Preamble
“The problem is not making decisions—it’s making decisions that stay wise when the world changes.”
—Anonymous strategist, 2024
In boardrooms, think tanks, and policy units, decision-makers are beginning to whisper the same thing: traditional tools are showing their age. SWOT analysis still has its place. MCDA frameworks remain sound. But the terrain is different now—quicker, slipperier, more entangled with data than ever.
Enter artificial intelligence. And not just any AI, but large language models (LLMs), agent-based platforms, and GPT-powered ideation engines. The arrival of AI into the decision-making process is not just an upgrade. It is a paradigm shift.
This piece explores the evolving landscape of AI-Augmented Decision Support Tools (DSTs), the enduring role of classical strategy frameworks, and why the strategic mind of tomorrow will be a hybrid—part human, part machine, fully adaptive.
References
I have included a link to documents referred to in this post in a repository found here : AI Decision Support (these are all outlines)
🧵 AI-Augmented Decision Support Tool: Document Summaries
- 📘 Strategic Decision Analysis Framework (SDAF)
A comprehensive methodology guiding decisions from scoping to post-implementation, integrating stakeholder mapping, MCDA, risk analysis, and continuous improvement. - 📗 Software Requirements Specification (SRS)
Outlines the technical and functional blueprint for the AI-Augmented DST MVP, including GPT integration, MCDA tools, stakeholder interfaces, and explainability features. - 📙 Vision Document
Defines the mission, personas, feature roadmap, and strategic goals of the DST—culminating in a federated, agent-based strategic co-pilot for complex decisions. - 📒 Business Case
Makes the financial, operational, and strategic argument for developing the DST via a phased Buy → Build → Agent approach, backed by SWOT, PESTLE, and ROI analysis. - 📕 Business Canvas for AI-Augmented DST
Presents the business model, lean canvas, and strategic alignment for the DST, targeting foresight teams, analysts, and innovation officers across sectors. - 📓 Canvas and Discovery Overview
Explains key canvas types (Business, Lean, Product, Software) and contrasts Business, Digital, and Software Discovery processes relevant to DST development. - 📔 SIC Code Reference Document
Summarizes global industry classification systems (UK SIC, NAICS, ISIC, NACE) to inform DST market segmentation and regulatory alignment.
🧩 What’s Broken—And What’s Not
The current frameworks, methodologies and tools still work (see section Comprehensive Guide to Decision Analysis Tools and Methodologies in Strategic Decision Analysis Framework (SDAF) )
I suggested a Strategic Decision Analysis Framework (SDAF) to capture the possible developmental structure and as reference for further development .
As documented in its seven-phase structure—from problem scoping to continuous improvement—it offers a systematic, stakeholder-conscious, and risk-aware methodology for navigating complex choices. These foundations do not need to be replaced. They need to be supercharged.
Traditional tools like SWOT or MCDA suffer from three limitations in today’s context:
- Manual complexity—Scoring, weighting, and modelling take time.
- Static outputs—No built-in feedback or contextual adaptation.
- Data blindness—They assume inputs are complete, timely, and human-generated.
AI does not discard these methods; it retools them:
- A GPT-powered ideation module can generate strategic options with nuance and breadth.
- Scenario planners informed by LLMs simulate consequences across markets or stakeholder groups.
- MCDA dashboards now visualize criteria impacts in real-time, with AI justifying its logic in plain English.
🧠 What is the Analysis: A Blueprint for the Future
Let us draw from the outline analysis: Vision Documents, Business Cases, SRS specifications. Across these files, a clear narrative emerges: we are not building another decision tree—we are architecting a cognitive co-pilot.
The Core Features of AI-Augmented DSTs:
- SWOT + MCDA + GPT = MVP
The MVP combines traditional analysis with AI-generated insights. Users input criteria, and the tool returns not just rankings, but AI-supported reasoning. - Multi-role Support
From business analysts to product designers, the tool adapts to the user’s context—providing relevant modules and outputs. - Visual + Verbal Intelligence
A decision is not just a chart; it is a story. The tool outputs dashboards and narrative explanations. Interpretability is not optional; it is embedded. - Explainability and Compliance
Audit logs, rationale exports, and GDPR-aligned privacy features ensure traceability—essential in regulated sectors like finance or health.
And the strategic fit? The tool slots directly into SDAF, aligning each AI feature with a phase of the framework: NLP for stakeholder mapping in Phase 1, GPT ideation in Phase 3, predictive risk models in Phase 4.
🛠 What Will It Take to Build This?
Spoiler: more than code.
According to the Software Requirements Specification (SRS), a mature AI-based DST demands:
- Modular Architecture
React front-end, Python backend, GPT APIs, and a PostgreSQL core—wrapped in a seamless UX. - Role-based access and scenario wizards
Because a policy advisor does not think like a product manager, and should not have to. - Scalable Infrastructure
The MVP is designed to support 100+ concurrent users, but the long-term vision includes federated agents trained on proprietary strategy data. - Security + Governance
Encryption, audit trails, and AI Act alignment are not afterthoughts—they are embedded in the build roadmap.
Ideation, MVP or Prototype
Is this worth exploring? You can create a Custom GPT for further exploration and analysis. I created Basic Ai augmented Decision Support Tool Custom GPT on Chat GPT for ideation using the requirements and vision document. It took 15 minutes to create and 30 minutes to improve. An example of the Prompt and output is in the appendices.
Custom GPT might be not the solution but it helps with ideation an example : Business Template Tutor Took me 10 minutes . I can return and improve and it has configuration, release, and version control (You have a choice for personal or public use) . The document repository also has multiple tools suggestions for Ai assisted ideation
Note: My cheat code is using role-based AI prompts and documentation: Business cases, or Vision and Software requirement documents (or other relevant documents).
💸 Why This Matters for Strategists, Not Just Technologists
This is not just a tool for IT teams to tinker with. It is a weapon in the strategist’s arsenal.
Use case: Creating a business canvas an entrepreneur means that the process can be interactive, the primary beneficiaries are strategic foresight teams, analysts, and innovation officers in mid-to-large firms. These are people burdened by ambiguity, required to justify trade-offs, and tasked with balancing stakeholder interests in rapidly shifting environments.
With AI-augmented DSTs it is hoped that:
- Decision-making time drops by 40%.
- Foresight expands from a single scenario to real-time option modelling.
- Strategic alignment is no longer anecdotal—it is mapped, scored, and explained.
📊 Why Traditional Tools Are Losing Ground
To put it plainly: Excel is not built for foresight. Power BI can visualize data, but not generate or explain decisions(it’s being updated). Notion can house a SWOT table, but not simulate a future state under three ESG scenarios.
Competitor tools are catching up, but they lack one thing: integration.
The DST envisioned here is built around strategy—not retrofitted. It integrates stakeholder mapping, decision modelling, and explainability from the ground up.
🧠 The Strategic Mind of Tomorrow
In the not-too-distant future, we may not just have a decision tool. We will have a decision partner—one that:
- Synthesizes 200 pages of reports into a single strategic brief
- Flags inconsistent logic in a risk assessment
- Suggests a more inclusive stakeholder scenario
- Documents every step for audit, learning, and iteration
This is not science fiction. The technical and business architecture exists today. The only question is whether strategy leaders will embrace the build—or wait for a vendor to do it for them.
🚀 Final Thought: Strategy Is Still Human—But Smarter
The point of AI is not to replace judgment. It is to sharpen it.
In a complex world, the strategist is still responsible for direction, values, and purpose. But the tools have changed. And those who recognize that shift—and build accordingly—will not just make better decisions.
They will make the future.
Appendices
Basic Ai augmented Decision Support Tool Custom GPT
AI Prompt
Role: You are a social ethicist and AI developer analysing the regulatory gaps in AI governance. Task: Create a decision matrix that evaluates the impact of: The lack of sufficient AI regulation in various industries. The implementation of outdated ethical frameworks in AI governance. The potential outcomes of continued misalignment between AI progress and legislation. Mitigation strategies to address risks as AI technology advances faster than legal frameworks can adapt. Expected Output: A structured decision matrix outlining risks, consequences, and solutions. Comparative insights between existing and proposed ethical models. Recommendations for proactive regulatory adaptation
Answer
🧠 AI Governance Decision Matrix
⚖️ Comparative Analysis of Ethical Frameworks
🛡️ Recommendations for Proactive Regulatory Adaptation
1. Adaptive Regulatory Infrastructure
o Use Regulatory Sandboxes to test AI systems with real-world feedback loops.
o Adopt Dynamic Licensing—renewable certifications based on ongoing audits.
2. Embed AI Ethics-by-Design
o Make pluralistic ethics (rights, outcomes, care) a requirement in the SDLC.
o Mandate third-party Ethics Risk Reviews akin to security audits.
3. Institutionalize Participatory Governance
o Fund AI Citizen Assemblies for high-impact domains (e.g., policing, credit scoring).
o Include Ethics Advocates in regulatory agencies.
4. Global Coordination
o Align standards with OECD AI Principles, EU AI Act, and GPAI.
o Promote reciprocal AI safety accords for transnational platforms.
5. Real-time Monitoring & Redress
o Require post-deployment impact tracking and citizen redress mechanisms.
o Invest in AI observatories to detect emergent harms (akin to environmental monitoring).
