AI STRATEGYPARTNERS

AI Solution Space Map

discovery

The landscape of AI technologies is broad - it is by far not limited to chat, GenAI, or trendy agents. The AI Solution Space Map allows to you to embrace the full range of possible AI solutions. It organizes AI capabilities across three key dimensions—data, intelligence, and user experience—helping teams avoid premature technology choices and align solutions to real user and business needs. By exploring the full solution space, you can make smarter decisions, especially in a fast-evolving environment where new AI technologies emerge daily.

AI Solution Space Map visualization

Principles

  • Different problems require different types of AI solutions—there is no one-size-fits-all

  • Solutions should be mapped systematically across data types, intelligence types, and user interaction patterns

  • A broad, structured view prevents premature commitment to suboptimal or overhyped approaches

  • The solution space is dynamic: expect it to evolve as new technologies and paradigms emerge

Implementation steps

1Define your solution dimensions

Identify the key dimensions relevant to your problem: data types and modalities, learning goals (predictive, generative, agentic), and user experience requirements (conversational, graphical, hybrid, generative interfaces).

2Explore candidate solutions

For each dimension, map available AI techniques. Document possibilities across supervised, unsupervised, rule-based, predictive, generative, and agentic methods.

3Analyze problem requirements

For your specific problem, clarify data characteristics (modality, labeling), intelligence needs (prediction vs. generation vs. action), UX expectations, and deployment constraints.

4Identify and prioritize options

Match solution candidates to your requirements. Favor solutions that balance simplicity, feasibility, user value, and future evolution. Don't overcomplicate early prototypes.

5Validate and iterate

Prototype fast and validate assumptions. Re-map the solution space if constraints, technologies, or user needs evolve.

Examples

  • Mapping textual data challenges across predictive NLP (classification, extraction) and generative NLP (summarization, content generation)

  • Exploring visual recognition problems across computer vision solutions: object detection, segmentation, multimodal approaches

  • Comparing predictive, generative, and agentic AI options for customer engagement tools

  • Evaluating conversational vs. graphical UX approaches for integrating AI into user workflows

Anti-patterns

  • Hype-driven choices: Selecting AI technologies based on trends or marketing, without considering problem fit or operational constraints

  • Neglecting simpler options: Overlooking straightforward predictive or rule-based methods in favor of heavyweight generative models

  • Ignoring user experience constraints: Choosing AI techniques without considering how users will interact with outputs

  • Freezing the solution space: Treating today's AI map as static instead of adapting it as new technologies emerge

  • Ignoring existing strengths: Failing to leverage your team's existing expertise in specific modalities or AI approaches wastes time and delays early wins.

Resources

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