Navigant Research Blog

What Exactly Is AI? Does It Matter?

— June 19, 2018

The more I read about artificial intelligence (AI), the less clear it becomes what people mean when they talk about AI. And while semantic arguments can be a waste of everyone’s time, a loose definition of Al presents an opportunity for vendors to package software into something that it isn’t. Blockchain and AI vie for position as the most overhyped term in technology today. But blockchain at least benefits from a common understanding of the underlying technology, even if its potential uses are severely overstated. Any buyer of AI-powered software should beware of strangers bearing gifts: always make sure that AI products are capable of what you want them to do, not what the vendor claims.

AI Beats Expectations to Be Simultaneously Everything and Nothing

Everyone has a definition of AI. Your perspective, knowledge of analytics, and desperation to sell some technology have a great bearing on what constitutes AI. I have seen definitions that range from “any code that includes an IF statement” to “nothing currently in existence.”

Technology marketers can never be accused of reticence when it comes to latching on to the latest industry buzzword. AI’s loose definition means it can be applied to virtually any piece of technology. An IF statement in a computer code means that a computer will make a decision based on some form of data input. At a very basic level, this is an intelligent decision.

Many disagree and claim the boundary for what constitutes AI lies in more sophisticated analytics processes. Essentially, people look for examples of a computer mimicking human thought. The big question is: How close to human thought does a computer have to get before it is considered capable of “thought?” Indeed, each time a new development happens in the field of advanced analytics, such as AlphaGo beating a human at the notoriously complex game of Go, someone will always say that it’s not true AI. This trend is summed up in Larry Tesler’s theorem that AI is “whatever hasn’t been done yet.”

The Paradoxical Definition of AI

I have no idea what AI is supposed to be, and I believe that this uncertainty stems from the blurred boundaries of its definition. The sorites paradox explains this well: Starting with one grain of wheat, how many more grains must be added before one has a heap? Similarly, there is no clear answer to the questions around what constitutes intelligence, when AI takes over from basic tech-based processing, or how close to human thought tech processing must get before it can be deemed even artificially intelligent. Some may propose specific demarcation; others immediately disagree.

What Matters Most Is That None of This Matters

We often start reports on technology with a definition to help the reader better understand the boundaries of what we write about. I have spent far too long trying to come up with a definition of AI with which I am comfortable. I can’t come up with a sensible boundary for what does and doesn’t constitute AI, and even if I did, more people will disagree than agree. But who cares? AI really means the latest and shiniest analytics product to hit the market.

What buyers should bear in mind is whether this technology does the job for which it was intended at a competitive price. Just make sure that anything painted in AI colors is going to make a decent ROI for your business.


OSIsoft and Partners Help Demonstrate the Power of Private LTE Networks

— May 29, 2018

Sempra Energy called on a consortium of vendors to help deliver a real-time wind farm monitoring system after suffering from expensive, unplanned turbine outages at its Broken Bow II wind farm in Custer County, Nebraska. Failure in pitch assemblies are one of the most common causes of wind turbine outages. The run-to-fail cost per turbine is well over $100,000—cranes are needed to dismantle the blade assembly and return it to the ground for repairs. On a windy day, assemblies cannot be returned to ground, so must remain idle until it is safe to do so. However, if Sempra could identify problems before they occur—such as the need to replace lubricant—a crew could carry out maintenance and repair work from inside the turbine at a fraction of the cost.

A private LTE network was built by Nokia to facilitate the collection of high speed data from sensors in the turbine towers and stream it back into OSIsoft’s PI data historian. The private LTE network was selected as few other options beyond point-to-point radio were previously available at the remote site. While microwave was option, an LTE chosen for its ability to scale to different use cases.

Beyond high speed data acquisition into PI, LTE can also be used for remote monitoring, site security, video streaming, and remote collaboration. Sempra Energy is keen to investigate new video use cases video for remote assistance and training of maintenance workers, and also to monitor and ensure that health and safety regulations are adhered to.

A Vendor Ecosystem Was Required to Deliver the Solution

SenseOps built the gateways that collect, store, and transmit high speed sensor data across the Nokia LTE network and into Sempra’s PI system. SenseOps’ challenge was to take an off-the-shelf gateway from Advantech and ruggedize it to operate in temperatures between -5°F to +100°F, installed 300 feet in the air in a wind turbine. An additional challenge was that, because of their location, the gateways must reset, reboot, and upgrade firmware remotely.

Nokia built the private LTE network across the wind farm, which then connects to a local internet service provider’s network. Nokia used Qualcomm’s technology to build the LTE network, which is so new that this example is a pre-commercial release. Qualcomm has been driving the development of private LTE on the 3.5 GHz band, which will be fully released later in 2018. OSIsoft’s PI data historian stores the data and makes it available for analysis.

Wind Farm-Based Internet of Things Deployment Has Potential for Growth

As with most Internet of Things deployments, the Broken Bow project began with a strong business case to develop a monitoring system that would save it millions of dollars over a 10-year period. However, there is further value that can be created by the technology. We have already discussed remote sensing and collaboration; however, more intuitive algorithms could be developed in the future by cross-referencing data from this wind farm with others, increasing the set of data from which the algorithm can learn, and creating detailed insights.


Swinging the Blockchain Hammer – Event Horizon 2018: Part 2

— May 8, 2018

In my second post following the Event Horizon 2018 event, I discuss blockchain startups’ business models. I see too many startups following the peer-to-peer energy trading model, rather than pursuing business models that address utilities’ current needs.

A Hammer without a Nail Is Just Scrap Metal

Innovation in the technology industry is unrivaled. “Necessity is the mother of invention” may be the credo for utilities’ innovation efforts, but the technology industry is different: it must stay ahead of the curve through endless cycles of R&D. The output of this research has given birth to many fantastic products that have revolutionized the way we live—no one really knew they needed an iPod or smartphone until the products were brought to market.

Conversely, many fantastic technologies—from Betamax VCRs to Google Glass—fall ignominiously by the wayside. They are the proverbial hammers that failed to find a nail. Despite the hype, right now blockchain is a hammer searching for a nail. The rash of startups developing energy-focused blockchain solutions are each hoping their solution will be the one that transforms the industry. However, there is likely not enough room for all of them.

Peer-to-Peer Energy Trading Is (Probably) Not the Right Nail

However, I have concerns that the business models pursued by many startups are not optimizing the pursuit of those elusive nails. After many conversations with people across the industry and listening to several startup pitches, I am worried that too much investment is flowing into the wrong business models. Right now, the majority of energy-focused blockchain startups include some element of peer-to-peer energy trading. While I remain positive about the future of transactive energy markets, there are still significant barriers to adoption.

So why are there so many TE-related startups? The cynic in me believes that too many of the people behind these startups are following the money and copying the business models of previously successful initial coin offerings (ICOs). Rather than spending time with a utility to identify existing business needs, it’s far easier to raise a few million dollars by launching an ICO with a white paper that promises Uber-style industry disruption and bitcoin-like token inflation.

ICO Follow-the-Leader Will Consign Many Startups to the Scrapheap

There is a saying often, but incorrectly, attributed to Einstein: “The definition of insanity is doing the same thing over and over again, but expecting different results.” Unfortunately, blockchain startups seem to value short-term ICO success, not the value of their business models: too many are recycling the business models of their peers to raise seed capital rather than identifying the business pain points that blockchain can address. This is not just unoriginal; it is fraught with danger.

Potential new entrants will do well to note the following observations before deciding on their business models:

  • We do not need any more peer-to-peer blockchain startups. More than enough currently exist for the world’s transactive energy requirements.
  • A peer-to-peer trading platform does not address any current, pressing issue with utility industry business processes.
  • The energy industry is not the taxi industry. Regulators will not permit Uber-style disruption. No startup can simply release an app and instantly sideline industry incumbents.
  • Regulatory approval for peer-to-peer energy trading could easily take longer than it takes a startup to spend all its seed capital.
  • No peer-to-peer energy trading model will create cryptocurrency billionaires. Startups should think twice about using any form of token. I am unconvinced regulators will permit the energy system to be priced in anything other than fiat currency.
  • True peer-to-peer energy trading is a physical impossibility; some form of centralized control and pricing must be done to maintain electricity networks.

A Robust Analytics Infrastructure Is Vital for Future EV Business Models

— May 1, 2018

The electricity industry is waking up to the prospect of large-scale deployments of EVs. And well it might, as all signs point to a future where EVs are increasingly common. Annual demand from EVs for electricity could exceed 400 TWh by 2035, creating the largest opportunity for new load growth in a generation. However, EVs will also pose significant problems to network utilities, particularly in areas where grids are already constrained. The future EV market presents many opportunities and sizeable threats to the utilities industry.

EVs Present Unique Opportunities and Challenges

EV integration is a complex and unique issue. When EVs charge, they are loads; when idle, they are storage; and when dispatching back into a network, they are sources of supply. They also move around, so utilities will never have full visibility of their location.

EV fleets and buses present different opportunities than individually owned cars. There is a complex and competitive ecosystem of stakeholders, some of which will be in direct competition with incumbent energy suppliers—there is little room for monopoly market thinking, even for vertically integrated utilities. The customer base is also diverse, with different needs and requirements. Complexity is only one issue. The future pace of change is arguably a tougher nut to crack. Utilities must prepare themselves for a dynamic and open future where change and uncertainty are the only constants.

Treat EVs Like Any Other IoT Deployment

A new Navigant white paper, Charging Ahead with EV Analytics, assesses the future EV market and details the many opportunities and sizeable threats they create. Its primary focus is on the data and analytics requirements of an EV infrastructure. Most if not all EV opportunities rely heavily on data and analytics. Likewise, analytics will also help mitigate many risks. While many utilities are excited about the opportunities presented by EVs, few have made significant investments in the data and analytics architecture that will support the diverse and rapidly changing processes that future EV business models demand.

One of the paper’s central messages is that the electrification of transport is in practice a digitization project. EVs and charging infrastructure are essentially IoT deployments. Consequently, EV business models rely heavily on IoT devices and an associated data and analytics platform. While a handful of utilities are actively planning their future IT infrastructure to support EV integration, many more recognize the EV opportunity but have not yet built solid strategies.

Now Is the Best Time to Start EV Planning

The business process requirements of EVs will change over the coming decades. It is a futile exercise to design and build an entire infrastructure around projected future requirements. Instead, a flexible approach to architecture will enable a utility to adapt to these changing requirements. A detailed roadmap that identifies specific inflection points in EV adoption will act as a signal for when to add or remove functionality.

2025 is often cited as the year EVs step into the mainstream. While this may seem a long way off, it is far better to plan now when EVs are not an operational problem than when they are. There is good reason to act now. There are strong arguments for EV adoption to follow an S curve. The further into the future, the faster the rate of adoption. Planning for EVs while there is time to spare can help avoid having to rush critical decisions once time is scarce.


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