AI Combined with Automation: The Perfect Marriage for Scalable, Intelligent Operations
This on-demand webinar, recorded January 8, 2026, shows how pairing AI with automation creates throughput without sacrificing control. The idea is simple: AI decides; automation does. That split turns complex work into consistent outcomes at scale.
The recording is available after a quick login or registration.
What you'll learn from the session
- Where AI adds judgment (classification, routing, summarization) and where automation executes (RPA, APIs, workflows).
- How to design an operations stack that scales without chaos: data, AI services, automation, and orchestration.
- Why DataOps is the foundation for reliable AI outputs and auditability.
- Practical guardrails: policy, access, human-in-the-loop, and activity logging.
- How to measure impact with hard metrics: cost-to-serve, cycle time, first-time-right, and SLA adherence.
- Change tactics to get frontline teams using AI assistance daily, not occasionally.
- Build vs. buy: where to use out-of-the-box tools and where custom models make sense.
Quick start plan for Operations leaders
- Week 1-2: Pick three high-volume, rules-heavy workflows with clear SLAs. Baseline current metrics.
- Week 3-6: Add AI for triage or document understanding; automate the handoffs with APIs or RPA. Keep a human in the loop.
- Week 7-12: Expand to two more workflows, standardize prompts and playbooks, and publish weekly scorecards.
- Quarter 2: Centralize reusable components (prompts, connectors, templates). Stand up an Ops-AI council for approvals and escalations.
Architecture pattern that works
- Data layer: Clean, tagged data; event logs; document stores. Version everything.
- AI services: Models for classification, extraction, summarization, forecasting, and agent assist.
- Automation layer: RPA for legacy screens; APIs and workflow engines for speed and reliability.
- Orchestration/control: Routing, retries, SLAs, and human review queues.
- Observability: Prompt/version tracking, latency, failure reasons, and audit trails.
- Memory: For multi-step agents, use a persistent store to keep context across tasks.
Metrics that matter
- Cost-to-serve and cost per case
- Cycle time and queue aging
- First-time-right (or first-contact resolution)
- SLA hit rate and exception rate
- Average handle time and agent assist adoption
- Quality pass rate, NPS/CSAT (where relevant)
Risk and governance basics
Set clear rules for data use, model access, and human sign-off. Keep prompts, versions, and outputs traceable. For a solid frame of reference, review the NIST AI Risk Management Framework.
Where AI + automation pays off fast
- Customer operations: Email and chat triage, intent routing, response drafts, and quality monitoring.
- Finance ops: Invoice capture, three-way match, dispute classification, collections outreach.
- Supply chain and logistics: Forecasting, exception handling, and last-mile task automation (pair well with robotics).
- Insurance and claims: FNOL intake, document review, fraud flags, and claims correspondence.
- IT and security: Alert deduplication, enrichment, and guided response with playbooks.
Related on-demand sessions
- DataOps Can Build the Foundation For Your Generative AI Ambitions (January 6, 2026)
- From Complexity to Clarity: AI + Agility Layer for Intelligent Insurance (December 11, 2025)
- Turning a Hacker's Toolkit Against Them (December 11, 2025)
- CMS Buyer's Briefing: A Live Look at What's Next in AI-Driven Platforms (December 11, 2025)
What's new in AI Ops this week
- Agentic AI scaling requires new memory architecture (January 7, 2026)
- Optimism for AI-powered productivity: Deloitte (January 7, 2026)
- Grab brings robotics in-house to manage delivery costs (January 7, 2026)
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