Executive summary
AI isn't a “project” — it's a capability. Companies that get value from AI do three things well:
- Pick use cases that reduce cost or increase revenue (not demos).
- Treat data as product (quality, access, governance).
- Ship in small, safe iterations with clear success metrics.
This article is a practical guide for leaders who want outcomes: lower handling time in support, higher conversion in sales, fewer operational surprises, and faster decision-making.
What AI is actually good at (today)
AI is strongest when the problem is one of these:
- Text-heavy work: summarizing tickets, drafting emails, extracting entities from documents.
- Pattern recognition: forecasting demand, anomaly detection, risk scoring.
- Recommendations: personalized content, next-best action, product suggestions.
- Computer vision: quality inspection, OCR, compliance checks, safety monitoring.
AI is not a magic replacement for product thinking. If a workflow is unclear, undocumented, or constantly changing, AI will amplify the chaos.
The 4 “golden” business use cases (and how to validate them)
1) Customer support acceleration
Goal: Reduce average handle time and improve first-contact resolution.
What to build:
- A knowledge-assisted agent that suggests answers based on internal docs
- Ticket summarization + auto-tagging
- Escalation routing (predict urgency, product area, and sentiment)
How to validate:
- Baseline your current metrics (AHT, FCR, CSAT).
- Run a shadow mode pilot: AI suggests, humans decide.
- Measure improvements per team, not just overall averages.
2) Sales enablement and lead qualification
Goal: Increase pipeline quality and shorten time-to-first-response.
What to build:
- Lead scoring from CRM + web analytics + email engagement
- AI-generated account briefs before calls
- Personalized outreach drafts aligned to your ICP and product messaging
How to validate:
- Compare conversion by cohort (AI-assisted vs control).
- Track “time saved” and “quality lift” (meeting booked rate).
3) Back-office automation (documents + workflows)
Goal: Reduce manual processing and error rates.
What to build:
- Invoices/receipts extraction (OCR + validation rules)
- Contract clause detection and risk flags
- Automated approvals with audit trails
How to validate:
- Pick one document type with clear rules.
- Measure error rate, throughput, and rework cost.
4) Operations and risk monitoring
Goal: Detect issues earlier than humans can.
What to build:
- Anomaly detection on payments, inventory, or system logs
- Forecasting for demand and staffing
- Predictive maintenance signals (where applicable)
How to validate:
- Define what “bad” looks like (false positives are expensive).
- Start with alerts + explanations, then move toward automation.
The real blockers (and how to remove them)
Data quality: the silent killer
Most AI programs fail because the organization can't reliably answer:
- Where is the data?
- Who owns it?
- Is it accurate enough?
- Can we access it safely?
Fixes that work:
- Create a single source of truth for key entities (customers, orders, products).
- Add data contracts between services (schema + expectations).
- Build monitoring for freshness, null rates, and drift.
Governance: you need rules before scale
If you handle customer data, AI must be secure-by-default:
- Access control: least privilege for prompts and retrieved documents
- PII redaction: detect and remove sensitive data before storing or training
- Logging: audit what went in and what came out
- Human-in-the-loop: for high-risk decisions (finance, HR, compliance)
Treat AI outputs like any other system output: validate, monitor, and continuously improve.
A simple roadmap that works for most companies
Phase 1: Prove value (2–4 weeks)
- Pick one use case with clear metrics.
- Build a thin slice: retrieve trusted docs, generate a draft, track acceptance.
- Ship to a small internal group.
Phase 2: Productize (4–8 weeks)
- Add role-based access, logging, and guardrails.
- Improve relevance with feedback loops.
- Expand to adjacent workflows (e.g., support → sales handoff).
Phase 3: Scale (ongoing)
- Standardize patterns (prompting, retrieval, evaluation, monitoring).
- Create a reusable internal “AI toolkit” so teams ship faster.
What ROI looks like (realistically)
Good AI ROI usually shows up as:
- Hours saved per week per team (measured, not estimated)
- Faster cycle times (support resolution, document processing)
- Higher conversion or lower churn (tracked by cohort)
- Reduced risk (fewer fraud losses, fewer compliance misses)
The best AI projects are not the most complex—they're the ones embedded into a workflow people already use.
How Foreach approaches AI projects
We build AI systems the same way we build software: strategy, design, engineering, quality, and security.
- We start with metrics and workflows (not model selection).
- We design for reliability: evaluation, guardrails, and monitoring.
- We ship fast with iterative improvements.
If you want AI that moves your business forward, start with a use case and a baseline — and we’ll help you build from there.