Opportunity & Use-Case Portfolio
Where AI or automation can create measurable value and where it should not be used.
Capability domain
AI & Automation
Redesign how work is divided among people, software, models, workflows, and increasingly autonomous agents.
01 — Domain thesis
AI & Automation redesigns how work is divided among people, software, models, workflows, and increasingly autonomous agents.
AI & Automation is not primarily about adopting tools. It is about intentionally redesigning work and enterprise capability while increasing governance and control as systems gain intelligence, system access, and autonomy.
02 — Transformation questions
Questions the enterprise should be able to answer.
These questions define the capability—not an intake process.
- 01Which problems should use deterministic automation rather than AI?
- 02Where can AI create measurable business value?
- 03Which decisions must remain human?
- 04Which actions may AI recommend, initiate, or execute?
- 05What data, tools, and systems may an AI system access?
- 06How are models, agents, and workflows evaluated before production use?
- 07Which controls increase as autonomy increases?
- 08How are human override, escalation, and containment designed?
- 09How will AI-enabled work change roles and operating processes?
- 10How is performance measured after deployment?
03 — Capability model
AI & Automation as an enterprise capability
A reusable lens for understanding the disciplines that must operate together.
Automation Patterns
Rules, workflow, RPA, predictive models, generative AI, agents, and agentic workflows selected deliberately.
Human + Machine Operating Model
Tasks that remain human-led, become machine-assisted, are machine-executed, or require human approval.
Enterprise Integration
How AI interacts with governed data, APIs, tools, workflows, knowledge, and systems of record.
Lifecycle & Evaluation
Design, testing, evaluation, deployment, monitoring, change, and retirement.
Governance & Controls
Risk classification, permissions, oversight, auditability, intervention, and responsible use.
Measurement
Business outcomes, quality, reliability, risk, efficiency, adoption, and operational impact.
Automation patterns
Rules → Workflow → RPA → Predictive ML → Generative AI → AI Agents → Agentic Workflows
This is not a mandatory maturity sequence. Enterprises may use several forms simultaneously. As probabilistic reasoning, dynamic decisions, context, system access, and autonomy increase, evaluation, governance, security, observability, and human control must increase with them.
- 01
Rules
Deterministic logic for stable, explicit decisions.
- 02
Workflow
Structured coordination of tasks, systems, and approvals.
- 03
Robotic Process Automation
Repeatable execution across existing interfaces.
- 04
Predictive / Machine Learning
Probabilistic classification, forecasting, and decision support.
- 05
Generative AI
Creation, synthesis, interpretation, and conversational assistance.
- 06
AI Agents
Goal-directed systems that reason, use tools, and take bounded actions.
- 07
Agentic Workflows
Coordinated people, agents, tools, and deterministic controls across end-to-end work.
04 — Evolution & maturity
From fragmented practice to intentional enterprise capability
Maturity is visible in how decisions, evidence, controls, and operating behavior become connected.
- 01
Tool experimentation
Isolated use emphasizes access to models and point solutions.
- 02
Use-case discipline
Opportunities are selected by value, fit, risk, and evidence.
- 03
Integrated workflows
People, automation, AI, data, tools, and systems operate through designed workflows.
- 04
Governed capability
Evaluation, identity, permissions, observability, intervention, and lifecycle ownership support production use.
05 — Connections
A connected enterprise transformation capability
Governance asks who decides and controls. Architecture defines how the enterprise system fits together. AI & Automation examines how work changes when intelligence enters that system.
Strategy
Determines where AI and automation should create business value.
Explore domainGovernance
Defines acceptable use, accountability, oversight, and lifecycle controls.
Explore domainArchitecture
Provides integration, platforms, models, tools, context, and system access.
Explore domainData
Provides trusted information and enterprise knowledge.
Explore domainSecurity
Controls identity, authorization, data and tool access, monitoring, and containment.
Explore domainPeople & Change
Redesigns jobs, roles, skills, decision rights, and human-machine collaboration.
Explore domainExecution
Turns concepts into production systems and repeatable engineering capability.
Explore domain06 — Frameworks & artifacts
AI & Automation operating model
AI and automation convert trusted context, controlled tools, and designed workflows into augmented decisions and coordinated work.
Inputs
- Qualified use cases
- Trusted context and data
- Approved tools and controls
Decisions
- AI versus deterministic automation
- Human approval points
- Evaluation thresholds
Outputs
- Evaluated AI capabilities
- Automated workflows
- Operational evidence
Applied in Practice
Mapspring
AI-assisted implementation operating inside a controlled engineering system with explicit evaluation and human accountability.
See Mapspring in practiceFramework and Lab boundary
Frameworks define the model. Labs test implementations.
Frameworks define how the enterprise should think about AI & Automation as a transformation capability. Labs test coding-agent workflows, MCP, orchestration, tool use, evaluation, autonomous workflow prototypes, and other AI-native implementations.
07 — Labs & experiments
Applied exploration
Published
Agentic Workflows Lab
An active exploration of useful agent coordination, controlled tools, approvals, and human oversight.
Explore08 — Insights & related work
Published learning
Published
Agentic AI Security & Controls
Practical guidance on agent identity, authorization, tools, data boundaries, human approval, observability, and containment.
ExploreExplore related work
Continue through connected domains and frameworks.
AI & Automation intentionally redesigns work and enterprise capability while increasing governance and control as systems gain intelligence and autonomy.
Framework
Enterprise Agentic Transformation
A practical enterprise framework aligning agentic engineering, human and AI work, platform architecture, governance, security, and measurable outcomes.
ExploreFramework
Enterprise Transformation Process
A six-phase process for discovering, assessing, designing, building, operating, and evolving transformation.
ExploreLab
Agentic Workflows Lab
An active exploration of useful agent coordination, controlled tools, approvals, and human oversight.
ExploreInsight
Agentic AI Security & Controls
Practical guidance on agent identity, authorization, tools, data boundaries, human approval, observability, and containment.
Explore