What is Agentic AI and why does it matter?
Agentic AI describes systems that act as semi-autonomous agents: they observe goals, plan multi-step actions, execute across tools (email, CRMs, ad platforms, content repositories), and adapt based on feedback.
Unlike traditional point-AI that offers predictions or suggestions, agentic AI can carry out sequences and close loops — for example, researching prospects, drafting outreach, sending emails, then adjusting the sequence based on opens and replies.
Why this matters for sales and marketing teams
Velocity: Agents reduces the time to test and iterate campaigns, running hundreds of micro-experiments in parallel.
Scale: Personalisation moves from rule-based templates to dynamic, context-aware messaging at large scale.
Efficiency: Routine orchestration (e.g., lead qualification, follow-ups) is automated, freeing human teams for strategy and complex negotiation.
Risks and guardrails that are recommended early on include clear objective-setting, human-in-the-loop checkpoints for brand voice and compliance, and robust logging for audit trails.
Concrete examples and workflows sales & marketing can adopt
Here are three practical agentic workflows that can translate quickly into outcomes:
1. Autonomous lead nurturing agent (B2B)
Goal: Convert MQLs to SQLs.
What the agent does: pull enrichment data (company size, tech stack), score leads, draft tailored multi-touch email sequences, schedule calls with available AE slots, and update CRM fields after interactions.
Result: Sales teams reported higher lead engagement and faster response times because the agent handled routine follow-ups while AEs focused on demos and negotiations.
2. Creative optimisation agent (B2C retail)
Goal: Improve CTR and ROAS across channels.
What the agent does: generate multiple creative variants, allocate budget across channels based on early signals, iterate copy and images, and pause underperformers automatically.
Result: Campaigns achieved incremental uplift; marketers reclaimed time previously spent on manual A/B testing.
3. Proposal & negotiation assistant (complex sales)
Goal: Reduce proposal turnaround and increase win rate.
What the agent does: assemble personalised proposals from templates, suggest pricing options based on competitor and historical win data, and recommend negotiation tactics to AEs.
Result: Faster proposal delivery and more data-driven pricing decisions.
Operational notes: agents need access to clean CRM data, well-defined KPIs, and rate limits to prevent uncontrolled actions (e.g., mass email sends without review).
Case references
Real pilots and plausible scenarios showed how organisations adapted the tech with regional sensitivities in mind.
Financial services pilot
A mid-sized bank piloted agents to automate SME loan outreach. Agents scanned transaction signals and ERP integrations, then nudged likely candidates with pre-approved offers.
The bank saw faster application rates but introduced strict human review for offers above certain values. The pilot highlighted the need for AML and KYC integration with agent decision flows.
eCommerce marketplace
An online marketplace wanted to reduce cart abandonment across regional languages. An agent performed product recommendations, generated hyper-localised messages in multiple languages, and adjusted push timing based on regional buying hours and festival calendars. The initiative showed potential for massive scale but required rigorous localisation testing and bias checks on language models.
B2B software case
A SaaS vendor used agents for post-trial conversion: agents analysed in-product behaviour, surfaced likely expansion signals to account managers, and autonomously offered tailored webinars or trial extensions. The outcome was higher conversion when agents provided timely, relevant interventions, but the vendor ensured human approval for any discounting.
Measurement, ethics and the commercial math
Adoption succeeds when teams treat agents like product launches: clear success metrics, controlled rollouts, and fast feedback loops. A few principles can be applied:
- Define guardrail KPIs (brand-safety incidents, hallucination rate, error corrections) alongside business KPIs (CTR, conversion rate, time-to-conversion).
- Start with supervised autonomy: human review for first 10–20% of actions, then ramp.
- Log decisions for auditability and explainability.
A note on scale: PwC had estimated that AI could contribute up to US$15.7 trillion to the global economy by 2030, reinforcing the macroeconomic impetus for businesses to experiment responsibly with advanced AI capabilities (PwC, Sizing the prize: https://www.pwc.com/gx/en/issues/analytics/assets/pwc-ai-analysis-sizing-the-prize-report.pdf).
Checklist: five practical actions to get started (for sales & marketing leaders)
- Identify 1–2 high-friction workflows (lead follow-up, creative testing) and map required data sources.
- Run a low-risk pilot with human-in-the-loop review and a 4–6 week test window.
- Define success metrics and guardrail KPIs before the pilot starts.
- Ensure data governance: access controls, consent, and data residency rules aligned with local regulations.
- Create escalation and rollback procedures for brand-safety or compliance incidents.
