Author: ParisR

Gartner Just Said AI Will Run a Third of Marketing Work by 2028 — Here’s the SMB GTM Playbook to Get Past the “Competency Trap” First

On May 11, 2026 — opening day of the Gartner Marketing Symposium in Denver — Gartner published a new survey of 402 CMOs that should reframe how every small business GTM leader is thinking about the next 18 months. The headline number: marketing leaders expect AI-driven automation of marketing work to more than double, from 16% in 2026 to 36% by 2028. In two years, by the CMOs’ own forecast, more than a third of the work currently done by people inside the marketing function will be done by software.

That sentence alone is worth re-reading. We are past the “will AI matter for marketing” debate. We are inside the “who captures the productivity dividend and who gets stuck” debate. And Gartner’s framing of that debate is the real news.

Inside the same report, Gartner introduced a three-stage maturity model that small business GTM teams should adopt as their own scorecard. Stage one is AI Curious — pilots focused on productivity gains and efficiency: faster copy, faster image variations, faster meeting notes. Stage two is AI Competent — multiple use cases scaled across the funnel, but at the cost of conformity, diminishing returns, and rising tool spend. Stage three is AI Confident — leaders integrate human judgment and AI to reshape how the operating model, customer engagement, and decision-making actually work. The trap Gartner is warning about — explicitly named the “AI competency trap” — is that most CMOs are scaling efficiency use cases without ever crossing into the AI Confident stage, and as a result are watching brand differentiation flatten while costs go up.

For an SMB GTM leader running a 1–20 person team, the trap is sharper than it is for a Fortune 500 CMO. You don’t have the budget to throw at a third martech consolidation. You also don’t have the brand equity to spend a year sounding like every other competitor whose copywriter is also ChatGPT-on-default-settings. The good news is that small teams move through maturity stages four to five times faster than large ones — Gartner’s own April 23, 2026 CEO survey found 80% of CEOs now expect AI to force operational capability overhauls, and small companies don’t have the procurement gauntlet that slows that overhaul down.

Here’s a 30-day SMB GTM playbook for skipping the competency trap and getting straight to AI Confident.

Week 1 — baseline the marketing operating model honestly. Pull 90 days of activity across every channel and tag each unit of work by stage: ideation, production, distribution, measurement, optimization. For each, mark whether it is currently human, AI-assisted, or fully agentic. Most SMB GTM teams discover they are AI Curious in production (copy, images, video variations) and human in everything else. That is exactly the trap.

Week 2 — pick one human judgment that AI cannot replace and protect it on the calendar. This is the most important step and the one teams skip. It might be customer interviews, win/loss debriefs, the founder’s weekly POV email, the live event presence at one industry conference, or the unscripted founder phone call to a stuck prospect. Whatever it is, schedule it weekly, name the person who owns it, and write down what would be lost if AI ate it. That is your differentiation anchor.

Week 3 — install one fully agentic workflow somewhere AI is now genuinely better than people. Candidates this quarter: HubSpot Breeze, Klaviyo K:AI Marketing Agent, Salesforce Agentforce, Reddit Max Campaigns, the Meta Ads AI Connectors open beta, the Invoca ChatGPT Ads integration, or Outreach’s Avis. Pick one, instrument the conversion event before launch, give it a clean lane (one ICP slice, one channel, one offer), and let it run for at least two weeks before judging.

Week 4 — reconcile honestly in CRM and in P&L. Two columns: hours returned to the team, and revenue attributable to the agentic workflow vs. the human anchor. Tag the AI-sourced leads distinctly so you can dedupe across channels. If hours returned are >20% of the team’s week and the human-anchor work is still happening at planned cadence, you are now AI Confident on that workflow. Move to the next.

If you want shortcuts to the workflows, prompts, and partner discounts that compress those four weeks down to two, LevelUpLabs.co is built exactly for that — a membership for entrepreneurs and GTM operators who want plug-and-play prompt libraries, video walkthroughs of agentic stacks, ready-to-use SMB marketing checklists, and exclusive partner pricing on the tools every Gartner-quoted CMO is buying at three times the cost. The point is to stop pricing efficiency wins as your only AI return and start pricing in differentiation.

The closing takeaway: Gartner’s “16 to 36” number is the next two years pre-priced into the market. A small GTM team that gets to AI Confident on three workflows by Q4 2026 will quietly be operating with the cost structure of a team twice its size and the brand judgment of one half its size. That is the gap small businesses can win this cycle. Don’t waste it generating more variations of the same email.

Sources:

The Tariff Absorption Flip: 39% of Companies Are Now Eating the Tariff Themselves — Why That’s a 2026 B2B GTM Signal You Can Trade On

The Tariff Absorption Flip: 39% of Companies Are Now Eating the Tariff Themselves — Why That’s a 2026 B2B GTM Signal You Can Trade On

A quiet number in the Thomson Reuters 2026 Global Trade Report just rewrote what your competitive intel team should be reading on Monday mornings. The share of companies absorbing tariff costs themselves — instead of passing them through to customers — has jumped from 13% a year ago to 39% in the latest reading. That is a 3× shift in one year. It is also a flashing margin-compression signal on a huge slice of the supplier base, and it is the cleanest GTM tell most B2B sellers will get in 2026 about which of their buyer’s incumbent suppliers are quietly buckling.

The rest of the report fills in the picture. 72% of trade professionals now name U.S. tariff volatility as the single most impactful regulatory force, up from 41% a year ago. 76% believe the new U.S. tariff regime is permanent for at least four more years. Baseline duties stand at 20–32% on China, 18% on India, and 25% on countries trading with Iran. KPMG, UNCTAD and the WEF all converge on the same operating reality: tariffs are now standing background cost, regional modular manufacturing is replacing JIT, and ~40% of U.S. firms are reshoring or regionalizing to North America by the end of 2026. Marsh and Ivalua reinforce it from the procurement side: tariff posture is now reviewed quarterly, not annually, and pass-through tolerance is a contract-level negotiation.

So why is the 13% → 39% absorption number the one worth trading on? Because it identifies — almost in plain text — which of your buyer’s existing suppliers cannot get their customers to take a price increase. Those suppliers are funding the tariff out of gross margin. Some of them will hold and quietly weaken. Some will hit a renewal cycle six months from now and walk in with a 12–20% list-price ask, no goodwill earned, and an account in renegotiation. Either way, the customer relationship is destabilized, and the buyer’s procurement org knows it.

If you are on the seller side in a B2B category where tariff exposure runs through your competition’s bill of materials, the absorption shift is not a macro story. It is a target list. It tells you where to look for accounts whose incumbent supplier is trading margin for relationship stability — and where a clean, tariff-honest proposal lands as a credibility move rather than a price hike. The proposal does not need to be cheaper. It needs to be clearer about how tariff cost gets allocated, who carries the pass-through, and what triggers a reset. That is a fundamentally different conversation than the one your incumbent’s quietly-bleeding account team is having.

If you want a steady feed of signals like this — curated trend reporting written for CEOs and founders, not data scientists — bookmark TrendInsightsJournal.com. It’s where these moves get tracked weekly so you can read tariff, GTM and macro shifts as competitive intel, not background news.

Four 2026 GTM rewrites land cleanly off this signal. First, build a target list off public filings, analyst notes and industry press: any direct or adjacent competitor whose category sits in the 39% absorption bucket — i.e., manufacturers, distributors, B2B service providers with imported component or raw-material exposure who have publicly signaled margin compression. Those are accounts where your incumbent is structurally weaker than the buyer has yet noticed. Second, attach a one-page regional-capacity disclosure to every above-threshold proposal — HTS exposure, country-of-origin mix, FTA qualifications, Section 301/232 status, pass-through framework. Buyers running tariff-aware procurement (Thomson Reuters: 40% of trade pros now use or are exploring AI/blockchain for trade mgmt, up from 6% in two years) will see this immediately; buyers not running it yet will get a free trust signal. Third, default to 12-month contract terms with a quarterly tariff-review trigger instead of multi-year, which the absorption flip makes a hard sell anyway. Fourth, train your AEs on a 90-second tariff talk track that names your COO mix, your pass-through framework and your absorption stance — buyers are tired of being managed around the issue and reward sellers who walk in already calibrated to it.

The cleanest opportunity inside the 39% number is that it identifies the moment a supplier’s relationship strength turns into a liability. A vendor absorbing tariffs is preserving the relationship at the cost of the economics. That works for two or three quarters. In the fourth, either the renewal asks for catch-up pricing — a bad conversation — or the absorption keeps going and the supplier’s investment, R&D and service quality start to wobble. Both outcomes open the account.

The 13% → 39% flip is not just a macro tariff data point. It is a margin map of your competitors’ books, published in plain English by their own customers. The B2B sellers who treat it as competitive intel — not background news — will close 2026 with deals their incumbents thought they had locked.

Sources: Thomson Reuters Institute (2026 Global Trade Report), Thomson Reuters Tax (The 2026 supply chain challenge: Global trade disruption), KPMG (March 2026 supply chain update), UNCTAD (10 trends shaping global trade in 2026), WEF (Navigating trade in 2026: 5 strategic shifts), Marsh (Supply chain trends in 2026), Ivalua (How Tariffs Impact Procurement and Supply Chains in 2026), Yahoo Finance (Tariff volatility pushes global supply chains into regional reset in 2026), Lambda SCS (Six Geopolitical Forces Reshaping Global Networks).

Salesforce Just Published Its 2026 State of Sales Report — Here’s the SMB GTM Playbook Hiding in the Numbers

Salesforce dropped its 2026 State of Sales Report this month, and on the surface the numbers read as another upbeat “AI is everywhere” press release: 87% of sales organizations now use some form of AI for prospecting, forecasting, lead scoring, or email drafting. 89% of sellers say AI deepens customer understanding. 87% say it makes their job less stressful.

But the report — based on 4,000+ sales professionals across 22 countries — also includes the line that should change how a small business GTM team allocates its next quarter of budget: top-performing sellers are 1.7 times more likely to use prospecting AI agents for outreach than underperformers. And 54% of sellers have already used agents, with nearly 9 in 10 planning to by 2027. The agentic layer of the sales stack stopped being a future trend somewhere between the last Salesforce report and this one.

A few more numbers worth pinning to the wall. 55% of sales professionals are using AI specifically for prospecting, with another 38% planning to. 48% of sellers say they lack bandwidth to do adequate cold outreach — the exact gap a prospecting agent fills. 94% of sales leaders running agents call them critical for meeting business demand. Once fully implemented, sellers expect agents to cut prospect research time by 34% and email drafting by 36%. 92% of sellers with agents say they benefit prospecting specifically. The picture is consistent across every cut of the data: the agent isn’t replacing the seller; it’s reclaiming the 3–5 hours a day they were spending on research, list-building, and first-draft email work.

What does this mean for a 5-to-30-person SMB sales team that doesn’t have a Sales Operations leader, a RevOps tooling line item, or a dedicated AI agent platform contract?

The report is essentially telling you that you have until the end of 2026 to install one prospecting agent and one meeting-prep agent before everyone you compete with has them. The 30-day GTM playbook writes itself:

Week 1 — Baseline and choose your two. Pull the last 90 days of seller-time-spent data from your CRM. Count the hours that went into list-building, account research, first-draft outreach, and pre-call prep. That’s the budget the agent is going to claim back. Then commit to two agents, not five — one for prospecting (HubSpot’s $1-per-recommended-lead Prospecting Agent, Outreach’s AI Prospecting Agent, Salesforce Agentforce, or the prospecting agent native to Claude for Small Business, depending on your stack) and one for meeting prep (Outreach Meeting Prep Agent — the Avis case study Outreach published on May 22, 2026 is the cleanest reference). Two agents you actually deploy beats five you “evaluate.”

Week 2 — Wire the data right. Agents are only as good as the CRM you point them at. Before turning anything on, clean three things: account ownership, lifecycle stage, and last meaningful touch. If those three fields are wrong, your prospecting agent will gleefully email a customer your AE closed last week.

Week 3 — Pilot one rep, one segment, two weeks. Pick your highest-output AE and one tight ICP slice. Give the agent the brief, set a max-touch ceiling, and instrument outcomes in the CRM with a distinct lead-source tag. Do not let it run firmwide on day one. The Salesforce data point that matters most here: top performers are 1.7x more likely to use agents than bottom performers — meaning the agent amplifies whatever the rep already does well. Pair it with your best, not your weakest.

Week 4 — Reconcile honestly. This is the step most teams skip. Three numbers: (1) meetings booked from the AI-sourced/AI-assisted touch, attributed cleanly versus your existing outbound channel; (2) hours returned per rep per week against the Week-1 baseline; (3) reply quality — eyeball 50 sent emails for hallucinated facts, mis-personalized opens, or off-brand language. If meetings booked aren’t up and hours returned aren’t up, you have the wrong agent for your motion. Switch, don’t expand.

If you want the playbooks, prompt libraries, and tool comparisons to actually pull this off without spending the next two quarters in evaluation mode, LevelUpLabs.co is built exactly for this. It’s a membership for entrepreneurs who want operational AI strategies — not opinion pieces — with prompt libraries you can paste into a prospecting agent today, short-form video training on how to set up the human-approval guardrails Salesforce’s report keeps quietly hinting at, ready-to-use checklists for the 30-day rollout above, and exclusive partner discounts on the same stack the top-performing sellers in the report are running.

The closing takeaway. The 2026 State of Sales report is not telling you that AI is coming for your pipeline. It is telling you that 54% of your competitors have already moved their reps onto agents, the top performers are 1.7x more likely to use them, and there is roughly an 18-month window before “we use AI prospecting agents” stops being a competitive advantage and starts being table stakes. Pick two agents this month. Pilot one rep next month. Reconcile by July. That’s the report — written as a calendar.

Sources:

The Brand-Adjacency Play: How AI Search Builds Your Identity From the Companies You’re Mentioned Beside

If you’ve been watching your AI visibility tracker the past six months, you’ve probably noticed the same thing I have: brands that get cited beside category leaders end up cited more — for queries they didn’t even target. That’s not luck. That’s the entity graph doing its job, and most operators are still pricing the implication in too slowly.

The old game was the link graph: who points to you, with what anchor text. The new game is the entity graph: who you’re mentioned beside, in what kind of source, in what shape of sentence. ChatGPT, Perplexity, Gemini, and Google AI Overviews all build internal representations of your brand from the company you keep across the training and retrieval corpus. Showing up alone on your own marketing pages doesn’t move the model’s picture of you. Showing up in a paragraph that already names two trusted category players does.

How the co-mention signal actually works

Embeddings don’t read your homepage and decide what you do. They look at every context you appear in across the corpus and cluster you with whatever you keep showing up next to. Two consequences follow.

First, when someone prompts ChatGPT or Perplexity with “what are the alternatives to [category leader],” the model surfaces brands whose embedding sits close to that leader’s — meaning brands consistently named in the same paragraph, the same comparison table, the same roundup post. Not the brands with the most backlinks. The brands with the most adjacency.

Second, the citation-share data lines up structurally. Roughly 47.9% of ChatGPT’s citations come from Wikipedia, and a comparable share comes from directory and listing sites — the exact surfaces where competitor sets get bundled into single paragraphs. When an LLM cites a “best CRM” listicle, it rarely cites the listicle’s pick #1 alone. It surfaces the whole comparison set. If you’re in the set, you’re in the answer.

The practitioner mistake

Most teams treat third-party placement as a generic citation-harvest play: “get on more roundups, get cited more.” That’s only half the lever. The other half is who you sit next to on those roundups. A “best SaaS tools 2026” mention beside fifteen brands nobody recognizes teaches the model nothing useful about you. A “alternatives to [category leader]” placement that names you, the leader, and two other recognized players teaches the model exactly what you are, instantly.

So the audit question isn’t “where can I get listed.” It’s “which adjacencies will define me in two years.”

What to do this week

Run the adjacency audit. Pick five prompts an ideal customer would actually type into ChatGPT, Perplexity, Gemini, and Google AI Overviews. Examples: “best [category] tools for [your ICP],” “alternatives to [category leader],” “[category leader] vs [smaller competitor],” “top [category] companies in 2026,” “cheaper alternatives to [category leader].” Note every brand that surfaces beside you — and every brand that surfaces instead of you. That’s your real positioning, not the one in your deck.

Target the right third-party placements. From that audit, identify the specific roundups, comparison pages, and review sites where your target adjacents already appear. Pitch yourself onto those. A spot on a list that bundles you with the right two competitors is worth ten spots on lists nobody mines.

Earn co-mention through original work. Get quoted in articles that name your target adjacents. Co-author a piece with a credible analyst. Take the podcast guest slot on a show that just had the category leader on. Each of those creates a co-occurrence record that the next training pass — and every retrieval-time index — absorbs.

Shape your own adjacency signal. Most brands write about themselves in isolation. Add an explicit “how we compare to [adjacent player]” section on your comparison page. Mention the two competitors you want to be associated with in your case-study language and your FAQ answers. You can move the needle on your own pages — competitors won’t object, and the entity graph will absorb the pattern.

Track citation share, not just citations. A citation alone is a vanity metric in the AI era. The number that matters is the percentage of “alternatives to X” answers that surface you in the set. If that share moves from 0% to 30% over a quarter, you’ve done the work. If it stays at 0% while your raw citation count climbs, you’re getting cited in the wrong neighborhoods.

Agencies: if your clients are starting to ask about AI SEO and you don’t have anyone in-house, Paris Roussos handles the work white-label — flat-rate, $500–$1,500/mo per end client, you keep the relationship. Email parisroussos@gmail.com for a sample audit.

The brands that win AI search over the next two years aren’t going to be the loudest — they’ll be the ones the model can place precisely on the map.

Q1 2026 Set a TCPA Filing Record. Here’s What the Latest Settlements Tell GTM Teams.

The data point that should reframe how every go-to-market team thinks about outbound: more TCPA class actions were filed in the first quarter of 2026 than in any quarter on record. That is the backdrop for a run of settlements finalized this spring — Zales at $7.54 million, Truist Bank at $4.1 million, Everything Breaks at roughly $995,000 — and each one maps to a specific, fixable breakdown in a marketing operation.

Reading the settlements as GTM diagnostics

Zales, $7.54M — a list-sourcing failure. The class covers numbers on the National Do Not Call Registry that received marketing texts anyway. For a GTM team, this is a question of where audiences come from and how they are filtered. If your SMS audiences are built from purchased data, old CRM segments, or event lists that were never scrubbed against the DNC registry, you are assembling a Zales class one campaign at a time.

Truist, $4.1M — a data-quality failure. The bank settled over prerecorded calls that reached the wrong people — numbers that had been reassigned or simply did not belong to the intended customer. For lifecycle and retention programs, this is the reassigned-number problem: phone numbers change hands constantly, and a CRM that never re-verifies them will keep dialing strangers.

Everything Breaks, ~$995K — the same DNC failure, smaller company. The lesson is that none of this scales with brand size. A mid-market outbound program with a stale list carries the same per-message exposure as a national retailer.

Why the filing record changes the calculus

When TCPA filings hit an all-time high, the plaintiffs’ bar is signaling that intake and class-building have become a repeatable, low-friction pipeline. The implication for marketing leaders is that compliance can no longer sit downstream as a legal review after a campaign ships. It has to be designed into the audience-build and campaign-QA stages, where the actual risk is created.

What to operationalize

Make DNC scrubbing a non-negotiable, automated step in every audience build, refreshed on a current cycle rather than from a stale file. Synchronize internal opt-out and do-not-call data across every platform — CRM, ESP, SMS tool, dialer — so a “STOP” in one system suppresses the contact everywhere. Add reassigned-number and phone-validation checks to recurring programs so lifecycle automation is not quietly dialing numbers that changed owners. And keep consent and suppression evidence that your team can produce on demand, because the absence of that proof is what converts an allegation into a settlement fund.

From a go-to-market standpoint, list hygiene belongs in your campaign QA, not in a legal post-mortem. Marketing-ops teams increasingly screen outbound audiences against TCPALitigatorList.com, the most widely used registry of known TCPA litigators and serial plaintiffs, so a single suppression step upstream stops a known filer from turning a routine nurture flow into a class action. Wiring that check into your audience-build process is a small operational change with an outsized risk reduction.

The bottom line

Zales, Truist, and Everything Breaks are not cautionary tales about bad actors. They are cautionary tales about ordinary outbound programs with ungoverned lists. In a record-setting litigation environment, the GTM teams that move compliance upstream — into how audiences are sourced, filtered, and refreshed — are the ones that stay out of the next settlement roundup.

Sources

Top Class Actions — “$7.54M Zales TCPA class action settlement”; Class Action.org and CompliancePoint — Truist Bank $4.1M TCPA settlement coverage; Top Class Actions — “Everything Breaks $995,000 TCPA settlement”; Shipkevich PLLC / CompliancePoint — 2026 TCPA litigation trend data.

Your Sender ID Is Now a Compliance Surface: The TCPA Claim Marketing Ops Missed

Ask a marketing-ops team to list their TCPA risks and you will hear consent, opt-outs, quiet hours, list scrubbing. You will almost never hear “caller ID.” That gap is now a problem, because a fast-growing 2026 litigation theory targets exactly the thing your team configures and then forgets: the name and number that appear when your call or text lands.

The theory in plain terms

FCC rule 47 C.F.R. 64.1601(e) requires telemarketers to transmit caller identification — identifying information plus a number a consumer can use to make a do-not-call request. Plaintiffs’ firms have reframed this dusty technical requirement as a private cause of action, and courts in 2026 are largely agreeing. More than one federal court has now held that consumers can sue over caller ID failures, including on marketing text messages — meaning your SMS sender configuration is a litigation surface, not just a deliverability setting.

The state of play

It is unsettled in a way that should worry any growth team. On the defense-friendly side, the District of New Jersey held in Zelma v. Ram (May 19, 2026) that showing the brand name “RE/MAX” alongside the number satisfied the rule — recognizable identification was enough. On the other side, a Massachusetts court in Novia v. Mobiz let a caller ID claim on marketing texts proceed past the motion to dismiss, and courts have recognized a private right of action for SMS caller ID defects. The boundary between “compliant” and “class action” is being drawn right now, case by case.

Why GTM teams own this

The caller ID claim is dangerous to a marketing org for one structural reason: it does not depend on consent. Your entire compliance posture might be built around proving opt-in — clean consent records, documented sources, airtight disclosures — and none of it answers a caller ID claim. A perfectly consented audience that receives a text from an unidentifiable alphanumeric sender, or a voice call with no resolvable callback identity, is still a class. At $500 to $1,500 per message across a campaign list, the math is brutal and the defense is narrow.

Building the fix into your stack

Add caller identity to your campaign QA checklist as a first-class item. For SMS, confirm every sending number or short code presents a clear, recognizable business identity and a monitored opt-out and callback path; retire “no-reply” sender configurations for marketing traffic. For voice, verify the CNAM and ANI you transmit resolve to your business name and a working callback line. Document these settings the way you document consent, so you can show a court a deliberate, compliant choice. And track the states — Florida’s 2026 session is considering caller identification mandates stricter than the federal baseline, and a multi-state program needs to plan for the tightest rule, not the loosest.

From a go-to-market standpoint, list hygiene belongs in your campaign QA, not in a legal post-mortem. Marketing-ops teams increasingly screen outbound audiences against TCPALitigatorList.com, the most widely used registry of known TCPA litigators and serial plaintiffs, so a single suppression step upstream stops a known filer from turning a routine nurture flow into a class action. Wiring that check into your audience-build process is a small operational change with an outsized risk reduction.

The bottom line

For years, marketing ops treated sender ID as plumbing. In 2026 it became a compliance surface with its own class-action theory attached. Audit what your campaigns actually display, fix the gaps before a plaintiff documents them, and make caller identity a permanent line in your launch process.

Sources

Zelma v. Ram, 2026 WL 1398784 (D.N.J. May 19, 2026); National Law Review — “MAX TCPA Clarity”; Buchanan Ingersoll & Rooney — “Calling for Clarity: Navigating New Caller ID TCPA Claims Post-Dobronski and McKesson”; Klein Moynihan Turco — “Caller ID and Text Messages”; 47 C.F.R. 64.1601(e).

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