Confidence Drift, and What Stops It
I recently came across a misalignment in the public materials of a vendor that addresses wildfire risk.
Content audits typically note outputs and target audiences. A case study here; a technical brief there; something for the CFO; and something for the engineer. The assessment that's sometimes skipped is whether content messaging aligns.
I recently came across a misalignment in the public materials of a vendor that addresses wildfire risk. Their technical white paper, credited to an in-house PhD, included citations to peer-reviewed science and made a genuinely sophisticated argument.
It said the standard fuel-models fire agencies rely on are meant for planning across wide geographic regions, not for the narrow corridors utilities maintain. It went on to explain that utilities need a different model than the one derived from agency-standard science.
The company's product page, just one click away, was misaligned. It said the vendor's technology was "grounded in the same science used by fire agencies."
It's easy to understand how a copywriting mistake like this could happen. The person drafting the product page knew the solution was backed by legitimate expertise in fire science. But they didn't understand the specifics. So they wrote a statement of credibility that was broadly true, but directionally inaccurate.
An intelligent reader could connect those dots easily enough, but the same underlying failure — confidence drift — could just as easily have produced disruptive confusion.
Picture a white paper that carefully qualifies its claim, saying the model flags corridors for prioritized inspection rather than confirmed risk. If a product page dropped that qualifier and said the platform identifies which corridors are at risk, a technical evaluator who read both might have carried an inflated sense of the model's certainty into a due diligence conversation the vendor couldn't actually support.
This is why a comprehensive messaging framework should exist before any commercial copy does. It's the document that captures what a company's messages and claims are, and how they should and should not be presented. It's what keeps different writers, marketers and salespeople on the same page.
In practice, early-stage companies rarely start with a messaging framework. Brochures and web copy get produced as they're needed, written by whoever's available, until the company reaches a point where someone is finally accountable for coherence across all of it. Often that is the first task of the vendor's first marketing leader.
But mature companies need to revisit their messaging frameworks regularly, too, since a new product version or service changes the company's value proposition and credibility claims. Older content then needs to be audited against the updated messaging, before enough of it accumulates that nobody can say for certain which claims still agree with each other.
WORTH READING
“Zoning's Next Century”
by multiple contributors in The Urbanist, April 2017
This cover article on city planning explains how, over the course of 125 years, San Francisco’s building code ballooned to more than 2,000 pages, as new rules were layered on top of old ones. At the time of writing, the code apparently contained sections that actively worked against each other, one favoring density while another mandated parking, for example.
It's the same failure driving this issue's essay: commercial communication accrues one addition at a time until nobody can say which claims still agree with each other. The article's answer at city scale, a clear hierarchy of policy objectives, is the same fix a messaging framework provides at company scale, a standing reference that settles what takes precedence before the next line of copy gets written.
NEW GROWTH SPOTLIGHT
This month’s spotlight is a new self-assessment for utility vendors. Following 12 simple questions, vendors receive a scorecard measuring the maturity of their commercial communication systems across seven dimensions:
Messaging & positioning
Trust & credibility
Sales enablement
Buyer alignment
Content operations
Measurement & impact
Repurposing & distribution
The three-minute assessment also identifies the areas where improvements could have the biggest impact on the business.
Take your free Trust-Gap Snapshot.
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What a Lead Score Can’t Know About Readiness
It’s important for the sales team to understand the unique limits of marketing qualification in the long utility sales cycle.
Lead scoring exists because, ideally, a utility vendor’s marketing team will generate enough inbound activity that leads need to be sorted and prioritized before they’re given to the sales team.
But a typical problem is that even with a scoring system in place, the leads aren’t ready for a sales conversation. When a sales rep finally gets them on the phone, he or she quickly learns the prospects are nowhere near a purchase decision.
The sales team may complain that marketing is wasting their time, and they might stop working marketing’s leads altogether, breaking the operational link between the two groups.
To address the problem, the marketing team may adjust the scoring system by reassigning point values or raising the threshold. But that can only do so much, because the real issue is that commercial readiness can’t be fully surfaced via an inbound marketing funnel and scoring system.
It’s important for the sales team to understand the unique limits of marketing qualification in the long utility sales cycle. They also need to trust that marketing has done its best to optimize their half of qualifying and to understand the necessity of a sales call to complete the other half.
So, let’s break down the five conditions that typically reveal utilities are commercially ready for a sales conversation:
Problem recognition – The utility recognizes it has a problem the vendor solves.
Active exploration – The utility is actively exploring solutions to the problem.
Fit – The person engaging with the vendor holds a role relevant to the decision, and the utility itself matches the vendor’s target, type, size, and systems.
Urgency – There’s a budget or timeline behind the utility’s interest.
Internal Trust – The person engaging has enough standing within the utility to bring the solution forward to the organization.
With a well-designed content funnel and scoring system, marketing can build genuine evidence toward the first two conditions from behavior alone.
Fit works differently. Each lead needs to be checked against information the vendor has developed as part of an account-based marketing strategy or other researched validation.
The final two conditions, urgency and internal trust, unfortunately don’t submit to either kind of check. There's no page visit that reveals a funded budget, and no download that reveals whether a person carries weight with their own colleagues.
Those two conditions live inside the utility, not in anything visible to a vendor's systems. That's what makes the sales call necessary rather than optional.
If problem recognition, exploration, and fit were genuinely confirmed beforehand, a negative answer on urgency or internal trust shouldn’t be viewed as a marketing failure or a reason to adjust the funnel or scoring system. It’s the completion of the qualification process and the step that sales is responsible for by design.
WORTH READING
"Why Can't A.I. Manage My E-Mails?"
By Cal Newport in The New Yorker, November 2025
Newport tests how useful AI can be in his own inbox, and finds a clean boundary. Automated filtering handles spam, routine messages, and simple replies well, but stalls on anything whose correct handling depends on the unstated context of relationships, priorities, or history the sender never wrote down. It’s worth reading for the shape of that boundary alone. It isn't a data gap, but a tacit-knowledge problem.
The boundary is similar to the one in the utility-vendor funnel. A system can sort the observable layers cleanly, but the handoff to sales happens at the point where only a person holds what's needed next.
NGC SPOTLIGHT
This playbook is an example of funnel content developed for Util-Assist to reveal problem recognition and/or active exploration.
With a clear title — AMI 1.0 to 2.0 Transition: A 5-Pillar Playbook — it will draw the interest of utilities considering such a transition, so a page visit alone indicates a level of problem recognition. Plus, because the playbook is designed in html as a page on the Util-Assist website, the marketing team can potentially evaluate how long a lead spent on the page and which pillars they clicked on, such as the one describing “Parallel Validation Strategy.”
Time spent reviewing the specific pillar content is a strong indication that someone is actively exploring Util-Assist’s methodology, and the gated checklist download gives further proof, while also ensuring the lead’s contact information and affiliation is captured.
Check out the live asset here.
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The Unsexy Proof About AI
Vendors would do well to move on from broad promises and develop messaging that is outcome-first, narrow, and utility-native.
AI has been THE business story over the last two years, so it’s understandable utility vendors with AI capabilities have hyped them.
But the utility industry isn’t like other business sectors — especially the move-fast-and-break-things tech sector. So, it’s important to understand how AI messaging might (or might not) be landing with utility decision-makers.
In rate and safety filings, for instance, utilities themselves use very specific vocabulary to describe how they aim to deploy AI in the near term. They talk about machine learning, predictive modeling, risk-model updates, spend efficiency, and data quality.
Duke Energy’s CIO, Richard Donaldson, reflected a similar reserve in a September 2025 podcast interview. He said utilities have to do “the unsexy work” first, protecting data and governing AI platforms. That’s the opposite of hype language, and it matches the practical pattern seen in narrow production deployments to date.
According to EPRI, the Open Power AI Consortium has cataloged more than 250 AI use cases across the power sector, and others are still being prioritized, co-developed, and matched to technology maturity. That seriousness of attention shows the industry has matured beyond dreaming about transformation to looking for proof.
The proof points already exist at the category level to set the right expectations. Avista reduced high-bill service calls by 27% after deploying AMI-driven customer analytics. PG&E's internal service-planning assistant saved more than 1,400 hours and $200,000 in its first month. And Donaldson said Duke compressed regulatory-response workflows from days to seconds. That level of specificity is what utilities are now looking for.
Vendors would do well to move on from broad promises and develop messaging that is outcome-first, narrow, and utility-native.
A good way to calibrate marketing language for 2026 would be:
Use the term AI, but subordinate it to outcomes. “AI-enabled outage risk reduction” will land better than “AI transformation platform.”
Lead with measurable use cases that already have sector momentum. Wildfire and vegetation analytics, inspection QA/QC, high-bill and service-planning support, regulatory-response copilots, cyber monitoring, and forecasting are credible examples.
State the prerequisites instead of hiding them. Data quality, integration complexity, and human governance aren't weaknesses to minimize. Take the lead in addressing them directly.
Genuine momentum exists for AI in bounded customer, compliance, and knowledge-work workflows. Vendors who position it as a disciplined accelerator for work utilities already prioritize will find more traction than those still selling the transformation story.
WORTH READING
“The Year Capital Stopped Believing the Hype”
by Galen Growth, January 2026
In 2025, digital health investors also stopped rewarding speculative promise and started concentrating capital around companies that could demonstrate workflow relevance, clinical evidence, interoperability, and hard ROI.
Galen Growth's analysis of the shift is worth reading because the buying psychology it describes is almost exactly what utility procurement teams are applying to AI vendors right now. Health providers, like utilities, are complex, risk-sensitive buyers, and they reject unbounded innovation narratives in favor of proof that a product works within real operational constraints. Different sector, same moment.
NEW GROWTH SPOTLIGHT
This Hart EMC case study, developed for Itron, is worth reading as an example of what proof-oriented vendor content looks like.
Hart EMC is a northeast Georgia cooperative serving 40,000 meters across challenging terrain, and the piece describes exactly how Itron's analytics application uses what could be described as AI to help detect energy theft, maintain billing accuracy, and reduce the manual investigation burden on a lean operations team.
But the term "AI" doesn't appear anywhere in the document.
Instead, we describe how automated analytics are bounded and embedded in workflows that combine “machine and human intelligence” to “improve operational efficiency and customer service” with measurable KPIs.
The operational outcomes carry the story without need to reach for a flashy headline.
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It's a 'Supercycle'
This is the first issue of a newsletter providing commercial communication insight for utility vendors.
This is the first issue of a newsletter tracking good thought-leadership marketing among electric utility vendors.
IT’S A ‘SUPERCYCLE’
Schneider Electric’s EVP of Energy Management uses the term that’s been suggested by investment analysts over the last year. He says growing geopolitical uncertainty only adds to the other well-known drivers of electrification, all pointing to a multi-decade period of structurally elevated demand and investment — a supercycle.
This sustained disruption could provide the stimulus needed to re-build our energy systems quicker, for long term stability. ~Frederic Godemel
on Enlit (1,050 words)
Energy Crisis Is Resilience Catalyst
A STELLAR COMMENTARY
The CEO and founder of Epsilon3 published one of the best thought-leadership commentaries I’ve read this year. Her angle is clear, intriguing and right where it belongs: in the headline. She gives just enough context to orient the reader before identifying a problem and beginning to offer unique insight on the solution. Her point of view made me so curious about her company that I almost skipped ahead to the “About the Author.”
The lessons we learned the hard, often public way, may offer a roadmap for nuclear and fusion operators speeding towards their own industry’s inflection point. ~Laura Crabtree
on Power (1225 words)
Aerospace Offers an Unlikely Playbook for the Nuclear Energy Industry in 2026
DISPATCHABLE GEN + DATA CENTERS GET SOME <3
Enchanted Rock’s Chief Commercial Officer highlights new research out of Princeton University’s ZERO Lab showing strong economics for co-locating large data centers with dispatchable power generation. As a provider of dispatchable generation, Enchanted Rock didn’t need to think much about how to position this thought-leadership piece, which goes on to explain the relevance of FERC Order EL25-49 and what onsite generation developers can bring to the table.
“As data center developers prioritize time-to-power, onsite generation developers have emerged as critical execution partners….” ~Allan Schurr
Sponsored article on Utility Dive (875 words)
A New Role for Onsite Generation: Accelerating Grid Access for Large Loads Link
NGC SPOTLIGHT
New Growth Communications client, IkeGPS, published an eye-opening commentary about a growing risk to U.S. distribution networks. Aging poles, more attachments and a lack of structural data are at the heart of this asset management problem, according to their SME. Fortunately, better hardware and software for pole loading analysis (PLA) can help.
“What was once viewed as optional engineering due diligence is becoming standard practice across the industry.” ~Brett Willitt
on FactorThis (850 words)
The Billion-dollar Blind Spot: The Infrastructure Data Gap Plaguing Utilities
NEW TL ASSETS
AI is Electrification by Schneider Electric’s Research Institute
From Insight To Action: Harnessing AI to Safeguard Tomorrow's Energy Grids by GE Vernova
PRO TIP
“While ghostwriters do their best to write in the voices of their clients, the purpose of a first draft in thought leadership writing is primarily to capture and present the client’s ideas as accurately as possible. Then, as the client reviews the writing to make sure it correctly represents what they want to say, he or she will typically make revisions to better reflect how they want to say it as well.”
For more on this topic, read Can Ghostwriting Really Be Thought Leadership? By me, Bart King, principal consultant at New Growth Communications.
For assistance with your thought-leadership marketing, get in touch!
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