AI use by sales and marketing teams
This research will explore how sales and marketing teams use advanced AI capabilities that go beyond basic prompting. It will examine what these capabilities are and how teams apply them in business workflows to support tasks and outcomes.
The current state and what matters now
Actors
Sales and marketing teams remain the primary users, but the operating environment now includes more machine-mediated actors and more AI-native buying behavior.
- Revenue teams: SDRs, AEs, demand gen, lifecycle, field marketing, and RevOps using AI for prospecting, qualification, routing, forecasting, pricing support, and follow-up.
- Platform vendors: CRM, MAP, CDP, sales engagement, ad-tech, and conversation-intelligence vendors embedding copilots, agents, and workflow automation.
- Point-solution startups: tools for outbound personalization, intent detection, enrichment, meeting prep, content generation, and account research.
- IT, security, legal, and compliance: gatekeepers deciding what data can be used, where models run, what is logged, and what must be reviewed before customer-facing use.
- Executives: CROs and CMOs pushing productivity, pipeline, and cost reduction while demanding clearer proof of lift and lower headcount dependency.
- Buyers and their AI agents: prospects increasingly use ChatGPT, Perplexity, and similar tools to shortlist vendors, compare claims, and filter outreach before human contact.
Moves
Teams are moving from isolated generation tasks to continuous, event-driven workflows and AI-assisted operating models.
- Always-on prospecting: agents scanning accounts, content, engagement, and social signals to work 24/7 rather than in batch campaigns.
- AI-led qualification and routing: triaging inbound and outbound leads automatically, with human SDRs focused on exceptions and higher-value conversations.
- CRM-native execution: research, list building, sequence enrollment, logging, and follow-up happening inside the system of record.
- Trigger-based outbound: using recent behavioral events such as profile views, post engagement, competitor interactions, and product signals instead of static cold lists.
- Machine-readable positioning: structuring claims, proof points, pricing logic, and compliance details so buyer-side AI can parse and compare them.
- Ad optimization by AI: automated valuation of impressions and audience targeting that reduces manual media setup and audience guessing.
Leverage
Advantage increasingly comes from orchestration quality, signal quality, and trust, not just model access.
- Proprietary first-party data: CRM history, product usage, call transcripts, engagement data, and campaign response improve relevance.
- Workflow integration: systems embedded in daily tools can act faster and with less user friction than standalone copilots.
- Fresh buying signals: recent behavioral triggers outperform broad lists because AI can react while intent is still live.
- Decision context: account history, persona, stage, objections, and pricing constraints make outputs more usable for reps and marketers.
- Governance infrastructure: logging, approvals, provenance, and publish gates make AI safe enough for customer-facing use.
- Machine visibility: brands that are easy for AI agents to interpret, rank, and recommend gain an edge in the new discovery layer.
Constraints
Adoption is broad, but deployment is constrained by trust, quality, control, and operational brittleness.
- Data hygiene: incomplete CRM records and fragmented systems still degrade output quality and break automation.
- Routing fragility: fragmented lead stacks can misclassify records, drop qualified opportunities, or create invisible failure modes.
- Generic output: AI SDR and outreach tools often fail when targeting inputs are weak, producing low-quality volume.
- Brand and compliance risk: hallucinations, wrong claims, and unapproved publishing can create legal or reputational damage.
- Buyer skepticism: prospects are increasingly sensitive to templated, obviously AI-generated outreach and low-trust seller behavior.
- Measurement difficulty: teams still struggle to prove causal revenue lift rather than just faster content, more activity, or lower cost.
Success Metrics
Success is being measured in operational and revenue terms, with more emphasis on quality, trust, and conversion.
- Productivity: time saved per rep or marketer and reduced manual admin.
- Pipeline impact: meetings booked, opportunities created, conversion rates, and influenced revenue.
- Quality: reply rates, show rates, content performance, and fewer factual or brand errors.
- Adoption: weekly active users, workflow penetration, and percentage of tasks assisted by AI.
- Cost efficiency: lower cost per lead, lower cost per meeting, and reduced agency or contractor spend.
- Speed: shorter campaign cycles, faster follow-up, and quicker handoffs across the revenue process.
Underlying Shift
The game has shifted from creating more output to operating a responsive revenue system.
Earlier AI use focused on drafting, summarization, and research. The current phase is about agents that sense signals, decide what matters, and execute actions across the stack. That makes the CRM and adjacent systems less like passive records and more like coordination layers for continuous go-to-market action. At the same time, buyers are bringing AI into their own evaluation process, so marketing and sales must optimize not only for human attention but also for machine-mediated discovery and comparison.
Current Phase
The market is in a mid-to-late adoption phase for basic AI use, but an early phase for reliable agentic workflows.
- Not early: most teams have tried AI for content, research, or summarization, and vendors have embedded copilots widely.
- Still early: always-on agents, cross-tool orchestration, and autonomous revenue workflows are not yet standardized.
- Why this matters: differentiation is moving from experimentation to integration, governance, and measurable execution quality.
What to Watch
- Agentic workflow maturity: whether agents can reliably research, qualify, route, follow up, and update systems with minimal human intervention.
- Vendor consolidation: whether major platforms absorb point solutions through native copilots and orchestration features.
- Governance standards: stronger rules around approvals, logging, provenance, and publish gates for customer-facing AI.
- Buyer-side AI adoption: how procurement agents change vendor discovery, comparison, and deal velocity.
- Signal quality: whether teams can use recent behavioral triggers without crossing into noisy or invasive automation.
- Proof of lift: pressure to show causal revenue impact, not just faster content production or more outbound volume.
Events and actions shaping the domain
AI is compressing sales work into the admin layer
Signal-based outreach is becoming the new outbound default
Sales teams are building 24/7 AI prospecting systems
AI lead scoring is replacing legacy CRM heuristics
Marketing teams are being judged on AI-era discoverability
Interpretation of what’s changing