate, to the last digit, the value of Pi. Since that’s a transcendental number, delivering the result would literally take forever and an infinite number of digits, but that level of precision isn’t needed in the real world. What’s actually needed in terms of data to perform the calculation is minimal, and yet in the real world the truth is that complex answers, meaning complex problems, are typically framed by complex data sources. AI is valuable in data creation if it can create useful business answers from complex data, and if that data is “new” then both getting the data to AI and getting useful results to decision-makers could generate our traffic.

We actually have, in our history of IT, some insight into where that data probably comes from. In the first age of IT, businesses recorded the commercial results of their activity via computers. You sold or bought something, and punched the record of the transaction onto an 80-column card, which the computer would read and transfer to magnetic tape. Applications analyzed the tape to analyze business operations, but the view they had of “operation” was limited to the financial/commercial impacts. In the second age, we did online transaction processing, and that brought IT into the processes more directly. The PC and the Internet took that process integration further, and by that we mean that IT entered into the real world of what we did and how we did it, not just the world of the outcomes of what we did. The ultimate step in this is to have IT look out into the real world and capture what’s there.

IoT sensors of various kinds, and real-time video analysis, could already allow us to analyze movements, activities, risks, and opportunities in the real world. We’re starting, via things like digital twins, to model systems in a way that facilitates simulation, monitoring, and control. Bringing humans into this, ideally using video analysis, could allow applications to manage even complex systems of workers and their supporting technology elements, in the real world. We have already started to apply this approach in some industries, such as health care, and it’s likely that AI/ML, perhaps particularly in the form of task-specific agents linked through digital-twin-like models, could expand this considerably.

Could this sort of thing be what Cisco hoped for from AI, a salvation of network spending created by massive fusions of AI and IoT to link applications better with the real world? Even if Cisco were inclined to abandon its fast-follower strategy, could they drive this sort of thing? In a way, Cisco is like its telecom customers, trapped in a culture. So if they don’t drive the change, can they fast-follow it? It depends on who leads it, and how fast they go.

The biggest threats to Cisco in 2025 come from Broadcom and HPE, because the direction these two would likely take would make fast-following difficult. Broadcom’s logical goal would be to develop a new application set and commoditize networking it, so chips would win. HPE’s logical goal would be to create, via its Juniper acquisition, the perfect network companion to that new application set, thus locking up the network side before Cisco can mount a pursuit.

Another threat pairing is Ericsson and Nokia. Both companies are very interested in IoT, particularly interested in massive and even public deployments, because of the link to 5G. Both companies have been pushing IoT-related applications through sales and marketing channels, and both are directing their initiatives to network operators, telcos. While Cisco also sells to telcos, its AI and security initiatives are more directed at enterprises.

All of this, IMHO, means that Cisco may have to take a different path to growth than the one it seems to be touting now, the one that’s worked for it in the past. Following others to dominance relies on overtaking them, a challenge in itself with large players like the ones Cisco is now competing with. Whether Cisco has something in mind already is something I can’t claim to know, but I’m pretty confident that if they don’t, there will be other reorgs in 2025.

First, all 167 of these enterprises were cloud users in 2024, and I think the great majority were also in 2023. No non-users expressed plans to adopt the cloud in 2025, no current users planned to turn their back on it either. Of the group 37 companies said they expected to increase their cloud usage next year, 35 said they’d be looking to reduce it (repatriation), and the rest had no comment one way or the other.

Of the group, 48 said they had multiple cloud providers, but I think the actual number is perhaps more like 65; remember I’m getting unsolicited comments not asking/surveying, so there’s always a chance that an enterprise simply doesn’t say anything about a topic. However, only 5 of this group indicated that they actively used multiple providers to support the same applications; the great majority of multiple cloud users assign applications to a single cloud. Of the 48, 29 said that one of their cloud providers was used for SaaS, and of that group 21 said that the SaaS services were managed directly by a line organization, not by IT.

Interestingly, four of the five “true multi-cloud” users said their alternative provider was not one of the cloud Big Three; IBM and Oracle were both cited. In addition 14 enterprises said they were “considering” or “evaluating” the use of multiple providers, and in that group, all said they would look at both the other Big Three and outside that group, again mostly at Oracle or IBM.

Of the 37 companies who expected to increase cloud usage in 2025, all thought the majority of their increase would come from current cloud applications. Nine expected “some” contribution from new applications, but only two thought that new applications would add significantly to their cloud usage. Of the 35 who expected reduced cloud usage, the majority expected this to result from either usage management of current applications or redesign of the applications to reduce cloud usage. Only two said they planned to remove an application from the cloud completely. Interestingly, four said that their reduced usage would come from dropping a second cloud provider.

Overall, it is expected that cloud usage and spending will rise, since net of the gains and losses appears positive, but cloud growth isn’t expected to be as much in 2025 as it was this year, which in turn was less than it had been the prior year. Like any form of computing, cloud computing has a kind of natural level that, as it’s approached, limits incremental growth.

Note here that enterprises separate “cloud” and AI, except where they acquire AI as a cloud service, something only 14 of the companies said they’d done. That doesn’t mean there’s a lack of interest; the notion of getting a secure, meaning sovereignty-guaranteed, form of GPU as a service is popular with a full third of enterprises, mostly for use in training self-hosted AI models.

GPUaaS is complicated, say enterprises, for four reasons. First, it’s a balance between data sovereignty and cost, two things that enterprises need at the same time. How do you then balance them? Second, more and more enterprises see AI hosting as a kind of “revenge of the cloud provider” thing, meaning that they believe that just as they’ve gotten onto cloud-provider manipulation for normal applications, those providers introduce cloud AI to keep feathering their nests. Third, there is relatively little enterprise interest in GPUaaS except during model training, and during that period enterprises would tolerate a higher cost than they would for enduring AI hosting. Finally, fitting GPUaaS training to self-hosting of AI means understanding what the latter demands, and enterprises see AI as a kind of ultimate moving-target example.

Sovereignty is a contractual guarantee of data security, according to enterprises, and that means it can only be provided by a credible source with deep pockets. There are a lot of comments out there on the potential opportunity GPUaaS presents telcos, and there’s some basis for these given that telcos are near the top of the list of trusted guarantors of sovereignty. The problem is that enterprises realize the linkage between AI models and techniques, training, and GPUaaS is critical, and telcos aren’t seen as having any understanding of AI at all, and presenting no credible career path for AI professionals. One CIO told me “You know who’d be an even worse employment choice for an AI expert than an enterprise like us? A telco.”

For the second point, enterprise IT sees the cloud-AI play, directed at personal productivity augmentation of “Office” tools, as largely wasted money. One reason is that their own experience with “copilot” tools in coding has been increasingly disappointing. A year ago, five enterprises out of six said they believed AI assistance in coding would be highly valuable, but within six month that had dropped to less than half, and in the last quarter of this year, one in four continued to have a positive view.

The remaining issues reflect the increased dominance of analytics, what might now be called “agent-oriented” AI. This AI application set depends on company-private data, which means that it’s the same sort of data that enterprises have already decided they can’t cede to cloud storage. It has to be kept in-house, which means that the AI model has to be hosted in the data center. But training demands (say enterprises with experience) five to ten times the AI resources that running a trained model would require, and there’d be little or nothing to do with those extra GPUs when training was completed, which would make self-hosting prohibitively expensive.

Then, of course, there’s the problem of deciding how to self-host in the first place. Enterprises see AI as a moving target, and are reluctant to aggressively pursue it when it seems a new “better” model, technique, or both comes along every week. Enterprise uncertainties on AI are magnified by the concern that an early AI decision will prove sub-optimal when something better comes along. On the average, enterprises say that an AI approach is obsolete in about four months.

This enterprise “wait-till-things-settle” reaction to the pace of change in AI is impacting the cloud for 2025, I believe. AI is expected to revolutionize business, yet right now it’s changing too fast to adopt with any sense of securing full value from it. While this is the case, do you adopt something else in its place? That’s not how to deal with a revolutionary technology. AI is holding the cloud, and IT in general, hostage right now. I think that’s likely to be true through the first half of 2025, but by 2H25 we may finally understand how to move forward with AI, and where AI isn’t the answer. We’ll also know where AI needs augmentation, in particular how it links up with IoT and digital twins to generate data and establish context in real-world activities. Watch 2H25, then, for signs of where the cloud may be heading in the long run.

An astonishing number of critical network and IT assets have one common element—they need to be put somewhere, usually somewhere specific. Increasingly, that means somewhere proximate to points of user connection to networks, because latency is emerging as a key factor in almost all new applications of technology. But even for “mature” things like access networks, the most critical and cost-intensive piece of networks overall, pride of placement is critical. Wireless towers have to be put somewhere wireless users need them, and you have to feed towers with fiber that has to pass along rights of way. FTTH needs the same right of way, and even FWA needs to feed the broadband radio systems as well as site the antennas.

Given all of this, you might think we’ve uncovered the secret asset for telcos to leverage, and twenty years ago or so that might have been true. In the US, for example, we had nearly fifteen thousand telco central offices, and another seven thousand other facilities (parts stores and places for craft personnel to work, for example). We had over a hundred thousand telco-owned cell towers, too. Now? Telcos have been selling off real estate, from towers to facilities, for decades. It’s hard to get the data, but the majority of towers are now supported through shared-lease arrangements, over half the non-CO and almost a third of CO facilities are sold or on the block for 2025/2026. Telcos seem to have been selling off a competitive advantage, but that’s not the whole story.

The big push behind real-estate sales by telcos is due to a combination of shifts in telecom service opportunity and the focus of Wall Street. If you look at a central office, you see something that’s valuable as a point of connection between telecom switches and outside plant, mostly copper loop and remote nodes fed by T/E TDM trunks. Given that there are few places in the world where copper service delivery is profitable or even useful, you can understand how the value of a CO would diminish. Add to that the fact that real estate costs, and selling it not only reduces carrying cost, it generates money to fund things, and it’s not hard to see why there are short-term reasons to sell it off.

Those pressures are often unbearable when you can’t think of any near-term reason not to. We’ve heard for ages how telco real estate could be used, for example, to house cloud data centers, but telco successes in cloud computing aren’t exactly leaping off the financial pages, are they? We’re hearing now that edge computing or GPUaaS are similar ways that telco real estate could be exploited, but if we want to be fair, we have to ask why these “opportunities” are more likely to be exploited successfully than the cloud.

A good question, then, would be whether there’s some other powerhouse player that’ being created as telcos divest their real estate assets. That turns out to be really complicated to determine.

The “real real-estate” portion of assets, meaning actual buildings, seem to have been sold to a wide variety of companies, none of which seems to have any conspicuous technical credentials to flaunt. I’m told, for example, that the largest buyer of non-CO buildings sold by telcos has been trucking companies, perhaps because so many of these buildings were parts warehouses. The one closest to me, for example, was sold to a local trucking firm. The point is that I can’t identify anything suggesting that some secretive tech giants are buying up the buildings.

That may be a tad less true for former central/switching office facilities. While the relatively small number of these that have been sold off doesn’t show a pattern of prospective tech exploitation, some countries seem to be seeing newer acquisitions by companies whose industry affiliation is unclear, or who seem to be real estate investment companies or even shell companies, perhaps proxies for some larger players who don’t want immediate publicity. I think that some of the cloud providers may be arranging for real-estate assets near the edge, just in case.

That qualifier is important. The truth is that there are no current applications that could justify a major edge deployment of IT assets of the type that would validate real estate exploitation. That’s why the telcos haven’t held on to their own assets there. There are, however, two potential applications that could do that, and they have something in common—a direct link to the real world. In fact, that’s the only thing that can make the edge truly valuable in the first place.

Real-world applications are those that require synchronization with real-world elements, which means they have to be run in real time, with minimal latency between the application and the elements and processes they’re involved with. They also have to be highly reliable, because controlling a real-world process means creating a potential real-world disaster if you mess up. I’ve blogged about both in the past, so I’ll just mention them here.

The first is the empowerment of the portion of the workforce that’s not desk-bound, which makes up roughly 40% of all workers and slightly more than that portion of the total unit value of labor. We’re talking some steps now to realize the benefits of this empowerment, but only by slightly expanding the scope of IoT applications relating to industrial and manufacturing processes to cover multiple facilities and the interconnecting transportation resources. The applications are still largely local in nature, meaning they can be addressed with on-premises hosting, but if the scope of operation gets larger and the interactions more complex, cloud/edge hosting is likely required.

The second potential application is truly transformational, enough to touch everyone’s life. Fill the real world with sensors. Give each person an “assistant/agent”, likely based on AI, and give every point of purchase and service the same thing. Drawing on the mesh of sensors, and the interests of both buyers and sellers, AI agents would interact to help everyone work and live, finding thing we want, helping us with our tasks, even entertaining us.

Most of the technology needed to support either of these applications is already available, and what remains is more a matter of policy than of technology. If tech is to do more for us, it has to integrate more tightly with our lives and work, with our world. That raises issues about privacy and security that we’ve been, so far, reluctant to address. Can an application that helps us work also monitor how hard we’re working, and can one that can help us find each other or find a product or service we want to buy, also stalk us?

Hype may save us here. I think the overwhelming amount of hype we deal with is largely due to the fact that, absent things of real value and interest, things are invented or exaggerated. Might these two applications benefit either by becoming hype targets, or by becoming a way to realize the exaggerated value of things like AI. We can hope, not so much for next year but perhaps soon thereafter. Maybe even for the telcos, while they still have real estate to play in the new games.

Happy New Year.

What is the core value of AI? The answer is that it can do stuff a person or group could do, but faster. “Artificial intelligence” is the key term; maybe our use of the acronym has made us forget. The point here is that AI does what we do, but faster/better. We can enter something into a search engine and AI can digest the results, summarize the points if you like. I get AI results all the time when I search, which is often. I sometimes find them helpful, sufficient for my purpose, but most of the time I need to look at actual search results to get the details I need. Is this use of AI transformational? Not to me, nor to enterprises I talk with. Businesses don’t run on the Cliff Notes of economic activity, they run on the details. The most common use of AI fills our appetite for instant answers, but that’s not going to be enough to justify massive investment.

Where do enterprises find AI helpful? In most cases, what they really like is something like the notion of the AI agent, which enterprises believe is an AI model that’s trained to operate on a very specific set of information. While we hear about agents mostly in the context of autonomy, meaning AI acting alone, enterprises are generally not comfortable with having AI act on information without human supervision. So the value here is specialization, and the reason that’s valuable is that AI can quickly analyze something that a person or people would take longer to analyze, and in applications where time is critical, AI offers a real advantage.

Enterprises say that they also like AI in business analytics missions, because AI can spot patterns that people simply take too long to find. Do enterprises believe that their staff is incapable of analyzing the same data and reaching the right conclusions? No, but they think AI could do it faster and perhaps (in its agent form, not its generative form) more reliably. Can I do my taxes? Sure. Could an AI tax agent do them better? Sure, but so could a CPA. AI agents offer speed and specialization.

CIOs are getting fairly strident in their rejection of the popular “copilot” form of AI, which they classify as a kind of attempt to popularize AI and dodge actual business-case scrutiny. One told me “We have thousands using AI to help them write emails or maybe short memos. Tell me how this does anything for the company. What’s driving it is that as-a-service AI is expensed, and most companies, like us, don’t evaluate line-department use of technology delivered that way. If we did, we’d probably crush it out.”

All of this seems validated by recent comments by Broadcom. The new-age chip giant says that there’s a sea change underway in the AI space, a shift away from GPUs to specialty chips designed for machine-learning applications that sure sound like agent applications to me. If true, it could be the first silicon signal that AI focus is shifting away from the hosted chatbot model to something enterprises have said they favored.

OK, so what does this mean? I contend it means a lot of what’s claimed for AI doesn’t stand up to what those who’d have to invest in it consider a realistic assessment. I read an article early this week that claimed that AI would demand fiber and that telcos were eager to see that. Will AI demand fiber? Surely it will inside clusters of AI-GPU servers, but in the network? Our first example of AI, the typical search-enhancement example, may be nice to get an answer to a simple question, but how much is that worth? Remember my comment about running a business on Cliff Notes? Same with running a part of one, or a network. Given that, how much traffic does AI generate outside its own cluster? Enterprises have told me from the first that there is no impact on network traffic created by AI outside the AI cluster and training connections.

What would make AI transformational? Data, and more specifically, real-time data. We run businesses and enhance productivity, buy and sell products, based on information very similar in terms of timeliness to what we had when it was punched onto cards in Hollerith code. AI value demands we shift not how we process things as much as shift what things we process. Getting that real-time data to AI could increase network demand. AI could enable applications that, without it, would be difficult to create, and the running of those applications and/or review of the results could generate traffic, too. The business value of those applications could create benefits to justify investing in them, and in their traffic handling.

A lot of the value of AI, then, is linked to growth in IoT. It’s not so much in what gets media and PR attention—things like autonomous vehicles—as it is simply exploiting real-time information about business processes, not simply recording the result of those process. A sale, for example, might be a single record in the traditional handling of business results, but it might be multiple steps in real time. As real-time processing is integrated into the work itself, it generates more data and also has the potential to impact the productivity of the worker more directly.

The problem is that an IoT-real-time AI approach crosses a lot of technology lines, and few vendors are in a position to profit from the whole food chain. Given that, will any vendor see enough benefit to drive its own portion forward, especially when other essential elements may not be provided by the vendors responsible for other related technology spaces? Enterprises seem to think that progress here will come from a vendor willing to frame a kind of IoT/AI platform, and they name three candidates—Broadcom, HPE, and IBM.

I think the situation with AI is hopeful. Despite a major wave of hype on applications enterprises don’t think will make a business case, we’re seeing enterprises dig through the hype to find actual, valuable applications. We’re also seeing some companies talk about AI reality in a forum that really matters, Wall Street. Good things may be on tap for 2025.

A couple decades ago, I was fiddling with my modeling tools and determined that there were some very simple factors that decided just what broadband prospects might represent low versus high apples. One was “demand density”, a measure of the economic strength of a specific area of geography, and it’s roughly the GDP per unit of area. The second was “access efficiency”, which was a measure of the cost of deployment per unit area. If we were to (OK, I know this is one of those “roughly” things) relate the two, the potential ROI of an area is roughly the demand density divided by the access efficiency.

If we look at large geographies, like countries or the old Bell Operating Company regions, I’ve found that this “roughly” is good enough for all practical purposes, but if you zoom in you find that within a given large geography, there are places where our magic ROI ratio comes out favorable, and others (perhaps even others nearby) where it indicates the apple there is very high indeed. I did some quick assessments of this, and found that in the US it’s easy to find places as big as a hundred square miles that had magic-ratio ROI potential five hundred times as good as other places of similar size less than a hundred miles away.

When broadband is looked at through a wide-angle lens, we can assume that regulatory policies would be effective in leveling the playing field across most of the pieces of a large geography; a form of cross-subsidization. But this is less effective in a “privatized” world where players have some flexibility in where they target. It’s also not always fair to have people who elect to live in an area where magic-ROI ratios are certain to be low be offered superior facilities at the expense of others. One operator told me five years ago that the majority of their high-apple locations were high-end dwellings, and that the public “would be upset” if they knew who subsidies were being directed to.

Universal service was critical in getting basic phone service to everyone, but I think it’s clear to everyone that it’s not going to be as easy to get superior broadband to everyone. There have been suggestions that the solution to this lies with “public broadband” services, offered by cities, counties, or states, but where the governments span a large area we’re back to the question of whether the subsidies are equitable, and where they’re focused on a small area they may exacerbate the problem by picking the middle levels of our apples and putting the high one even more out of reach. Classic broadband, meaning media-based broadband creates this problem.

Ever hear of “pass cost”? It’s the cost a broadband company has to pay just to get broadband close enough to allow customers who order to connect; the cost to “pass” their home. When I first moved into my current home, I had no broadband at all because no provider “passed” me. Today, two media-based broadband providers pass me, but there are parts of my community where only one provider passes homes, and in those areas the broadband speeds available to me are simply not available at all. In a few areas within 20 miles of my home, nothing that approaches competitive broadband is available at all. Rural broadband subsidies have helped, but I think it’s clear that even in my own area, “universal broadband” isn’t the same thing as “equivalent broadband.”

The situation with broadband is not unlike what’s been, and is being, faced by postal services worldwide, at various levels. Changes in consumer behavior and communications, including and especially broadband and the Internet, have had major impacts on postal revenues, and most proposals to transform the agencies would create a risk to those living in rural areas, because the daily delivery costs can’t be covered by the revenue customers there can generate.

But things there are changing quickly, in networking at least. What’s changing them is mobile broadband and FWA. The explosion in the use of smartphones combines with the fact that premium customers often travel, to create demand for cell service in a broad area. Starting with 5G, the same technology can be used for FWA, and the fact that FWA carries the “last mile” without physical media significantly reduces that pass cost problem. Satellite broadband is also growing in popularity, though it rarely provides much more than a shadow of the service bandwidth that’s available to media-based or cellular broadband.

The problem here, in an era of global privatization, is that uneven demand density and access efficiency situation. In even rural areas there are towns, and whether these communities decide to deploy their own broadband media or a specialty operator decides to offer service there, quality broadband may be practical there when it’s not practical over the larger surrounding area. Various universal service subsidy approaches make more sense in these pockets of opportunity, too, so programs may appear to be improving broadband populism when they may be doing little or nothing for those high-apple areas.

I grew up in a rural area, and I know many who still live there. They often complain about broadband quality, but the also complain about a lack of convenient shopping, easy access to airports, quality and accessibility of schools and colleges, and other things that relate to the economic efficiency of providing a given service where there are simply not enough consumers to make the service profitable. Would it be possible to level the broad service playing field across all geographies and demographics? I doubt it. Should we try to accommodate the differences better? Surely.

With respect to broadband, providing better FWA is an obvious strategy, but could local governments not also be encouraged to run broadband media to residential areas, and require ISPs to connect to it? A standard strategy for that media, and even a “kit” to provide local access via the media, could reduce or eliminate the variation in service quality that some say is a problem with municipal broadband. Another useful step might be to require websites to deliver leaner content to sites with low bandwidth, and to stop launching video in windows without the user asking.

Another point here is the link between broadband policy and “copper retirement”. In many countries, including the US, there’s been a duel between the local access providers and regulators over the issue of retiring the copper twisted-pair plant. This plant cannot deliver competitive broadband, and the cost of sustaining it is a burden on telcos, enough that it may hamper deployment of suitable broadband facilities. The argument that retiring copper will put everyone at the mercy of “flaky Internet telephony” flies in the face of the fact that the majority of people already depend on smartphones for calls (if they call at all) and that many don’t even have a copper-based telephone installed in their home. This sure looks like a bad example of regulatory policy, which we’ll get to now.

The problem with broadband quality I’ve been talking about is greatest where overall (meaning national) demand density and access efficiency are low, which tends to be those with a large geography or with economic challenges. The US, Canada, and Australia fit into the first group, and most of the “third world” into the second. For that second group, there may be no good solution in the near term, other than to focus on wireless and the 5G standards. The problem with the first group has arguably been political—doing the right thing isn’t often doing the thing with the best political outcome.

I suspect that, even with wireless and FWA, broadband inequity is going to increase simply because it makes business sense to serve best those that pay most. There are no simple solutions to this, and things like municipal broadband may help some rural users but will exacerbate the problem for others who don’t live in an opportunity pocket. To the extent that the problem can be solved, it’s going to take a more thoughtful (and less political) approach to solve it.

Extreme has, for decades, battled for market share against giants like Cisco and Juniper. In this fight, as is often the case with a battle against giants, it’s been willing to deploy unconventional weapons. It introduced the notion of cloud-based management, and even a form of AI, well before it was prominent in the positioning of others, and even launched a network digital twin in 2022, as a means of gaining a systemic understanding of complex network infrastructure and its relationships to users and services. All of this seemed to be aimed at creating differentiation in a market where pushing packets is pretty much a matter of ones and zeros. Management and security seemed to be a good place to differentiate, and they still seem that way today.

Platform ONE is, as the name suggests, a broad tool. Its target is both network management and security, which hits a lot of enterprise hot buttons. The dual mission may be helpful to Extreme, because while security is a major enterprise priority with secure funding, there’s continuing skepticism about “platform” tools in the security space. It’s already got enough layers, say enterprises, and for most, Extreme isn’t a current layer provider but it wants to change that with a combination of cloud composability and AI.

AI is an almost-universal add-on to tools and systems these days, but it appears to me that Platform One is designed around AI rather than having AI plugged into it. In their presentation to analysts, Extreme AI Expert is the glowing core of the concept, in fact, the “One Ring” without the sinister Tolkien context. The principle is that networks are networks, and operationalizing and securing them as separate technologies or vendors is sub-optimal. Ops is best thought of as systemic, crossing management and security, LAN and WAN, virtual and physical. The more you know, the better it is.

The platform is then a cloud-hosted SaaS application, framed around an AI Expert core that in turn surrounds Extreme’s management/feature layer of tools, already hosted in the cloud. All Extreme product data is collected, and ecosystem partner data is likewise integrated. Other vendor equipment can be linked in via APIs, but it seems this would likely be the responsibility of channel partners or users to accomplish, at least at this point.

The user interface is both hierarchical and role-based, meaning that since Extreme’s sales conduit is largely based on resellers/integrators, the channel partner has a super-view of its customers, which then have their own set of role-based views within their own domain. Orchestration of the agent elements, governance and interface/data security, and platform service features are all integral to the Platform ONE core.

AI is integrated with the GUI features at all these roles/levels, and as noted it’s a core element of the platform and not a chatbot add-on. Three modes of AI operations are supported; conversational, interactive, and autonomous. In conversational mode, the AI element responds to user questions, much like a chatbot. This mode seems similar to a polled management framework; look when you want and see what you need. In interactive mode, the AI element will present conditions, like an event-driven system, and make suggestions, and the user can ask questions and implement recommendations. In autonomous mode, the AI element will actually take control and respond to things.

In terms of roles, Extreme offers two distinct classes of “users” (as opposed to channel partners offering integrated services). One class is called “users” and the other “buyers”, which may be a bit confusing, but reflects a distinction between those who operate the network and those who procure it. Things like budget planning and license and contract management fall into the latter category, while the former focus is the traditional operations elements. The progression of Learn, Plan, Deliver, and Fix is explicit in the design, with both the user and buyer classes having involvement in each step.

The goal of Platform ONE, in a functional sense, seems tied to workflows as a binder between infrastructure elements, network services, and user experience. Extreme has a longstanding interest in and support for virtual networking, and while the use of a virtual network is not mandatory with Platform ONE, I think it would enhance its capabilities by providing an explicit connectivity framework that can integrate the network environment.

Speaking of integration, one major question Platform ONE raises is how it’s adopted. Obviously, Extreme users can expect to leap into it and achieve real benefits. What about taking on Cisco or Juniper, though? The big money in networking these days is in the data center. The more cloud-centric an organization is, the less chance there is that there’s substantial network incumbency and an associated equipment transformation to deal with at the time of sale, but of course the less network there is, the less money there is in winning the customer in the first place. I think that real Extreme success has to come from actually displacing Cisco or Juniper, not from having the user shift away from the data center.

Would Platform ONE be enough, functionally, to justify a rip-and-replace? Probably not, unless either the competing gear was already old or there was a major change in network requirements that would justify replacing gear. Would it be enough to justify taking some or all of any planned network refresh? Yes, for many users, providing that it could deliver value to users during what Extreme would surely hope would be a network transformation in Extreme’s direction. That means pulling non-Extreme gear into the tent or depending on that “major change” to justify the refresh. The former approach is obviously safer and, if successful, more profitable. It’s also more appropriate for a channel-dependent vendor like Extreme.

Channel partners want leads more than anything else. They want their vendors to do the heavy lifting at the marketing and strategy level, generating excitement and prospects to call on. Even partners who have the skill and visibility to build their own leads will usually rely on vendors to pave the way in positioning. Extreme’s Platform ONE has the potential to generate excitement and leads, but the question of how it’s introduced into an account that’s not already using Extreme gear is important if Extreme is to take on, for example, Juniper as its customers navigate the HPE acquisition. Extreme CEO Ed Meyercord told CRN “Business doesn’t continue as normal when there’s a fundamental change like that. … And then that becomes a great opportunity to consider an alternative.” Like Extreme, obviously, and Platform ONE, with an appropriate Juniper bridge, could be just that. We’ll have to wait to see if that appropriate Juniper bridge, and the one for Cisco, is built. If it is, then both Juniper/HPE and Cisco might have to start looking over their shoulders.

When enterprises comment to me about “debt”, they tend to focus on what they see as a kind of IT populism, the increased direct use of IT-facilitating tools by line departments. CIOs and other IT professionals understand this; there’s pressure on line management to improve their operations, and often the time required to engage internal IT is seen as an issue. They also realize that as-a-service trends have made applications more accessible to line organizations’ staff. Yes, there is surely some IT parochialism involved here, but it does seem clear that the impact of what’s often called “citizen development” on technical and data debt has been under-appreciated.

Line organizations are parochial in their own thinking, by design. Companies are organized by role in the business, and you can’t have everyone running out to do others’ jobs without coordination. The same thing is true of development, of IT in any form. In the past, I’ve noted that things like cloud computing, particularly SaaS, and low-/no-code are most likely to be successful if there is some IT coordination, at least in the initial design, and particularly as a means of ensuring that organizations with interlocking activity don’t end up building their own silos.

Almost half of enterprises say that they impose little or no policy constraints on citizen developers, and almost two-thirds of this group say it’s not necessary. Of the over-half who do set constraints, the most common is a restriction on “chaining” applications, meaning having citizen developers write applications that run on the output of other such applications. However, it’s interesting to note that just over than a quarter of enterprises who set that constraint don’t constrain having citizen applications create databases or database records, and of course that can easily lead to the chaining they theoretically forbid. It’s also, I think, the source of a lot of serious data-debt risk.

Almost all enterprises say that it’s possible that citizen developers might create data that is redundant, contradictory, or flat incorrect. Often, some say, the duplicated data is in a different format from an IT source that the citizen developer didn’t know about. Five enterprises who set rigid control say that they had a major problem with data integrity that arose from use of low-/no-code tools, and now require an audit on such applications.

One area where data debt seems most likely relates to Office applications, spreadsheets and databases. Not only are these often passed around among workers, they are sometimes imported into major and even core applications. Spreadsheets were the big data-debt problem for four of the five enterprises who found it necessary to clamp down on citizen developer practices, but all four admit that they really have no way of knowing whether workers with Excel skills are conforming to policy. Half admit they suspect they are not.

How about AI? Only a few (less than one in ten) enterprises have considered the impact of AI on data debt, but all of them expressed some common concerns. The majority of them, while not necessarily spreadsheet-specific, are often related to spreadsheets.

One of the common value propositions for AI copilot technology involves assisting in the creation or analysis of spreadsheets, and this format is regularly used within line organizations for “casual” analysis of data. I’ve seen, in client companies, issues with what we’d now call “data debt” in Excel spreadsheets and Microsoft Access databases almost from the first, well before AI. But AI might well make things worse.

AI copilot technology used in development organizations is regularly characterized by enterprises as a “junior programmer”. They believe that results of AI code generation requires collaborative code review to prevent the classic technical debt problem. Surely the same sort of problem could happen with Office tools, and I’ve seen AI-assisted Word documents and AI research results that were truly awful in terms of quality. Could we expect our line worker, who obviously feels a need for assistance in the use of Excel, to understand the results and audit data quality? Obviously, no.

Enterprises almost never offer AI-linked comments on data debt at this point (which I think means any purported research on the topic has major risks), but remember that one of the long-standing complaints enterprises have offered on AI results is the difficulty associated with tracing the steps taken to get those results. Any given AI result could be a “hallucination”, and work to allow AI to retain context through complex analysis means chaining those results. Can we trust them? If there’s even a five percent error/hallucination rate in an AI analysis, the chances of getting accurate results from four chained analyses is less than one in four. And, would we know if that happened?

Data debt is a real risk, perhaps a greater risk than technical/code debt because of the “garbage in, garbage out” truth of IT. While there are surely benefits to AI, and to broader “citizen developer” participation, there doesn’t seem to be much doubt that both can contribute to data debt, and that would work against the business case for AI. You can’t improve company operations when the core data you use is being eroded in quality by the very mechanisms you’re relying on to make things better.