A happy 2026 to everyone! What better time to talk about something that I’ve been rolling around in my mind for a while. A change that I believe will shape this year to come, and perhaps a few more after that.
So, let’s begin our tale…
Not long ago, “alignment” meant a recurring meeting (that no one wants to attend) and a shared slide deck (that no one wants to create).
But I’m here to tell you those days are done: Real alignment is now a workflow.
A lead enters the system. Enrichment fires. Routing happens. Sequences trigger. A rep gets a task. An AE gets context. Ops gets an exception alert. Leadership sees it show up in pipeline reporting, and nobody has to copy/paste anything into a spreadsheet.
That’s not a “marketing thing” or a “sales thing” or a “tech thing.”
It’s a systems thing.
And that’s why a consolidation of skillsets is happening right in front of us: Traditional corporate silos are getting stitched together by automation and AI, meaning:
- Revenue people need operational muscle, and
- Tech people need line-of-sight to business outcomes.
AI didn’t kill the silos. It exposed them.
Here’s the uncomfortable truth: AI doesn’t magically fix bad operations. It scales whatever you already have.
If your process is shit, AI makes it a big pile of shit.
If your lifecycle stages are political compromises, AI scales departmental disagreement.
If your CRM data is a “best effort,” AI scales hallucinations… with confidence.
Even the big research firms are basically waving a flag that says: the hype is outpacing the operating model. Gartner has predicted that over 40% of agentic AI projects will be canceled by the end of 2027 due to escalating costs, unclear business value, and inadequate risk controls. Gartner
That’s not an “AI is bad” message. It’s a discipline matters message.
The winners won’t be the teams with the most tools. They’ll be the teams with the cleanest systems and the clearest definitions.
The new baseline: “Ops literacy” for revenue teams
A lot of people hear “ops” and think “the folks who build reports and fix Salesforce fields.”
That’s not what’s happening anymore.
Gartner is explicitly calling out “Augmented RevOps” as a GenAI use case. The idea is GenAI can help the teams that manage data, design automations, and administer technology. Gartner
Translation: RevOps isn’t a back-office function. It’s becoming the control room.
Which means more people in revenue roles need a working foundation in operational thinking. Not so they can become admins, but so they can contribute to outcomes that increasingly depend on systems.
Ops literacy for revenue people looks like:
- You can explain your lifecycle stages end-to-end (and what “done” means at each step).
- You understand why fields, objects, and definitions matter (and why “picklist chaos” isn’t a small problem).
- You can describe a workflow with clear entry criteria, exit criteria, and exceptions.
- You can spot when an “automation request” is really a process disagreement.
- You can QA data, not just use it.
This is also why AI adoption stats often come with an asterisk: Adoption is easy; outcomes are hard.
Salesforce reported that 40% of sales organizations were experimenting with AI and 41% said it was fully implemented (as of mid-2024). Salesforce And in its State of Sales research, Salesforce highlights that AI going mainstream puts a spotlight on trustworthy data, because AI needs clean data to be effective. Salesforce
In other words: the “AI era” is really the data + process era. AI just made it impossible to ignore.
The matching baseline: “Outcome literacy” for tech teams
On the other side of the org chart, more tech roles are being pulled into revenue outcomes.
Because once marketing and sales motions become workflow-driven, whether they asked for it or not, tech becomes part of go-to-market execution:
- Data architecture shapes attribution and forecasting.
- Integration decisions shape speed-to-lead and handoff quality.
- Security and governance shape what can be automated safely.
- Platform reliability shapes rep productivity and customer experience.
The tech people who thrive in this environment aren’t the ones who know the most frameworks. They’re the ones who can translate their work into business leverage.
Outcome literacy for tech people looks like:
- You can map your work to funnel metrics (conversion, cycle time, CAC payback, retention).
- You can prioritize by business impact, not just technical elegance.
- You understand the “system” of revenue (constraints, drop-off points, handoffs, feedback loops).
- You can ask, “What decision will this enable?” before you ask, “What should we build?”
And, yes, this is a shift in identity for a lot of teams. But it’s also how tech stops being a “cost center” and becomes a measurable “growth engine”.
Why now: Automation made the interfaces executable
For years, we managed the seams between teams with meetings and good intentions.
Automation makes those seams real:
- routing rules
- scoring logic
- enrichment vendors
- SLA timers
- sequence triggers
- lifecycle stage transitions
- forecasting models
- exception handling
Those interfaces used to be social. Now they’re operational. And once the interface is operational, everyone needs shared language.
This is the core reason skillsets are converging: when the work is cross-functional, the competence has to be cross-functional too.
The new archetype: The “Revenue Engineer”
Whether you call it Revenue Engineer, GTM Operator, RevOps-minded seller, technical marketer, the pattern is the same:
T-shaped people are becoming the force multipliers. Depth in one discipline, enough range across adjacent ones to deliver outcomes.
They can collaborate across systems because they understand:
- how revenue is produced (outcomes)
- how revenue is operationalized (process)
- how revenue is measured (data)
- how revenue is accelerated (automation + AI)
This is also why the “productivity upside” of GenAI is real, and frequently misunderstood.
McKinsey estimates GenAI could drive roughly 3–5% sales and 5–15% marketing productivity uplift. McKinsey & Company
But those gains don’t materialize because you “turned on AI.”
They materialize when you:
- pick the right workflows,
- define the process tightly,
- feed the system trustworthy data, and
- instrument outcomes so you can iterate.
That’s not a tool problem. That’s an operating model problem.
What to do Monday morning (a practical playbook)
If you’re leading a GTM team, a RevOps function, or a technical team supporting revenue, here are moves that create compounding returns.
1) Build a shared revenue dictionary
Define (in writing):
- lifecycle stages and exit criteria
- what qualifies a lead / meeting / opportunity
- what “handoff complete” means
- SLAs and exception rules
If definitions aren’t shared, automation becomes a political battlefield.
2) Map one workflow end-to-end, and commit to deploying it
(Shameless plug: let us do it for you)
Pick a workflow with visible business impact:
- inbound lead → meeting booked
- trial start → activation
- closed-won → onboarding kickoff
- renewal risk → save motion
Map every step, owner, data dependency, and failure mode. Then ship improvements in two-week increments.
3) Measure workflow health, not just output
Most teams measure outcomes (pipeline, revenue) but ignore workflow health:
- time-to-first-touch
- lead fallout rates
- handoff delay
- “rework” volume (records that bounce between stages)
- exception frequency
Workflow health is where the leverage is.
4) Treat RevOps like product management for revenue
RevOps shouldn’t be a ticket queue. It should be a product function with:
- a roadmap
- prioritization criteria
- release notes
- stakeholder enablement
- governance and QA
5) Put AI behind the process, not in front of it
Use AI to:
- summarize calls into structured fields
- draft emails and proposals inside guardrails
- suggest next-best actions based on defined rules
- triage exceptions and surface data issues
Avoid AI as a “replacement for clarity.” That’s how projects get scrapped for unclear ROI. Gartner
6) Create a cross-functional “GTM systems pod”
Not a bloated committee. A nimble pod.
- RevOps / systems
- data / engineering
- marketing ops
- sales enablement
- a rotating frontline rep/manager
Give it authority to develop and deploy workflow changes and measure impact.
7) Invest in role evolution and change management, not just hiring
If you’re trying to hire unicorns to solve systemic problems, it’ll stay painful.
Instead, evolve existing roles:
- sellers learn lifecycle + CRM hygiene + workflow feedback
- marketers learn attribution mechanics + routing + conversion instrumentation
- tech teams learn funnel constraints + revenue metrics + stakeholder context
This is how you build a durable advantage: shared operating capability.
The point (for absolute clarity)
This isn’t “everyone does everything.” It’s “everyone has line-of-sight.”
Specialization can still matter. But true irreplaceability (and your company’s competitive advantage, for that matter) is increasingly built in the connective tissue: process, data, automation, measurement, and governance.
The org chart might still show silos. But the work won’t, and shouldn’t.
And the teams that adapt fastest will look “unfair” to everyone else. Because they’re not just working harder, they’re operating as a single system.
