Success in the Wild: Kimberly “KG” Gress of Energage, C2O Advisors

“I come from a cobbler who worked until he was 93”

Kimberly starts her story not with her title, but with her grandfather, Eugene Fiorino.

He came to the U.S. from Italy in 1921, in the hull of a ship, convinced the streets would be “paved in gold.” When they weren’t, he was too proud to go back - so he stayed, opened a cobbler shop in Philadelphia, and worked there until he was 93.

From him, she absorbed a few things that now quietly power everything she does as a CS leader:

Integrity - Do what you say you’ll do, even when nobody’s looking.
Hard work - Not performative hustle; the kind that shows up consistently.‍
Kindness and giving back - She sees mentoring as her main way of “giving back to the world.”

On paper, Kimberly is a comp sci and math grad who survived automata theory and studied “AI” back in the early ’80s. In practice, she’s someone who:

Works hard, plays hard
Is fully committed to family Fridays (martinis, pizza, wings, 5:30 sharp)
Loves wine, game nights, cornhole, and the Outer Banks

That mix of nerdy, grounded, and human flows straight into how she leads CS teams.

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A “terrible programmer” turned leader - and an early data-driven CS pioneer

KG jokes that she was “promoted into management because I was a terrible programmer.” But that move turned into a career-defining path:

Running a P&L at SCT
- She owned the full motion: marketing, sales, dev, support, implementation.
- That view across the business gave her a deep understanding of how everything connects - and where CS really fits.

Joining Boomi in the early days of Customer Success
- She arrived when Boomi was ~25 people, selling integration-as-a-service.
- Subscriptions were perishable, and the question became:
  
  “How do we keep customers instead of constantly re-selling to them?”

At Boomi, Kimberly and the team:

Instrumented data early - They didn’t just track usage; they tracked the right signals.
Built an at-risk customer model - A process (patented by Kimberly and two fellow colleagues) that automatically flagged at-risk accounts and opened cases for the CS team to act on.
Treated data as more than a report:

“Data is not just a dashboard. It becomes your CS operating system.”

Takeaway for CSMs & CS leaders

Ask yourself:

Do we treat data as reports we look at once a month - or as the engine that tells us where to focus, where to invest, and where to divest?
If I disappeared tomorrow, would someone else know exactly how to use our data to run CS? If not, I have an operating system gap, not a dashboard gap.
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When your Boomi playbook doesn’t fit your new company

When Kimberly joined Energage, she tried to copy-paste the Boomi model. It didn’t work.

Boomi:

Large enterprise customers
Slower, complex sales cycles
Heavier, high-touch motion

Energage:

High-velocity, highly transactional, SMB and mid-market customers
A smaller segment of enterprise accounts
Very fast motion with limited human bandwidth

Her first big realization:

“I had to change how I think. I couldn’t just force an enterprise model onto a high-velocity business.”

So she let the data tell her how to think:

What segments actually exist?
What profiles need high touch vs. digital touch?
Where can we give small customers a great experience without blowing up the cost to serve?

Then Energage acquired another company with more enterprise-style customers. Now she’s managing two very different motions at once:

High-velocity, transactional customers
Slower-velocity, enterprise talent management customers

That creates complexity across:

Segmentation
Swimlanes and handoffs (Sales → Implementation → CS)
Role clarity and goal clarity

And yes, a lot of fear:

“The model’s ready to go, we’re launching Jan 1… and everyone’s afraid we missed something.”

KG’s answer:

Launch it.
Watch the data.
Review at the end of Q1.
Adjust.

Hustle + data + iteration, not perfection.

Takeaway for CS during/after an acquisition

If you’ve just acquired a product (and customers), ask:

Do our segments still make sense, or are we just bolting new customers into old buckets?
Are role & goal clarity written down for each stage (Sales, Implementation, CS, Support)?
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Do we have a Q1 experiment mindset, or are we stuck in “but what if it breaks?” mode?

**AI, automation, and what she actually wants from tools**

KG is clear-eyed about AI: she’s excited about it, but she’s not romantic about it.

Where Energage is using AI today

Right now, AI is actively helping in Support:

They use a co-pilot that sits alongside agents.
When a ticket comes in, the co-pilot surfaces:
- “We’ve seen this before.”
- “Here’s the likely response.”
Impact: It doesn’t reduce the number of cases, but it reduces time per case.

They’re testing it with humans in the loop first. If it continues to perform, they’ll consider automating direct responses to customers.

On the Customer Success side, AI tools aren’t fully in place yet - but Kimberly is very clear on what she wants:

Efficiency gains for CSMs

“The CSM who uses AI will perform better than the CSM who doesn’t.”

She imagines tools that help CSMs:
- Know when to reach out (and when not to).
- Accelerate prep and follow-up.
- Reduce manual, repetitive work.
Intelligent expansion signals
- Today, her team has some data correlations that indicate expansion readiness.
- What she wants next:
  
  AI that looks across and down the data to serve up expansion opportunities for specific segments.
Anticipatory support & anticipatory expansion
- On platform: tools that know what the customer’s “next three questions” will be and guide them proactively.
- In CS: tools that recognize, for example, “300 customers could improve employee appreciation scores” and automatically reach out with how your platform can help.

And she’s very honest about the AI vs. automation confusion we all feel:

“I always get AI and automation intermingled… I want AI to tell me where the expansion opportunities are. I can automate the simple tasks.”

Takeaway: a practical AI roadmap for CS

If you’re trying to bring AI into CS, KG’s path maps nicely into three phases:

Co-pilot first
- Start with human-in-the-loop assistance (for support or CSMs).
- Measure time saved, response quality, and customer feedback.
Automate the simple tasks
- Templated follow-ups
- Routine updates
- Low-risk “how do I do X?” support questions
Graduate to anticipatory signals
- Feed product + outcome data into models.
- Ask specifically: “Where are my best expansion opportunities?”
- Let AI surface segments and accounts, then keep humans in charge of the motion.

Customer Success is not “just account management”

KG is unapologetic about the value of true Customer Success.

She agrees that:

Customer Success = great customer experience + great customer outcomes‍
That equation touches every function (product, sales, marketing, support).

But she also pushes back hard on:

“Everyone can do customer success.”

In her world:

Not every CSM performs well.
The best CSMs are outcome-obsessed and adoption-driven, not just renewal-driven.
“Account management” is not the same as driving adoption and value realization.

She doesn’t have a perfect new title for the function (though “Customer Journey Ninja” made a cameo), but she’s certain of its core:

Make sure customers adopt the product
Make sure they hit their outcomes‍
Then earn the renewal and expansion

Everything else is decoration.

Mentoring, role clarity, and why “growing people” comes first

When KG describes herself as a “business human being,” she lists three things in order:

Growing people - Her greatest joy is mentoring and seeing others go on to big roles.
Role + goal clarity driven by data - Everyone knows what they own and how to measure it.
Customer Success - The craft that sits on top of 1 and 2.

She still mentors people both inside Energage and outside the company. She sees it as her way of giving back, just as much as any formal “social impact” project.

What this means for CS leaders

If you lead a CS org, ask yourself:

Do I treat mentoring and development as “nice to have” or as part of my job description?
Does every CSM know:
- Exactly what success looks like in their role?
- What metrics they’re responsible for?

When I review performance, am I mostly reacting to numbers - or actively using data to grow people?

“There’s no balance. There’s juggling and prioritizing.”

CS is not a 9-5 job, and Kimberly doesn’t pretend otherwise.

She doesn’t sell a fantasy of effortless balance. She calls it what it is:

“There’s no balancing. There’s only juggling and prioritizing.”

How she makes it work:

A genuinely 50/50 partnership at home (parenting + household).
Radical organization and prioritization.
Family always comes first:
- Her kids don’t remember a time she missed a game - even if it meant heating up dinner at Boomi and eating it in the stands.

Her two sons? One’s a data engineer, the other’s a data analyst doing a master’s in data science and applied statistics. The data gene runs deep.

Game nights, wine, and being fully present

Outside of work, Kimberly is geeking out on game nights:

She loves discovering new games that bring out everyone’s competitive side.
Current obsession: “Hues and Cues”, which gets “pretty vicious” with eight competitive people around a table.
Pair that with a good glass of Tempranillo or wine from Italy’s Umbria region, and she’s happy.

It’s a small detail, but important: the same person leading complex, AI-curious, data-driven CS motions is also someone who unapologetically loves Friday martinis, wings, and merciless game nights.

That kind of wholeness is exactly what makes people want to work with her, not just for her.

How to bring KG’s playbook into your own CS world

Here are a few prompts you can use with your team (or yourself) this week:

1. Turn your data into an operating system

What are the 5-10 signals we truly act on to:
- Flag risk
- Identify expansion
- Decide where to invest / divest
Are those indicators wired into workflows, or do they just live in dashboards?

2. Rethink segmentation after change (like an acquisition)

Do your current segments match how customers actually buy and use your product today?
Where can you introduce a digital CS motion for stable, low-touch customers?

3. Start small with AI

Where can a co-pilot help today (support macros, QBR prep, follow-up summaries)?
What’s one workflow you can safely test with AI and measure:
- Time saved
- Customer satisfaction
- Error rates

4. Invest in role + goal clarity

For each CS role, can you finish this sentence in one line?

“This role exists to ________, and we measure that by ________.”

5. Make mentoring part of the job, not a side quest

Who are the 2-3 people you could intentionally grow over the next year?
What would “success” look like for them in 12-18 months?

If you’re a CSM, CS leader, or aspiring “Customer Journey Ninja,” Kimberly’s story is a solid reminder that great Customer Success isn’t magic.

It’s:

A clear sense of who you are
An operating system built on data, not vibes
A willingness to launch, learn, and adjust
Real care for people - customers and your own team

And maybe a standing Friday night martini.

Success in the Wild:

Kimberly “KG” Gress of Energage, C2O Advisors