AI Framework: Transforming Ideation into Impact

AI is no longer just a buzzword; it’s a transformative force. Whether it’s traditional AI techniques like predictive analytics or Generative AI (GenAI) creating text, images, or entire conversations, organizations must now harness its power effectively to solve real-world problems.

Yet, one challenge persists: how do you transition seamlessly from identifying an AI opportunity to delivering results? In response, I’ve designed a step-by-step AI use-case framework that’s both robust and adaptable—applicable to traditional AI and GenAI use cases alike.

This framework ensures that every AI journey, no matter the type of technology involved, starts strong, stays relevant, and evolves with time. Below, I’ll walk you through this nuanced process, with practical tools and insights to help you stay ahead in this AI-powered era.

1. Discovery & Ideation: Start with the Right Questions

At the heart of any AI initiative lies the problem it’s meant to solve. This first phase ensures alignment between business goals and AI’s capabilities, setting the stage for impactful solutions. Let's not do AI for the sake of it and provide actual business value!

Key Steps:

Understand the Problem and Personas: Imagine you’re tasked with optimizing a customer support system. Start by mapping customer journeys, identifying pain points (e.g., long response times), and creating personas that capture the needs of support agents and end-users alike.

• Tools: Use jobs-to-be-done to map customer journey on Miro or FigJam

Hypothesise Solutions: Brainstorm hypotheses for both predictive and generative use cases. Use visual frameworks to organise ideas and frame AI’s role in addressing these pain points. For example, a hypothesis for traditional AI could involve using predictive analytics to preempt support issues, while for GenAI, you might explore chatbots that handle complex inquiries.

• Tools: Use opportunity solution tree (OST) to map solutions & experiments to alleviate pain points

Prioritize Use Cases: Create a prioritization matrix that evaluates use cases on business value, feasibility, and alignment with strategy. Include a new layer for GenAI-specific considerations, like data requirements for training models or the ethical implications of generated outputs.

• Tools: Develop a custom value vs complexity prioritisation matrix. Value could entail business and use value. Complexity includes costs, effort, talent and risks etc.

Rapid Prototyping: Before committing significant resources, simulate your solution. For instance, create mockups of a GenAI-powered chatbot interface or a low-code predictive model dashboard.

• Tools: Bubble (low-code prototyping), Figma (mock-ups), OpenAI Playground, Gradio, Streamlit etc.

Engage Stakeholders: Involve key stakeholders early in the process to ensure alignment with business goals and problem context. Regular collaboration fosters clarity and a shared vision for success.

• Tools: Use OKRs

Outcome:

This phase ends with a clear roadmap of prioritized AI opportunities, supported by prototypes and stakeholder alignment.

2. Data Problem & Feasibility: Grounding Ambition in Reality

No AI solution can succeed without the right data. This phase focuses on understanding the data ecosystem, bridging gaps, and ensuring compliance with regulations.

Key Steps:

Data Assessment: Understand data gap. Is your data sufficient and reliable? Go beyond exploration to perform a data quality audit, assessing completeness, consistency, and reliability. Traditional AI might demand structured customer interaction logs, while GenAI may require vast, high-quality datasets like conversational transcripts or labeled imagery.

• Tools: Great Expectations (data quality), Pandas/Excel (data audits), Alteryx (data prep), Hugging Face Datasets for exploring generative data sources.

Data Acquisition and Enrichment: Include external data sources if required, such as third-party APIs, open data, or partnerships. Consider augmenting synthetic data to enrich data. For traditional AI, this may mean combining internal CRM data with external APIs. For GenAI, consider partnerships for access to proprietary datasets or using pre-trained models like GPT.

• Tools: Snowflake (data sharing), Google BigQuery, RapidAPI (external API integration), Hugging Face Datasets, Gretel AI (synthetic data) etc.

Regulatory and Ethical Alignment: Whether deploying traditional models or generative tools, compliance is critical. Assess adherence to GDPR, CCPA, or similar laws, and address specific GenAI concerns, such as bias in outputs or IP infringement.

• Tools: Trustworthy AI frameworks (Microsoft Responsible AI), OneTrust (GDPR compliance).

Cost Analysis: Evaluate the financial feasibility of the AI project by assessing costs versus expected ROI. This assessment ensures resources are allocated effectively. For instance, integrating external datasets might be expensive but could unlock critical insights that justify the investment.

Define Success Metrics: Establish KPIs for model performance and business impact. Traditional AI metrics (e.g., accuracy) and GenAI-specific metrics (e.g., BLEU for text, FID for images). For business impact, it could be your North Star Metric (NSM)

• Tools: MLFlow, BLEU calculators etc.

Revise Prioritization Matrix (Optional): Based on new insights from data gaps, regulatory hurdles, or costs, revisit and refine the prioritization framework to reflect realistic outcomes.

Outcome:

This stage delivers a Data Readiness Report, complete with insights into data quality, regulatory compliance, cost analysis, and an actionable data acquisition plan.

3. Solution Development: From Concept to Reality

With clear priorities and a strong data foundation, it’s time to build and validate the AI solution. This phase focuses on technical development, risk mitigation, and integration planning.

Key Steps:

Feature Engineering or Prompt Design: Leverage domain expertise to create meaningful features that enhance model performance. For traditional AI, derive meaningful features (e.g., user segmentation). For GenAI, focus on crafting effective prompts or fine-tuning models to align with your use case.

• Tools: Python (Pandas, Scikit-learn), Featuretools, Hugging Face (prompt engineering).

Model Selection and Training: Use standardized frameworks to develop or fine-tune models. Traditional AI might rely on supervised learning techniques, while GenAI could involve training on domain-specific datasets for enhanced relevance.

• Tools: TensorFlow, PyTorch, Hugging Face Transformers, OpenAI.

Integration Planning: Ensure the AI solution fits seamlessly into existing workflows and systems. Clear documentation and close collaboration with software engineers are key.

• Tools: Postman for API validation, Zapier for workflow automation.

Prototyping and Validation: Design and test user-friendly prototypes of the AI system, ensuring alignment with user needs. Pilot projects help validate effectiveness before full-scale deployment. For example, pilot a churn prediction model with a subset of customers and validate its impact on retention strategies.

Risk Assessment: Identify and mitigate risks, such as data drift in traditional AI models or hallucination risks in GenAI outputs.

Reusability of Assets (Bonus): Create reusable data products to scale AI solutions across the organization, enhancing efficiency and speed.

• Tools: Brix by McKinsey QuantumBlack, Databricks

Data Architecture Plumbing (Bonus): Ensure robust data architecture with innovations like data lakehouses to simplify AI scalability.

• Tools: Delta Lake or Snowflake for integrated architecture.

Outcome:

At this stage, you’ll have a trained and validated model, fully designed prototypes, and an initial performance report.

4. Continuous Iteration & Improvement: Sustaining Excellence

AI solutions aren’t static. They must evolve with new data, user feedback, and changing business needs.

Key Steps:

Active Learning Pipelines: Develop mechanisms for AI systems to improve over time by learning from user interactions and newly available data.

• Tools: Kubeflow and Vertex AI for MLOps pipelines.

Explainable AI and Governance: Build trust by explaining model decisions (e.g., why a customer received a particular recommendation) and ensuring fairness in outputs.

• Tools: SHAP or LIME for explainability; OpenAI Moderation API for ethical oversight, DataRobot AI Governance.

Business Feedback Integration: Periodically validate models against evolving business objectives and customer insights to ensure alignment and relevance.

• Tools: Qualtrics or Google Forms for feedback collection.

Performance Monitoring: Use dashboards to track traditional AI’s accuracy and GenAI’s relevance or fluency in real time.

• Tools: EvidentlyAI, Grafana for monitoring.

Outcome:

The process concludes with robust monitoring systems, periodic updates, and continuous alignment with business objectives.

Final Thoughts!

This framework isn’t just a recipe for success—it’s a living framework adaptable to evolving AI paradigms. By integrating best practices and tools tailored to both traditional AI and GenAI, it ensures that your organisation can innovate confidently.

Whether you’re predicting customer behavior with traditional AI or generating immersive experiences with GenAI, this framework serves as a roadmap for delivering value, responsibly and at scale.

What tools have you used in your AI projects? Share your thoughts or challenges in the comments—I’d love to hear from you!