In the past two years, CTG has been building a research stream and practice to help corporate and investor clients gear up for new, ever-faster waves of disruption coming from the software world – our At the Cutting Edge research. In the blockchain space, for instance, we have been helping corporates understand relevant use cases and connecting them with the right innovators. A few weeks ago, we held a private executive summit on Blockchain in Energy & Industry in Boston, where we discussed value creation beyond the hype (more on that in my next post). In AI, we have been mapping how machine learning and autonomy are bringing value across the energy value chain, and developed analysis on how to differentiate innovators.
While each technology is different, and brings new challenges to the industry, we are finding more and more similarities in how we approach these different topics, and how we’re asked to help. Below are some early thoughts taken from recent presentations and conversations at Utility Week, Ecosummit London, and our own executive summit on the topics of blockchain and AI.
A consistent method to approach new topics with a cool head
In your daily industry readings, you probably come across titles like “how blockchain will revolutionize energy,” or “AI is the future of all things industrial.” These articles typically touch on a couple of pilot projects you have already read about, and draw a straight line between these pilots and a futuristic world where this technology has solved everything.
The frequency of these FOMO-inducing pieces is useful to track, if only to place a topic on the hype cycle. But when it comes to understanding how a technology can impact our industry, and when, we prefer to rely on a combination of public and private signals.
We typically start by collecting as much publicly-available information as possible: listing the start-ups in the space, the investors, the deals and partnerships, consortia and pilots, the M&A events. We then derive trends from this data set, and make assumptions on the state of the market.
In a second movement, we test our hypotheses by talking to the key players in the ecosystem – top start-ups, investors and large corporates. We organize roundtables, sit in private meetings, interview the best innovators – generally trying to get a sense of drivers and barriers behind adoption.
This tends to yield soberer results than broad articles, but is very effective at pinpointing both promising use cases and frustrations (typically of the lack of differentiation or commercial impact). For instance, at our blockchain summit, an executive from the most forward-looking utility in the blockchain space shared that, “While we have been at the forefront of experimentations (investing in blockchain start-ups, consortia, building ventures), and those experimentations have worked technically, we are still in search of compelling business models to provide a return on that investment.”
Such grounded comments from market insiders helps us approach new technologies with a cool head (and avoid FOMO).
Mapping use cases along the energy value chain
We find that one of the best ways to map use cases and areas of value creation is to plot current experiments and pilots along a simple energy value chain.
For instance, we produced the following on AI:
In more complex versions, we cluster comparable start-ups along the value chain and identify hotspots of innovation and competition. We are then able to question specific start-ups on differentiation. For instance, predictive maintenance is a field in which numerous companies are proposing similar solutions, and differentiation seems to crystalize around expertise in specific verticals.
Note that PowerScout co-founder Kumar Dhuvur and Awesense CEO Mischa Steiner will both be presenting at Cleantech Forum San Francisco on January 22-24, during a dedicated session we are putting together on AI in the energy field.
Recommendations for external innovation
While AI and blockchain are topics of different maturity, applicability and disruption horizon, some reflections on how to engage external innovators apply to both. Here are a few of our top recommendations, coming from both ourselves and some of the most experienced people we’ve talked to in these fields. Most of it is common sense, of course.
- Be ready to invest in the journey: No one knows to what extent AI or blockchain will disrupt energy or industry. But investing in experiments and taking the time and resources to build internal expertise/opinion will allow you to shape the answer, and expose your company to new cultures and ways of doing things in the meantime.
- Don’t dismiss incremental value creation: It’s tempting to get started now on the end-goal of transactive energy. But we’ve seen some very interesting use cases building significant value in predictive maintenance or wholesale trading, for instance. These use cases will help the market grow with proof points.
- Be outcome-focused and technology-agnostic: AI is not needed everywhere, and blockchain even less so. There are very particular cases when they become valuable. Start with a desired outcome in mind, instead of a pre-conceived notion of what tech to use.
- Put a premium on vertical expertise: This is especially true in the case of AI, where differentiation between different predictive maintenance companies is mostly a team’s ability to understand their buyers’ challenges and solve them.
- Don’t trust black boxes: Blockchain technology has brought with it a culture of openness and collaboration. If a team cannot explain what an algorithm or a distributed ledger tech does in simple English, or if they hide behind confidentiality, it’s probably not a great sign.
In our next post, we will review some of the learnings from our executive summit on Blockchain in Energy & Industry that took place in early November in Boston. If you have any questions, feedback or wish to hear more, don’t hesitate to reach out to firstname.lastname@example.org