Hristina Acuna

Hot take to start: if your AI only works for most of your cases, you haven't proven it understands your business. You've proven the technology works. Those are not the same thing.

A LinkedIn thread this week sharpened this for me. AI scientist and Cohorte founder Charafeddine Mouzouni argued in a post that an AI agent can only join a team that knows how its own work gets done. Robert Heckel, CEO of Computer Visionaries AI, went further in the comments: documenting the process isn't enough; you have to capture why experienced people break it. Then, replying to my comment, he put it better than I could: the happy path proves the technology works. "The exceptions prove whether it understands the business."

He's right. And the data agrees with him.

Ready models, unready companies

In January 2025, Sam Altman wrote on his blog that 2025 might be the year the first AI agents "join the workforce." This August, Deloitte published a survey of 501 US leaders, from senior managers through the C-suite, conducted this spring. Only 5% said their business processes were highly prepared for AI agents. Only 15% had scaled agents across functions. Business processes ranked as the least-prepared area of all.

The detail that stopped me: most leaders Deloitte interviewed said they're layering agents on top of existing processes instead of redesigning them. That's the old way of thinking in a new outfit.

What I've seen, in private companies and federal agencies

I've watched the same pattern play out in both the private and federal sectors. One team finds a tool that solves its problem. Another department buys its own. Then another. Every implementation is a success on its own terms. Every team gets its dashboard, its workflow, its win.

But the systems don't talk to each other. The data lives in one place, the context in another. And the people who need the full picture most—executives and leadership—only ever see slices of it. They're asked to make organizational decisions from segmented, fragmented information. Nobody gets to look at the business with an organizational mind.

This isn't just my experience. GAO, the federal government's nonpartisan watchdog, has published a report on fragmented, overlapping, and duplicative federal programs every year since 2011. Its 2026 edition credits about $774 billion in financial benefits from fixing those issues so far, with more than $100 billion still on the table. And McKinsey's 2026 State of AI survey, which included 1,719 leaders across 97 countries, shows the same pattern playing out with AI. Eighty percent of respondents say AI has improved their individual productivity. Yet only 37% can point to any impact on their organization's bottom line, a share that hasn't moved since last year. The wins stay local. The enterprise doesn't feel them.

Here's the part that should worry all of us: we're repeating the old mistake with AI. The people are getting faster. The business isn't. Department-by-department AI is just department-by-department software with a faster engine. The roughly 6% of companies McKinsey counts as AI high performers do it differently: nearly three-quarters of them fundamentally redesign their workflows around AI, compared with about a quarter of everyone else.

Why the outliers are the whole point

In that thread, I said that focusing on outliers runs against our current culture of Time to First Value and MVP. I stand by it. We're so focused on getting it to work for most cases that we never teach it the cases that matter. Then we wonder why it can't go three levels deep.

The routine cases prove AI works. Any good demo can show you that. The outliers are where your business actually lives: the franchisee with the unusual agreement, the policy exception someone approved years ago, the rollout that never fit the playbook. Those cases carry the complicated design and policy decisions your most experienced people make without even noticing. Mouzouni described work like this as running on folklore. If the folklore never leaves people's heads, no agent, however capable, can learn your business.

That's where the human comes in. AI can now work through deep, time-consuming problems in seconds, the kind that used to mean weeks of pulling reports from five systems and stitching them together. But it can't decide which cases matter. A human has to point to the outliers because the outliers are what prove AI has learned the model and the business, not just the majority.

Think AI-forward, not AI-added

The old playbook: buy per team, implement per team, measure adoption per team, launch for the majority, and handle the edge cases "later." Later never comes.

An AI-forward approach flips it:

• Start with the enterprise question, the one leadership can't answer today, not a single team's pain point.
• Design around the exceptions first. Ask your most experienced people Robert's question: When do you break the process, and why?
• Connect before you automate. An agent that sees one department's data will give you one department's answer.
• Measure outcomes, not adoption. Logins weren't value before AI. They aren't value now.

Notably, 75% of the leaders in Deloitte's survey said they see more value in humans and agents working together than in agents on their own. The human isn't the bottleneck in this model. The human is the teacher.

AI won't fail on your easy cases. It will fail on the ones you never showed it. Implementation isn't an EVENT; it's an education.

So tell me: what's the one exception in your business that only your most experienced person knows how to handle, and has anyone ever written it down?

A dose of truth to start: being liked is not a strategy. Not for software companies, and not for us.

I work in customer success, the part of a software company that makes sure customers actually get value from what they bought. My corner of that world is franchising, so my customers run brands with locations in every sector you can imagine. And the first lesson I saw some of my colleagues learn the hard way was one I learned years ago while I worked in the federal space:

Customers can LOVE you and still leave.

They can love your onsite visits. They can love your energy on the kickoff call. They can love that you remember their kids' names and send the good holiday cards. But if the product doesn't move their business forward, they will leave. If you have not proven that the business outcomes are tied to the product you represent, they will leave. Politely. With a lovely goodbye email. It's business, not personal.

The leaky bucket nobody puts on the slide

The software industry loves to celebrate net revenue retention, the number that includes upsells and expansion. It looks fantastic on a board slide. But the honest number is gross revenue retention: how much revenue you actually KEPT from existing customers before any expansion makeup is applied.

That honest number is slipping. Benchmark data from Aleph and Benchmark it, covering full-year 2025 across 342 SaaS and AI-native companies, puts median gross revenue retention at 84%, down from 88% the year before.

Translation: plenty of customers who were "happy" last year are not customers this year. The relationships didn't save them. Results would have.

I see the exact same pattern in careers.

The career version of churn

Think about the feedback in your last review. "Everyone loves working with you." "You had a great year." "You're such a team player."

Lovely. Frame it. Now try using it in a promotion conversation.

This isn't just my hunch. Stanford researchers Shelley Correll and Caroline Simard analyzed performance reviews and shared their findings in Harvard Business Review in 2016. Women were systematically less likely to receive specific feedback tied to business outcomes. Developmental feedback was linked to outcomes 60% of the time for men and 40% of the time for women. And vague feedback was associated with lower performance ratings for women, but not for men.

So many of us are walking around with a performance review that reads like a relationship health score. Lots of warmth. Very little evidence. Yes, I know what you're thinking: "But Hristina, this was in 2016!" And sadly, this article is still being quoted everywhere because not much has changed. The performance feedback women receive is the career equivalent of a quarterly business review full of login charts: it tells a nice story, but nobody renews on it.

Hot take: you are not your activity

In my field, the classic trap is confusing activity with value. Logins aren't value. Tickets closed aren't value. Check-in calls aren't value. They're activity. Value is whether the customer opened more locations, whether their franchisees performed better, and whether they saved time and made money.

Careers fall into the same trap. Meetings attended. Emails answered within four minutes. Being the person who always says yes to the extra project. All activity. And if your manager can't connect your work to a business outcome, neither can the person deciding your promotion.

We have a saying I live by: if you can't connect your product to the customer's P&L, you're not doing customer success; you're doing customer service with a calendar. Swap "product" for "work," and it applies to every one of us.

AI is about to make this painfully obvious

Here's why this matters more now than it did five years ago. AI is eating the admin: the meeting notes, the follow-up emails, the "just circling back" reminders. I've been close to this at my own company, leading decisions on which AI tools we adopt and working on an AI agent for our customer team. What I've learned is simple. AI won't replace the people who drive outcomes. It will replace the parts of the job that were never the real job.

The research says people aren't going anywhere, but the job is changing. Gartner predicts that by 2027, half of the organizations that expected to significantly cut their customer service workforce because of AI will abandon those plans. It also predicts that no Fortune 500 company will fully replace its human customer service staff with AI by 2028. Gartner's analysts describe the human role that remains as one focused on complex problem-solving and relationship building.

Read that carefully. The relationship part stays. But it now comes attached to judgment, exceptions, and the hard problems. Being pleasant to work with is the floor. It's no longer the ceiling.

If your value was mostly being reliably busy, AI is coming for that. If your value is judgment, outcomes, and knowing what the customer really needs, you have never been more needed.

What I'd tell any ambitious woman reading this

My career has not been a straight line. I've reinvented myself at every stage, and "move and learn" is genuinely how I operate. I've worked in gaming, F&B, franchising, IT, and the federal and private sectors. Every reinvention taught me something that remains valid for my customers today: nobody stays for the vibes. They stay for the results.

So here's my unsolicited advice, one practitioner to another:

1. Translate vague praise into outcomes yourself. When your manager says, "Great year," smile, then ask, "Which result mattered most to the business, and what should I own next?"
2. Write your value story before review season. Not a list of tasks. A list of changes: what was different in the business because you were there.
3. Hand the busywork to the robots. Use AI for the admin and spend the time you win back on problems only you can solve. More bandwidth means nothing if we fill it with more check-in calls.
4. Keep being warm. Seriously. Warmth opens doors. Just don't mistake it for the reason you get to stay in the room.

The renewal is decided long before the renewal call

In customer success, we say renewal is a RESULT, not an event. It's decided in onboarding, in the first wins, and in every moment you show a customer what changed because of you.

Your promotion works the same way. It isn't decided in the review meeting. It's decided in all the months before, every time you pointed to an outcome instead of an effort.

Be liked. Absolutely. But be undeniable.