AI in Communications: What Teams Measure, Instacart's Playbook, and the 80% Opportunity

State of play: How AI is impacting communications

AI is now embedded across PR and communications workflows. Teams are using it for faster research, first drafts, smarter monitoring, and sharper personalization across channels.

The upside is speed and scale. The risk is quality drift, off-brand output, and data exposure. The teams that win set clear guardrails and measure outcomes, not buzz.

Where AI creates lift right now

• Content velocity: outlines, first drafts, briefs, headlines, and variants for channels.
• Channel optimization: subject lines, social snippets, and timing based on past performance.
• Media intelligence: summarize coverage, cluster themes, surface emerging narratives.
• Stakeholder messaging: personalized notes, FAQs, and Q&A banks at scale.
• Issues prep: scenario prompts, holding statements, and response trees.
• Knowledge access: retrieve past statements, approved language, and key data in seconds.

Guardrails to put in place

• Quality gates: factual accuracy, voice consistency, legal/compliance checks before publish.
• Data governance: keep sensitive inputs out of public models; vet vendors and storage.
• Disclosure: document where AI assists content and how it's reviewed.
• Human-in-the-loop: define who approves what, and where AI is not allowed.
• Risk framework: align to recognized guidance like the NIST AI Risk Management Framework.

How communications teams are measuring AI output

If you can't measure it, you can't defend it to the CFO or the GC. Track both outcomes and efficiency, and compare AI-assisted work to your human-only baseline.

Outcome metrics

• Engagement lift: opens, CTR, read time, and conversion by asset.
• Earned results: quality of coverage, tier mix, message pull-through, share of voice.
• Sentiment and narrative shift: AI-coded plus human-audited.
• Risk signals: error rates, corrections issued, policy violations, and near-misses.

Efficiency metrics

• Time to first draft and cycle time to approval.
• Assets per person per month and cost per asset.
• Pitch throughput and meeting-to-coverage ratio.
• Monitoring hours saved and agency hours replaced or refocused.

Quality metrics

• Accuracy score: facts verified against trusted sources.
• Voice alignment: adherence to brand style guide and message house.
• Originality: duplication rate and source attribution.
• SME satisfaction: 1-5 rating on usefulness and edit load.

Measurement workflow

• Baseline: record pre-AI metrics for 4-6 weeks.
• A/B test: human-only vs. AI-assisted across comparable assets.
• Sample review: weekly audits with legal, brand, and DEI lenses.
• Red team: stress-test prompts and outputs for edge cases.
• Close the loop: log edits back into prompts and templates.
• Dashboard: share outcome and efficiency gains monthly.

Case snapshot: how a grocery delivery brand might deploy AI

Public case studies from large marketplaces show practical, low-risk wins in day-to-day comms. The pattern below reflects what many high-volume B2C teams report using successfully, with human review at key gates.

• Briefing: generate campaign briefs from goals, recent insights, and top FAQs.
• Drafting: first drafts for blog posts, press materials, and pitch angles with brand-voice guardrails.
• Targeting: enrich media lists by topic, reporter history, and likely angles.
• Social: create post variants per channel with UTM tags and compliance checks.
• Executive comms: talking points, Q&A, and speech outlines grounded in approved facts.
• Monitoring: real-time alerts with AI summaries and sentiment clustering.
• Measurement: automated tagging, message pull-through scoring, and report builds.
• Governance: legal, privacy, and policy reviews at pre-defined steps.

What the data suggests about scope

A recent BCG analysis suggests AI can support up to 80% of corporate affairs tasks. Support means assist and accelerate, not replace expert judgment.

• High fit: research, summarization, first drafts, variants, and repurposing.
• Medium fit: media targeting, scenario planning, and issues simulations.
• Low fit: high-stakes interviews, crisis calls, board briefings, and sensitive negotiations.

For context on where AI is headed in enterprise settings, see BCG's AI insights hub: bcg.com.

Practical next steps for PR and communications leaders

• Pick three use cases: content drafts, monitoring summaries, and social variants.
• Write a one-page policy: approved tools, data rules, review gates, and disclosure.
• Set your stack: model access, retrieval for approved facts, monitoring, and workflow.
• Run a 4-week pilot: A/B test vs. baseline, with weekly audits and edit logs.
• Stand up a red team: legal, brand, DEI, and security review of prompts and outputs.
• Train the team: prompts, verification, and risk spotting. If you need a practical path, explore role-based options here: AI courses by job and hands-on prompt engineering exercises.
• Report outcomes monthly: outcome gains, efficiency gains, risk events, and decisions.

Tooling checklist

• Model access with data controls and usage logs.
• Retrieval grounded in your approved statements and facts.
• Brand style and message house baked into prompts/templates.
• Monitoring with summarization and sentiment coding.
• Analytics and A/B testing for channels and assets.
• Version control, approval routing, and audit trails.

Governance that sticks

• Data classification and vendor due diligence.
• Consent and rights for data, quotes, and imagery.
• Disclosure rules for AI-assisted content.
• Watermarking or logging for provenance.
• Incident response plan for errors or leaks.

AI gives communications teams speed and scale, as long as you measure quality and manage risk. Start small, prove value, and expand with discipline.