Many of you know that I use a survey-driven demand model to forecast stuff, and recently I decided to run the model on one of my favorite topics, managed services. The model forecast significant growth over the next five years, peaking in 2025 at an annual level of over 50%. That’s sure interesting, so I wanted to talk about what’s going on now, and why my model thinks the future for managed services is so bright.
From the first days of corporate networks, it was an axiom that the total cost of network ownership (TCO) was half capex and half opex, which is another way of saying that running your network costs as much as buying it. You might expect, given this, that improvements in network management tools would have impacted that ratio of TCO components, but that’s not been the case according to enterprises. What has helped over the last two decades has been the shift to VPNs versus router-and-trunk network-building.
What has hurt netops cost control, interestingly, is security. Two-thirds of enterprises I talk with tell me that they spend more on security management than on the rest of network management. A large part of this is due to the fact that security involves so many different functions and layers, and it’s generally true that the management of a complex system is related to the complexity, meaning the number of elements you have to manage.
Another large part of security cost is the support of remote sites, including WFH. Over the last decade in particular, companies have worked hard to expand their footprint, and doing that has meant adding sites and network connections to places where there is no support staff and in most cases, no qualified technical resources at all. Internationally, on the average, over two-thirds of sites have nobody present who has what the company considers “adequate technical literacy”. Those locations not only can’t participate in security management, they can’t participate in network operations management, and in many cases can’t participate in remediation even if they’re talked through it—which can be an issue in itself for far-flung locations.
One big multinational company gave me some great input on this. They have over two-thirds of their sites outside the US, and a third are in small countries with limited technologists in the labor pool. Their first initiative to address this was to improve their operations tools, but they found that well over 80% of their problems created a loss of connectivity and so the tools couldn’t see what was wrong because something was wrong. They next worked to pick someone suitable, usually the manager of a site, and train them as technologists to provide local network/security support. In almost every case, the person they trained was gone within a year, hired away by someone else. They finally solved the problem with managed services.
This doesn’t mean that managed services are without their issues. The biggest problem for the concept has always been cost. In order to offer managed services, the service TCO has to be little more (at worst) than the perceived TCO of the self-managed option. Even where there are issues that make self-managed network operations difficult, enterprises still balk as the TCO rises toward that magic two-times-network-cost number. Managed service providers need management economy of scale, meaning that they need the cost of managing their customer base, calculated on a per-customer basis, to be better than the customer could achieve individually.
A surprisingly large number of network operations and security operations tools don’t consider management economy of scale at all. Generally speaking, management efforts are focused on responding to events. Once the number of events reaches the level where you can justify a 24×7 workforce commitment, more events will just mean more work, and more workers (you can review queuing theory and the classic bank-teller examples for details).
The key to effective bank teller operations is managing service time, and the same thing is true with managed services and economies of scale. If the human time needed to handle an event is reduced, then the number of events that a given number of people can handle per unit time is increased. For a managed service provider (MSP), this is critical in containing the price of the service and the rate of adoption while sustaining reasonable profit margins. The goal of MSP tools should be to reduce human effort as much as possible.
According to MSPs themselves, there are two specific things they’d want their tools to provide. First, they want to be able to deal with an event with the smallest possible human intervention, without sacrificing accurate handling and low rate of error. Second, they want to be able to spot an impending problem and either deal with it before it becomes a failure, or at least get as much information on it as possible before they lose contact with their remote site.
Artificial intelligence and machine learning (AI/ML) are obvious technology strategies to provide what MSPs want. It’s hard for me to get specific model data on the application of AI/ML to MSPs because my universe of MSP contacts is small and the percentage that use AI/ML is also small. However, I do have some data on the attitudes of MSPs, and enough to be able to support at least minimal modeling and forecasting.
MSPs almost universally agree that AI/ML, if properly implemented, would be of “significant” or even “critical” value to their business case. That’s the best response of MSPs to any proposed technology improvement. The average expected improvement in MSP pricing/profit and sales is roughly 33 percent.
Only about a third of MSPs say that they have had a viable AI/ML strategy presented to them. The number one reason is that MSP services are almost always linked to specific edge devices, and most of those devices come from vendors with no effective AI/ML strategy. Even where the vendor involved has a good AI/ML strategy, there’s only a bit less than a two-thirds chance that it will be presented effectively. The MSPs say that the vendor’s salespeople are likely to push hardware features and benefits explicitly, and present AI/ML benefits superficially. Where the benefits are provided, they often fail to consider the MSP’s specific role; the presentation is almost enterprise-centric.
Another issue MSPs cite in how vendors view service/security management tools in general, and AI/ML in particular, is the issue of scope. The reason why MSPs are valuable in remote sites is that those sites lack technically qualified people. A corollary to that point is that the people in these sites are unlikely to recognize the source of a network failure and identify who should be called. Every single MSP I’ve chatted with says that they get a “significant” number of calls relating to problems that have nothing to do with the service they provide. In fact, connectivity issues on premises, which include things like unplugged cables, devices that are turned off, or bad configurations on local devices or systems, are often the number one source of calls. This is a problem for MSPs because they’re expending management resources on something that they’re neither responsible for nor getting paid for.
Most MSPs would like to offer local-network management services. They say that not only would it be a profit center, it would also give them a come-back to relentless reports of problems that are outside the scope of their SLA with the customer. “We can’t see the conditions here, but with our whiz-bang Local Network Guru service, we can diagnose issues right down to the computer.” Make a profit from selling what you’re now giving away at a loss.
Let’s now circle back to the model data. The model is saying that support issues in remote sites already encourage managed services where the sites are truly remote, meaning that local technical skill is limited. Over time, the model predicts that network and security complexity will grow, and that the growth will gradually increase support problems even in less-remote locations. By 2025, over two-thirds of all remote sites will be considered “difficult” to support, and it’s this factor that multiplies the interest in managed services.
The model data suggests that while there are two obvious sources of managed services (MSPs and the network operators themselves) the fact that managed service interest tends to start in “thin” locations means that MSPs have an opportunity to jump in and gain credibility when the network operators who are local to the enterprises are unable to offer service in those thin locations. As interest in managed services shifts to more and more remote sites, the MSP offers the path of least disruption in adding the sites, which means they could have an enduring role even in major-market areas.
The model also says that local-network management add-ons to the MSP relationship are likely to follow the same thin-to-thicker path of evolution. Companies who experience the benefits of local network management in areas where staff technical skills are minimal are likely to extend those services to “thicker” sites as they’re added.
Another important consideration in managed services is that the number one driver of MSP interest is SD-WAN, for the obvious reason that SD-WAN is becoming the VPN strategy de jure for remote sites, even when those sites are in major market areas. The model says that an effective managed service story, combined with a good SD-WAN offering, could create a compelling offering in almost any market area. For the network operators, reluctance to get into the SD-WAN and managed services game risks being disintermediated by early buyer interest that they can’t or won’t address. Getting those early MSP commitments reversed will then be difficult.
The final point in our view of the managed service future comes from worker demographics. We are facing an explosion in the demand for skilled network operations people worldwide, and there is little chance that the workforce can meet that demand. “Management economy of scale” is important, as I’ve noted, but not just to lower overall cost, but also to conserve and optimize a resource that will be more important every year, and proportionally less available. Most people today can’t fix their own cars, and in many areas we’re already at the point where most enterprises can’t fix their own networks. More and more will face that problem, and as they do we can expect managed services to prosper.