AI Technology Radar / Scenario Planning / Strategic Foresight Playbook

Purpose: execution playbook for senior AI PM, AI Architect, CBAP-level BA, product governance, enterprise architecture, risk, compliance, security, procurement, and financial retail AI owners. Scope: monitor emerging AI technologies, regulatory shifts, vendor/platform changes, model economics, security threats, and workforce impacts, then convert them into portfolio options, experiments, architecture runway, watch/trial/adopt/hold decisions, and executive narratives. Use constraint: this is a learning, architecture, and portfolio artifact. It does not provide legal, compliance, procurement, audit, security, or investment advice. Formal enterprise decisions require accountable internal review.

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1. Target Audience

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2. Learning Objectives

Use this playbook to:

1. Stand up an AI foresight operating cadence in 30 days.
2. Define a signal taxonomy that separates hype from decision-grade evidence.
3. Run an AI technology radar with watch, trial, adopt, and hold decisions.
4. Build scenario narratives and connect them to assumptions, triggers, and portfolio options.
5. Maintain an assumption ledger with owners, freshness dates, thresholds, and response actions.
6. Convert signals into experiments, architecture runway, vendor watch, regulatory watch, and executive decisions.
7. Produce financial retail examples strong enough for portfolio review and senior interviews.

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3. Executive Summary

This playbook turns AI foresight into a repeatable operating model:

source scanning
-> signal intake
-> evidence scoring
-> assumption ledger
-> technology radar
-> scenario planning
-> option portfolio
-> experiment backlog
-> architecture runway
-> vendor / regulatory watch
-> executive decision narrative

The playbook is deliberately decision-centered. It avoids trend summaries, hype slides, and vendor-led roadmaps. Every material signal must answer:

• Which assumption is affected?
• Which decision becomes stale?
• Which option becomes more or less valuable?
• Which experiment should be funded or stopped?
• Which architecture capability must be prepared?
• Which control, eval, or vendor review must change?
• Which executive decision is requested?

The strongest outcome is not a perfect prediction. The strongest outcome is a portfolio that remains adaptive when model economics, regulation, security threats, vendor posture, customer trust, and workforce capacity change.

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4. Source Anchors

Access date for source discipline: 2026-06-30.

Source handling rules:

• Each source gets authority level, date, owner, affected artifacts, and review cadence.
• Official sources and internal production evidence carry more weight than commentary.
• Vendor marketing can open a watch item, but cannot justify adoption by itself.
• A source that affects a regulated use case must route to Legal, Compliance, Risk, Security, Privacy, Model Risk, or Procurement as applicable.
• The playbook records decisions and evidence; it does not replace accountable governance.

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5. Operating Model

5.1 Core Roles

5.2 Cadence

5.3 Minimum Artifacts

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6. Signal Taxonomy

6.1 Signal Classes

6.2 Hype vs Signal Rules

6.3 Signal Intake Template

# AI Signal Intake

Signal ID:
Date captured:
Source name:
Source URL or system:
Source authority:
Signal class:
Short description:
Affected use case:
Affected capability:
Affected assumption:
Evidence quality: L0 / L1 / L2 / L3 / L4
Materiality: low / medium / high
Reversibility impact: reversible / partially reversible / hard to reverse
Recommended action: watch / trial / adopt / hold / stop / refresh decision / open experiment / update control
Owner:
Decision due:
Trigger indicator:
Notes:

6.4 Evidence Quality Scale

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7. Technology Radar Model

7.1 Radar Rings

7.2 Radar Quadrants

7.3 Blip Card Template

# Radar Blip

Name:
Quadrant:
Ring:
Decision thesis:
Evidence summary:
Affected assumptions:
Affected capabilities:
Risks:
Controls or evals required:
Trigger to move inward:
Trigger to move outward:
Decision freshness date:
Owner:

7.4 Ring Movement Rules

7.5 Scoring Model

Use a simple 1 to 5 score. A high score does not automatically mean Adopt. It determines review priority.

Priority formula:

priority = strategic relevance + evidence quality + value potential + risk exposure + reversibility + time sensitivity

Use the score to decide review attention, not adoption outcome.

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8. Scenario Planning Model

8.1 Scenario Workshop Agenda

8.2 Recommended Scenario Set

8.3 Scenario Card Template

# Scenario Card

Scenario name:
Time horizon:
Core narrative:
What must be true:
Early indicators:
Affected business capabilities:
Affected architecture capabilities:
Affected vendors:
Regulatory implications:
Security implications:
Workforce implications:
Customer trust implications:
Options that gain value:
Options that lose value:
Architecture runway implications:
Decisions to accelerate:
Decisions to delay:
Executive message:

8.4 Scenario-to-Decision Matrix

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9. Assumption Ledger

9.1 Ledger Template

# Assumption Ledger Entry

Assumption ID:
Statement:
Decision supported:
Owner:
Confidence: low / medium / high
Evidence:
Evidence level: L0 / L1 / L2 / L3 / L4
Freshness date:
Trigger indicator:
Threshold:
Impact if wrong:
Response if triggered:
Related radar blips:
Related scenario:
Related experiment:
Related ADR or control:

9.2 Example Ledger

9.3 Decision Freshness Rules

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10. Trigger Indicators

10.1 Trigger Register

10.2 Trigger Response Menu

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11. Option Portfolio

11.1 Option Portfolio Template

# AI Option

Option name:
Option type: learning / platform / compliance / security / vendor / workforce / customer trust
Scenario coverage:
Assumption reduced:
Decision unlocked:
Cost to learn:
Potential value:
Risk reduced:
Reversibility:
Timebox:
Evidence output:
Stop rule:
Owner:

11.2 Portfolio Example

11.3 Option Portfolio Review Questions

1. Which options hedge more than one scenario?
2. Which options are cheap learning bets versus major commitments?
3. Which options reduce irreversible dependency?
4. Which options are only attractive under a single forecast?
5. Which options have clear stop rules?
6. Which options create architecture runway for multiple product teams?
7. Which options reduce regulatory, security, or workforce risk?

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12. Experiment Backlog

12.1 Experiment Template

# AI Foresight Experiment

Experiment name:
Assumption tested:
Scenario relevance:
Decision unlocked:
Method:
Data used:
Controls:
Success threshold:
Failure threshold:
Timebox:
Owner:
Evidence output:
Decision forum:

12.2 Example Experiment Backlog

12.3 Experiment Governance Rules

• Every experiment tests a named assumption.
• Every experiment has a decision forum before it starts.
• Every experiment has success and failure thresholds.
• Every experiment has risk controls, data controls, and evidence outputs.
• A demo without thresholds is not an experiment.
• An experiment that cannot change a decision should not be funded.

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13. Architecture Runway

13.1 Runway Canvas

# Architecture Runway Item

Capability:
Business problem supported:
Scenarios hedged:
Decisions enabled:
Current maturity:
Target maturity:
Dependencies:
Controls supported:
Vendor lock-in reduced:
Evidence needed:
Investment increment:
Stop rule:
Owner:

13.2 Recommended Runway

13.3 ADR Prompt

# AI Foresight ADR

Decision:
Context:
Signals considered:
Assumptions:
Scenarios affected:
Options considered:
Decision:
Why now:
Reversibility:
Vendor lock-in impact:
Regulatory impact:
Security impact:
Workforce impact:
Decision freshness date:
Trigger to revisit:

13.4 Build / Buy / Partner Implications

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14. Vendor and Regulatory Watch

14.1 Vendor Watch Template

# Vendor Watch Entry

Vendor:
Service:
Affected use cases:
Current dependency:
Contract / term signal:
Pricing signal:
Model lifecycle signal:
Security / privacy signal:
SLA / resilience signal:
Exit path status:
Materiality:
Owner:
Decision freshness date:
Recommended action:

14.2 Vendor Watch Topics

14.3 Regulatory Watch Template

# Regulatory Watch Entry

Source:
Jurisdiction:
Authority level:
Publication date:
Access date:
Affected products:
Affected AI systems:
Potential obligations:
Controls affected:
Evals affected:
Evidence affected:
Owner:
Applicability status:
Decision required:
Review date:

14.4 Regulatory Watch Topics

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15. Communication Cadence

15.1 Communication Matrix

15.2 Executive Narrative Template

# AI Foresight Executive Decision Memo

Decision requested:
What changed:
Why it matters:
Affected assumptions:
Affected portfolio items:
Affected architecture runway:
Affected controls or regulatory watch:
Affected vendor dependencies:
Affected workforce assumptions:
Options considered:
Recommended decision:
Evidence:
Risks and mitigations:
What would change our mind:
Decision freshness date:

15.3 Monthly Review Agenda

# Monthly AI Foresight Review

1. Material signals since last review
2. Radar movements and hold decisions
3. Assumption ledger changes
4. Trigger indicators crossed or nearing threshold
5. Experiment results and new experiments
6. Option portfolio funding decisions
7. Architecture runway changes
8. Vendor watch and regulatory watch
9. Workforce and adoption signals
10. Executive decisions and owners

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16. Financial Retail Case Pack

16.1 Agentic Banking Operations

Radar posture:

Experiment:

• Sandbox payment exception workflow.
• Simulate low-risk cases.
• Red-team tool permissions.
• Require complete audit trail.
• Human approval before any customer or value movement.

Architecture runway:

• tool permission service;
• workflow state machine;
• SoD policy enforcement;
• runtime monitoring;
• kill switch;
• evidence graph.

16.2 GenAI Contact Center

Radar posture:

Signals:

• model summarization quality improved;
• override burden varies by intent;
• customer complaints require traceable evidence;
• workforce trust depends on source snippets.

Decision:

• scale low-risk summarization;
• keep regulated scripts in trial;
• invest in quality telemetry and supervisor review.

16.3 Digital Identity and Synthetic Fraud

Radar posture:

Trigger indicators:

• confirmed fraud cases using deepfake voice;
• onboarding manual review rate;
• credential issuer coverage;
• false positive impact on legitimate customers.

16.4 AML Typology Evolution

Radar posture:

Experiment:

• Build eval set from typology memos and past cases.
• Test retrieval quality and citation accuracy.
• Require investigator attestation.
• Measure time saved and quality improvement.

16.5 Open Banking Personal Finance AI

Radar posture:

Decision:

• trial insights with clear disclosure;
• monitor opt-out and complaints;
• require data lineage and consent audit.

16.6 Regulatory Reporting

Radar posture:

Experiment:

• Draft report sections from approved source documents.
• Require every generated claim to map to evidence.
• Keep human attestation and final approval.

16.7 Model Provider Changes

Radar posture:

Trigger indicators:

• pricing change;
• data-use term change;
• model retirement;
• outage;
• benchmark parity from another provider;
• regional availability change.

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17. Anti-Patterns

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18. Interview Answers

18.1 30-Second Answer

I build an AI technology radar as a decision system, not a trend list. Each signal is classified by source authority, evidence quality, affected assumption, materiality, reversibility, owner, and trigger. The radar uses watch, trial, adopt, and hold states, and it feeds scenario planning, portfolio options, experiment backlog, architecture runway, vendor watch, regulatory watch, and executive decisions.

18.2 2-Minute Answer

In financial retail, AI changes too quickly for a static roadmap. I would create a foresight architecture with three layers. First, a signal taxonomy monitors model capability, economics, vendor changes, regulation, security threats, workforce impacts, customer trust, open banking, fraud, and resilience. Second, a technology radar converts signals into watch, trial, adopt, or hold decisions with evidence thresholds. Third, scenario planning tests assumptions against futures such as model commoditization, regulatory tightening, agentic operations, fraud acceleration, and workforce disruption. The output is practical: option portfolio, experiments, architecture runway, vendor and regulatory watch, and executive memos. For example, I might trial agentic payment exceptions in a sandbox, adopt evidence graph runway, and hold autonomous customer-impacting actions until permissions, audit trail, and human approval are proven.

18.3 Executive Version

The reason we need an AI radar is not to track every trend. It is to keep our decisions fresh. Model cost, vendor terms, regulations, security attacks, and workforce capacity can change faster than our annual roadmap. The radar tells us which assumptions changed, which options to fund, which decisions to defer, and which architecture investments preserve future choices.

18.4 Architect Version

The architecture value is option preservation. Scenario planning tells us which futures stress our assumptions. The architecture runway then invests in capabilities that help across multiple futures: AI gateway, EvalOps, evidence graph, tool permissions, human oversight workflow, vendor inventory, cost observability, and fraud intelligence. Each runway item has a decision it enables and a stop rule.

18.5 BA Version

The BA role is to make uncertainty explicit. I capture signals as assumptions, map them to affected processes, requirements, controls, stakeholders, and acceptance criteria, and define trigger thresholds. This prevents ambiguous statements like "employees will adopt" or "regulation should be fine." Instead, each assumption has evidence, owner, freshness date, and response action.

18.6 Follow-up: How do you avoid hype?

I require decision relevance. A signal is not important because it is popular. It is important if it changes a named assumption, option value, risk exposure, reversibility, timing, or stakeholder decision. Hype can enter Watch, but it cannot move to Trial or Adopt without evidence and thresholds.

18.7 Follow-up: How do you handle vendor lock-in?

I identify lock-in across contracts, data flows, logs, prompts, evals, APIs, tool schemas, approvals, and skills. Then I decide where abstraction is worth the cost. For high-risk or high-volume workloads, I maintain portable evals, model inventory, exit terms, and an AI gateway where it creates real option value.

18.8 Follow-up: What makes scenario planning useful?

Scenario planning is useful only if it changes decisions. A scenario must produce indicators, affected assumptions, options, architecture implications, and executive choices. If it only produces a narrative, it is strategy theater.

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19. Portfolio Exercise

19.1 Scenario

Build a foresight package for a financial retail institution with:

• GenAI contact center pilot;
• AML investigator copilot discovery;
• payment operations automation proposal;
• open banking personal finance roadmap;
• digital identity fraud concerns;
• primary model provider dependency;
• executive pressure to show AI productivity within two quarters;
• risk team concern about regulatory evidence and customer harm.

19.2 Deliverables

1. Source registry with at least 12 sources.
2. Signal taxonomy and signal intake log with at least 15 signals.
3. Technology radar with at least 20 blips across six quadrants.
4. Assumption ledger with at least 10 assumptions.
5. Trigger indicator register with at least 10 triggers.
6. Three scenario cards: model commoditization, regulatory tightening, and fraud acceleration.
7. Option portfolio with at least eight options.
8. Experiment backlog with at least six experiments.
9. Architecture runway with at least eight capabilities.
10. Vendor watch and regulatory watch entries.
11. Executive decision memo requesting fund, stop, scale, or hold decisions.

19.3 Scoring Rubric

19.4 Portfolio Narrative

I designed an AI foresight operating model for a financial retail portfolio. It combines a signal taxonomy, AI technology radar, scenario planning, assumption ledger, trigger indicators, option portfolio, experiment backlog, architecture runway, vendor watch, regulatory watch, and executive decision memo. The artifact demonstrates that I can turn AI uncertainty into governed decisions, not hype tracking.

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20. 30-Day Execution Plan

Week 1: Foundation

Week 2: Radar

Week 3: Scenarios and Options

Week 4: Governance and Executive Narrative

Days 21-30: Stabilize Cadence

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21. Self-Review Checklist

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22. Final Operating Principle

The AI technology radar is useful only when it changes decisions.

The complete loop is:

watch signals
-> test assumptions
-> move radar blips
-> fund options
-> run experiments
-> build architecture runway
-> refresh vendor and regulatory posture
-> communicate executive decisions
-> revisit when triggers fire

AI foresight is not about being first to repeat a trend. It is about knowing which decisions are fresh, which are stale, which are reversible, which are becoming locked in, and which evidence should change the portfolio.