Agentic AI for Enterprise Workflow Automation

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The full contents of Agentic AI for Enterprise Workflow Automation, slide by slide. Read it here, or use the viewer above for the designed version.

1. 02

   From Chatbots to Agents

   • Chatbots answer questions in one turn; agents pursue a goal across many steps
   • Agents call tools, check results, and revise their own plan mid-task
   • The shift moves AI from an information interface to an execution layer
   • Same underlying models — the difference is loop structure, not raw intelligence
   • Enterprise value moves from answering questions to completing transactions
2. 03

   What Makes AI 'Agentic'

   • Goal-directed: given an objective, not a fixed script of steps
   • Tool use: calls APIs, databases, and applications to take real action
   • Memory: retains context across steps and sessions to stay coherent
   • Planning and self-correction: breaks work into steps and adapts on failure
   • Autonomy is a dial, not a switch — scoped by policy, not left open-ended
3. 04

   Use Cases: Procurement and Finance Operations

   • Procurement: PO matching, vendor onboarding checks, contract clause extraction
   • Finance ops: invoice triage, three-way matching, exception routing, journal entry drafting
   • Common thread: high-volume, rules-plus-judgment work with clear source documents
   • Agents handle the routine path; humans review exceptions and edge cases
   • Best early candidates: high transaction volume, stable process, measurable cycle time
4. 05

   Architecture: Tools, Memory, Orchestration

   • Orchestration layer (e.g. LangGraph, state-machine frameworks) sequences and routes steps
   • Tool-calling layer connects the model to ERPs, APIs, RPA bots, and internal services
   • Memory: short-term context window plus a vector store for retrieval-augmented recall
   • Guardrail layer validates outputs and enforces policy before any action executes
   • Observability layer logs every tool call and decision for audit and debugging
5. 06

   Human-in-the-Loop Design

   • Not full autonomy: agents propose, humans approve above defined risk thresholds
   • Checkpoints placed before irreversible actions — payments, contract sends, record deletion
   • Confidence scoring routes low-certainty cases to a reviewer automatically
   • Review interfaces should show the agent's reasoning, not just its final output
   • Approval friction should scale down over time as accuracy is demonstrated, not assumed
6. 07

   Case: Invoice Processing Agent (Illustrative Example)

   • Representative scenario: a shared-services finance team processing vendor invoices
   • Agent extracts line items, matches to PO and receipt, flags mismatches
   • Clean matches post automatically; exceptions route to an analyst queue with context
   • Illustrative range: 60-80% of invoices resolved without human touch in similar deployments
   • Numbers are directional, not audited results — actual gains depend on data quality and scope
7. 08

   Productivity Gains, Measured Honestly

   • Industry-reported ranges for document-heavy back-office tasks: 30-50% cycle-time reduction
   • Gains concentrate in transaction processing, not judgment-heavy or novel work
   • Measure against cycle time, error/rework rate, and staff hours reallocated — not headcount alone
   • First 90 days typically show smaller gains as exception patterns are discovered and tuned
   • Treat any vendor's headline number as a starting hypothesis to validate on your own data
8. 09

   Security and Permission Boundaries

   • Agents run under scoped service identities, never a human's full credentials
   • Least-privilege access per tool: read-only where possible, write access explicitly granted
   • Sensitive actions (payments, data deletion, external sends) require a hard policy gate
   • All tool calls and data access logged immutably for audit and incident response
   • Segregate agent environments from production credentials via secrets management, not hardcoding
9. 10

   Failure Modes and Guardrails

   • Hallucinated data: mitigate with grounding in retrieved source documents, not model memory
   • Tool misuse or looping: cap retries and steps, add timeouts and circuit breakers
   • Silent drift: outputs degrade as upstream systems change without agent awareness
   • Guardrail pattern: validate against schema and business rules before any action commits
   • Design for graceful failure — agent escalates to a human rather than guessing
10. 11

    Integration with Legacy Systems

    • Most enterprise data sits behind ERPs, mainframes, and systems with no modern API
    • Integration paths: API wrappers, RPA bridges, or middleware/iPaaS connectors
    • Screen-scraping and legacy RPA remain necessary bridges, not a failure of design
    • Data normalization layer matters more than the agent's reasoning quality in practice
    • Sequence integrations by transaction volume and API readiness, not system age
11. 12

    Pilot Results (Representative Findings)

    • Representative pilot: 8-12 week scoped deployment on a single process, single team
    • Success criteria set upfront: cycle time, exception rate, and reviewer time saved
    • Common finding: initial exception rate higher than expected, narrows after two tuning cycles
    • User trust builds through visible reasoning and easy override, not accuracy alone
    • Pilot scope should be narrow enough to fully instrument, not broad enough to impress
12. 13

    Governance Considerations

    • Establish an AI governance owner and a standing review cadence, not a one-time sign-off
    • Document intended use, data sources, and known limitations per agent, before deployment
    • Align with existing model risk, data privacy, and internal audit frameworks
    • Track regulatory developments relevant to automated decision-making in your sector
    • Require a kill switch and rollback plan for every production agent
13. 14

    Scaling Strategy

    • Scale by process pattern, not by agent count — reuse orchestration and tool layers
    • Build a shared platform: common memory, logging, and guardrail infrastructure once
    • Prioritize processes with clean data and stable rules before ambiguous judgment work
    • Fund scaling from demonstrated pilot savings, not a fixed upfront budget
    • Invest in change management — process owners, not just IT, drive adoption
14. 15

    Roadmap

    • Near term (0-6 months): expand pilot scope, formalize governance, build shared platform
    • Mid term (6-18 months): extend to adjacent finance and procurement processes
    • Long term (18+ months): cross-functional agent orchestration across departments
    • Continuous: reassess guardrails and permissions as agent scope and autonomy grow
    • Success measured by sustained cycle-time and quality gains, not deployment count