708 executives and AI Agents walk into a bar…..

And surprisingly, it’s more positive than I thought.

Why 708? PwC surveyed 408 executives and Clari surveyed 300.
408 + 300 = 708 — quick maths.

Anyway…

PwC’s recent AI agent survey shows something important: C-suite sentiment about agentic AI is shifting from hesitation to curiosity to impact.

Executives are increasing budgets and early adopters are reporting measurable productivity gains. That’s the good news.

There’s a problem, though.

Many pilot projects failed to scale — not because the tech doesn’t work, but because companies tried to run sophisticated agentic workflows on poor foundations.

Clari’s enterprise research nails the reason: it’s almost always the data and the operating model.

Agents need clean, trusted, actionable revenue signals to create dependable outcomes.

Without that, suggestions look wrong; reps stop using them; pilots stall.

Before diving into the impact of AI agents here are major CRO moves that took place recently:

• OX Security appointed Ohad Cohen as Chief Revenue Officer

• Everise named Robert Jimenez Chief Revenue Officer

• Actelis Networks appointed Mark DeVol as CRO, Americas.

• Rightworks named Jay Muelhoefer CRO alongside a new CEO

• Triton Digital appointed Sharon Taylor CRO

• Infoblox appointed Joshua Husk CRO.

• DoorDash hired Lee Brown (ex-Spotify ads) as CRO

Why You Should Probably Be Using AI Agents

PwC’s May 2025 survey shows:

• 88% of executives say their team or business function plans to increase AI-related budgets in the next 12 months due to agentic AI.

• 75% agree or strongly agree that AI agents will reshape the workplace more than the internet did.

• Of those adopting AI agents, nearly two-thirds (66%) report increased productivity. Over half (57%) report cost savings, faster decision-making (55%) or improved customer experience (54%).

It’s clear that leadership want AI results now — increased revenue, lower costs or other concrete value — and AI agents are delivering.

But, There Are Problems And Challenges To Consider

Interestingly, the danger here is in settling for too little.

Companies that stop at pilot projects will soon find themselves outpaced by competitors willing to redesign how work gets done.

Adapt, or risk getting left behind.

46% of respondents say they are concerned their company may be falling behind competitors in adopting AI agents.

Whether agents are running in the background to streamline tasks or actively responding to commands to gather insights, solve problems and get work done, they expand human capacity, speed and reach.

It takes leaders rethinking strategy, redesigning workflows and actively bringing employees into the mix.

But, right now, people including senior leaders are holding AI agents back.

The real challenges highlighted in the PwC report were those that were ranked at the bottom of the list and are rooted in organisational change:

• The ability to connect AI agents across applications and workflows (19%)

• Organisational change to keep pace with AI (17%) and

• Employee adoption (14%).

Another barrier to progress is a lack of trust in AI agents. 28% respondents ranked it a top-three challenge. When asked which tasks they trusted AI agents to handle, respondents expressed the highest levels of trust in areas like data analysis (38%), performance improvement (35%) and daily collaboration with human team members (31%).

All Roads Lead To Data

“78% of Enterprises Stalled With AI Adoption — Because They Don’t Trust Their Revenue Data” - Clari

Most pilots stall because the data that agents rely on is incomplete, messy, or untrusted. Agents aren’t magic — they only amplify whatever information you feed them. If that feed is noisy, agents make noisy recommendations, reps lose trust, and pilots die.

Here’s what “bad data” looks like and why it kills agent adoption:

• CRM fields are incomplete or inconsistent → agent can’t infer deal stage or next action.

• Duplicate records and bad enrichment → agents recommend outreach to the wrong contact.

• No call transcript coverage → meeting-prep and sentiment signals are blind.

• Activity lag or stale signals → agents surface stale next-best-actions that frustrate reps.

How To Go From Pilot → Scale

PwC’s practical advice: start with 2–3 simple workflows that deliver immediate value, measure impact, then expand.

Here’s how I translate that to GTM.

Play 1 — Enrich & Qualify (signal → fit)

• What it does: Auto-enrich inbound/scraped records and run a Qualify Person/Company agent.

• Value: Reps only handle clean, high-fit leads → less admin, higher conversion rates.

• Metric: % of enriched records that move to “qualified” vs baseline.

Play 2 — Meeting Prep & Win Plan (context → conversion)

• What it does: Auto-generate one-page pre-call briefs (context, signals, suggested lines of attack).

• Value: Reps show up prepared, shorter meetings, higher meeting→opportunity conversion.

• Metric: Change in meeting→opportunity conversion.

Play 3 — Signal-Driven Outbound & Prioritisation

• What it does: Agents monitor high-quality Ideal Customer Signals (funding, product launches, job moves, intent page hits, tech installs, press), score and prioritise accounts in real time, auto-generate hyper-personalised first touches (1-sentence LinkedIn / 2-line email + 30s call opener).

• Value: Fewer sequences to better accounts — higher reply→meeting conversion, faster pipeline creation, fewer wasted touches, more pipeline from the same headcount → lower CAC and faster payback.

• Metric: Meeting-booked rate per outreach; replies→meetings conversion; touches per booked meeting; time-to-first-meeting from initial signal; CAC on outbound-sourced pipeline.

What Cabot is building (and why it matters for PEG)

At Cabot we’re running Octave playbooks → Clay agent engineering → HubSpot enrichment → Instantly campaigns→ Feedback → Test → Results.

The result: micro-wins that compound into PEG — less headcount, more signal, higher output, lower CAC.

If you want to see this live, I’m doing a hands-on session with HubSpot for Startups on Aug 27: “0 → 100 Customers.” I’ll demo some of these agents in motion. Save a spot by messaging me directly on LinkedIn.

P.S. for those wondering: Cabot Insights blends autonomous AI agents with proven outbound playbooks—helping sales teams triple pipeline velocity, slash manual work and scale smarter, not headcount. Ready to 3× your pipeline without hiring more reps?

Let’s chat about your PEG plan.

– Stephen 

www.cabot-insights.com