Electricity. Power. Energy.
Aside from oxygen, water and food, is there anything more important for today’s modern world? Without power, nothing works – including lifesaving medical equipment -- and when the batteries and generators die, everything comes to a dead stop. Everything. Incredibly, we take electricity for granted, and when power outages happen, we take on all sorts of messy behaviors and deal with all sorts of crisis situations. If this sounds like a recipe for really bad CX, you’re right, but there’s more at play here.
I recently attended – and spoke at – the NCEC Cooperative Technologies Conference in Wilmington, North Carolina. The North Carolina Electric Cooperatives represents the 26 cooperative power providers across the state, primarily serving rural populations. There are some 900 electric cooperatives across the US; for enterprise communications professionals, consider these utility cooperatives as the SMBs of the utility world.
These cooperatives face some major technological challenges, that not surprisingly, are similar to those faced by SMBs in general, as well as the dilemmas faced by professionals in other vertical sectors. Having been involved in the smart grid sector for some 15 years, the challenges are as familiar to me as those facing UCaaS and CCaaS vendors, and here are three in particular from the conference.
You might not think AI is much of a story here, but it definitely is. Keeping the utility grid that makes modern living possible is imperative, and doing so depends on internal operations that require highly sophisticated automation and control systems.
In legacy terms, Supervisory Control and Data Acquisition (SCADA) is a prime example of this for utilities and has long been in use in verticals where most processes are automated, such as factories. Then there is the ongoing maintenance and management of the grid, which covers a lot of ground – literally - and this is where tools like sensors and drones provide remote coverage in ways that humans cannot.
The parallels of electricity providers to telecom are similar, as both are utilities that have experienced governmental regulation. More importantly, both operate transmission networks that are heavily-based on physical infrastructure. There is a key difference, however, and it’s central to why AI is so important to utilities.
With telecom, physical wired networks aren’t the only way to transmit data; most traffic, in fact, is now wireless, and satellite networks are a growing alternative. Whereas the content of telecom transmission – data – can be easily stored and shared, electricity cannot. There is no large-scale way to store power, and it can only be distributed over a wired network. This creates all kinds of challenges for utilities to forecast both power generation and consumption, and AI has a role to play for developing more accurate predictive models.
Another reason why utilities are a prime use case for AI is that all the inputs and outputs are measurable. This is a very data-intensive sector, so AI can be applied here very effectively – probably more so than in enterprise environments. As such, AI is not unfamiliar to utilities, but like everyone else, they are struggling to use it effectively, responsibly, and with a solid business case.
During the conference, we often heard concerns about the usefulness of Generative AI, and how AI can help deal with the growing range of cybersecurity threats. Another issue is growing pressure - both from energy consumers and regulators (namely the EPA) - to be green, and shift from fossil fuels to renewable sources. Here as well, utilities are looking for AI to help with optimizing their energy mix and finding new ways to incorporate renewables into the grid.
This is very particular for utilities, and of course what impacts utilities impacts every power consumer, including telcos. A key issue is climate change-related, as abnormal weather patterns have become the new normal. Aside from making it hard to forecast energy needs, extreme weather events can be very disruptive for service reliability. Hurricanes and fires can decimate above-ground infrastructure, and flooding can do the same for below-ground infrastructure.
Service reliability has always been table stakes for connectivity providers like telcos, so SMBs and enterprises alike face real business continuity challenges when power outages occur. At face value, this issue matters greatly to both businesses and utilities, but the fallout is much greater for the latter.
With energy being mission-critical for just about everyone and every business, it’s not surprising to see that AI plays a key role. AI can only do so much to prevent outages – as per above, extreme weather events are unpredictable – but AI can be more impactful for outage response, such as real-time identification of the cause, re-distributing power until the outage has been fixed, and notifying customers as to when they’ll get power back.
Non-weather factors also create reliability issues, with two in particular being cyberattacks and aging infrastructure. These issues have parallels for both businesses of all sizes, and service providers like telcos. Cyberattacks have become a serious issue for utilities in recent years, as they are soft targets for bad actors, along with other forms of public infrastructure like airports and the water supply.
Utilities and businesses alike are using AI to protect against cyberattacks, and it’s now just part of the landscape, as these threats will only intensify in today’s volatile political climate – both domestically and from abroad. During the conference, we also heard about physical attacks on the grid; this is a different class of threat, requiring a different type of response, but also well-suited for AI.
Aging infrastructure is another service reliability concern, and there are parallels here for businesses still rooted in legacy, premises-based technology. One of the speakers noted that much of grid’s infrastructure was built before 1978, and with today’s higher energy demands, outages are only going to increase. Not only is new infrastructure very costly, but construction takes time, and compliance with current environmental regulations makes it harder to get capital funding. In terms of managing with existing infrastructure, AI can be used here to better optimize asset utilization and automate operations to mitigate against service disruptions.
The need for AI to manage service reliability is actually more acute for utilities than for businesses, as they don’t have options for virtualization or going to the cloud. Energy requires a physical network, so there is no failover like businesses have when cloud outages occur, and telephony traffic can be routed over the PSTN.
Within businesses, the contact center is a good comparable for this issue with utilities, in that many are still tied to legacy platforms that cannot easily be swapped out, and these aging systems simply cannot meet today’s customer expectations. AI goes a long way to addressing that, and I’ll come back to the contact center in the conclusion of this article.
This is the third parallel between utilities and businesses, and it presents fundamental challenges for both. In simpler times, energy demand was easier to forecast, along with building capacity to support these needs. Today - for consumers and businesses alike - there is now an insatiable demand for data, connectivity and cloud-based services, all of which rely on an ample and reliable supply of electricity.
In our personal lives, a good example is electric vehicles, which creates an entirely new set of demands on utilities, not just to power EVs, but to build out and support a charging infrastructure that has never existed before. This space is still nascent, but the broader trend for electrification in the transportation sector extends well beyond cars - think trucks, trains, e-bikes, etc.
The overall impact on energy demand here will be substantial, and environmental concerns are a key driver. In the US, federal policy has set a target where by 2030, 50% of new vehicles sold will have zero-emissions. This target has become fluid, as marketplace realities must be accommodated, but the longer-term trend is here to stay.
Shifting focus from personal energy demand to those for businesses, the AI gold rush is another trend that was not on the radar of utilities until very recently. The exploding demand for all things AI to make businesses more competitive has triggered the need for new forms of capacity to aggregate, analyze and store massive datasets. While businesses are looking to AI for all kinds of solutions, the associated costs are too prohibitive to be done internally for almost everyone, so this is largely the domain cloud providers.
For AI to reach sustainable critical mass, data centers must be built on an unprecedented scale. Aside from the environmental concerns around heating and cooling, this represents an entirely new energy demand vector that utilities were never built to support, and there is genuine concern about their ability to do so now.
Just as we took the dial tone for granted in legacy telephony, it’s easy for all of us to take electricity for granted. It’s always there – until it isn’t – and then the impact is immediate. I’ve touched on three challenges discussed during the NCEC conference that clearly show a growing divide between supply and demand for power. There are strong parallels to businesses as they struggle to stay competitive while being tied to legacy technology, and for both scenarios, the promise of AI provides a way forward.
Utilities are in no position to build their way out of this by adding more infrastructure - at least in the short-term - and AI offers a variety of new capabilities to make things more manageable. The situation is no different for both SMBs and enterprises, as That brings us to the contact center, where utilities are able to realize tangible benefits from AI today.
All of the challenges outlined above manifest themselves in customer service issues, both for residential and business utility customers. Customer service has generally not been a priority in regulated industries like this one, but today’s energy customers do have choice, and are important partners in helping utilities balance supply and demand. While the contact center has nothing to do with energy transmission and distribution, it has everything to do with helping customers understand their power bill, and being their communications lifeline when the power goes out.
In this regard, I was a small part of the solution during the conference, as I returned to give another presentation about how AI can help utilities with these challenges. This year, my specific focus was the contact center and how both AI in general – and conversational AI in particular – are helping utilities improve CX today.
Overall, the CX benefits from AI are similar to those in other verticals, but for rural cooperatives, this is a big step forward; customer service has never been a core focus for them. Throughout the conference, there was clearly a healthy interest to better understand the AI story, and I’m optimistic that our session will open the door for them to further explore the broader possibilities that AI can provide today and beyond.
This post is written on behalf of BCStrategies, an industry resource for enterprises, vendors, system integrators, and anyone interested in the growing business communications arena. A supplier of objective information on business communications, BCStrategies is supported by an alliance of leading communication industry advisors, analysts, and consultants who have worked in the various segments of the dynamic business communications market.
