Our AI Plan: Hotwire Global on Why Building an AI Tool Is No Small Task

"Building an AI tool is no small task" - A practical AI plan for PR and communications teams

AI can multiply what a PR team gets done, but spinning up your own tool is hard work. It takes clear use cases, reliable data, tight governance, and a rollout plan your people will actually use.

Here's a practical blueprint inspired by agency practice, including lessons many teams learn the hard way.

Why building your own tool is hard

• Data is messy: media lists, coverage, notes, and briefs live in silos. Unifying it is step one.
• Accuracy and safety: models can fabricate facts, leak inputs, or drift without guardrails.
• Adoption beats features: if the workflow is clunky, no one uses it-no matter how clever it is.
• Maintenance never ends: models, prompts, and integrations need constant tuning.

Build vs. buy: a quick decision check

• Buy if the task is common (summaries, drafting, tagging, transcription) and tools already fit your stack.
• Build if you need proprietary knowledge baked in (client tone, coverage archive, messaging) or unique workflows.
• Hybrid wins often: off-the-shelf apps + a lightweight internal layer for governance, prompts, and data access.

High-impact use cases for PR teams

• Briefing: turn client docs into creative briefs, message maps, and Q&A packs.
• Content: first drafts for releases, bylines, bios, and social posts-always human-edited.
• Media: reporter research, angle suggestions, pitch variants by beat and outlet.
• Monitoring: coverage clustering, sentiment clues, share-of-voice snapshots, executive digests.
• Measurement: extract key messages, classify mentions, and generate wrap reports.

The six-part AI plan

• 1) Use cases: pick three that save the most hours each week. Script them end-to-end.
• 2) Data: clean your knowledge sources (style guides, boilerplates, past coverage). Add access controls.
• 3) Guardrails: define review steps, disallowed inputs, and approval rules. Log every run.
• 4) Tooling: standardize prompts, templates, and model settings inside your existing apps where possible.
• 5) Training: teach prompt patterns, fact-checking, and client safety. Use real account examples.
• 6) Measurement: track time saved, content quality, and client satisfaction. Report monthly.

Governance that keeps you out of trouble

• Human in the loop for all external content. No exceptions.
• No client confidentials in public tools without a signed policy and secure settings.
• Bias checks on media lists and sentiment outputs; sample and review weekly.
• Follow credible guidance such as the NIST AI Risk Management Framework and the UK ICO's AI and data protection guidance.

Tech stack sketch (keep it lightweight)

• Model access: API to a leading LLM plus a backup provider.
• Retrieval: store your vetted PR knowledge in a private, searchable index.
• Templates: prompt library for releases, pitches, summaries, and report sections.
• Integration: use your current docs, CRM, and monitoring tools; avoid new tabs if you can.

Training your team

• Teach prompt structure: role, goal, context, constraints, examples, output format.
• Create a "prompt wall" of proven patterns for common PR tasks.
• Run weekly 30-minute clinics: review wins, misses, and updated templates.
• Upskill doers first (account execs, writers), then roll out to strategists and analysts.

Need structured upskilling for different roles? Explore role-based options here: AI courses by job.

90-day rollout plan

• Weeks 1-2: pick three use cases, write prompts, define guardrails, set KPIs.
• Weeks 3-6: pilot with two client teams; track time saved and quality scores.
• Weeks 7-10: refine templates, fix edge cases, add light automation.
• Weeks 11-12: document process, brief legal/IT, and scale to more accounts.

Metrics that matter

• Time: hours saved per deliverable and per account each week.
• Quality: editor revisions per draft; client approvals on first pass.
• Throughput: assets produced per week without extra headcount.
• Risk: number of flagged outputs; zero external incidents.

Final note

Building your own AI tool is tough-and often unnecessary without the basics in place. Start with focused use cases, clean data, simple guardrails, and tight feedback loops. Ship small, measure, refine, then decide what's worth building.