AI Technology and Capability Roadmap

An AI Technology and Capability Roadmap is a technology roadmap that outlines the planned development and adoption of artificial intelligence technologies and AI capabilities over time.

• Context:
  • It can (typically) account for the planned adoption of advanced machine learning algorithms, phased implementation of autonomous systems, and forecasted advancements in natural language processing.
  • It can be referenced by an AI Strategy Document.
  • It can incorporate an Machine Learning Roadmap.
  • It can be the output of an AI Technology Roadmap Planning Task (part of technology planning).
  • It can provide a mechanism to help Forecast AI Technology Developments.
  • It can provide a framework to help plan and coordinate AI Technology Developments.
  • It can range from being a Formal AI Technology Roadmap Plan to being an Informal AI Technology Roadmap Plan.
  • It can include sections such as:
    • Objectives: Outlining the key goals and objectives of AI technology and capability development.
    • Scope: Defining the boundaries and focus areas of the roadmap.
    • Methodology: Explaining the approach and methods used to create the roadmap.
    • Stakeholders: Identifying the key stakeholders in AI technology and capability development.
    • Timeline: Providing a timeline for developing and implementing AI technologies and capabilities.
    • Technology Overview: Giving an overview of the AI technologies and capabilities covered in the roadmap.
    • Risk Assessment: Assessing potential risks and challenges associated with AI technology and capability development.
    • Implementation Plan: Detailing the steps and actions required to implement the roadmap.
    • Review Mechanism: Describing the process for reviewing and updating the roadmap as needed.
    • Conclusion and Future Outlook: Summarizing the key points and providing a future outlook for AI technology and capability development.
  • It can include critical capabilities such as Data Quality, Data Enablement, Data Stewardship, Storytelling, Leveraging Insights, Data Engineering, Data Management, DataOps, ML Engineering, MLOps, AI Product Management, and AI Project Management.
  • ...
• Example(s):
  • an LLM-focused AI Technology and Capability Roadmap.
  • a Legal Tech. AI Technology and Capability Roadmap.
  • A LegalTech LLM-focused Technology Roadmap that accounts for the progressive implementation and enhancement of Large Language Model technology in LegalTech, such as on: legal research, legal documentation, case study analysis, and the overall digitization of legal practice.
  • A Healthcare Industry AI Technology and Capability Roadmap that accounts for the implementation of new health information technology systems, like electronic health records (EHRs) or telemedicine capabilities, along with the development of AI-powered diagnostic and treatment tools.
  • ...
• Counter-Example(s):
  • a Software System Development Plan.
  • a Technical Requirements Document (for a technical requirements analysis).
• See: AI Strategy, Artificial Intelligence (AI) Strategy Document, AI Product Management, AI Requirements Analysis.

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References

2022

• "Building a Capability Roadmap: The Maturity Stages of Data & AI."
  • QUOTE: Most organizations have some form of company-wide strategic initiatives around data and/or AI. Those strategic initiatives demand roadmaps that prioritize capabilities the company will need to unlock. These critical capabilities can include Data Quality, Data Enablement, Data Stewardship, Storytelling, Leveraging Insights, Data Engineering, Data Management, DataOps, ML Engineering, MLOps, AI Product Management, and AI Project Management. Each of these capabilities undergoes four key phases; net new, pilot, rollout, sustain. This talk will walk through key areas that every company must have on their roadmap, dependencies between capabilities, and benefits associated with each area.
  • The audience will walk away with:
    • An overview of critical capabilities companies need to successfully realize value from their data and AI initiatives
    • How to build a strategic roadmap that will mature their organization to effectively work with data and build AI
    • Common pitfalls and challenges companies face during this process and tactics on how to avoid them

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