Strategic Software

A new category of enterprise solutions that combines frontier AI models with custom code and unique organizational expertise to address challenges traditionally handled by strategists and consultants. Strategic software differs from conventional marketing technology by addressing qualitative, strategic issues rather than merely automating deterministic tasks. For marketing organizations, this represents AI-powered systems that can analyze complex brand positioning, predict market trends, optimize customer journey orchestration, and generate strategic recommendations based on proprietary data. Unlike point solutions or generic SaaS platforms, strategic software creates continuous learning loops that compound organizational intelligence over time, enabling marketing teams to operate at strategic consultant-level insights while maintaining operational speed and scale.

Referenced in these posts:

Strategic Software

While code has revolutionized business operations over the last few decades, strategy has remained stubbornly human—until now. Strategic software, powered by AI and enterprise-specific knowledge, is transforming how companies conceive and execute strategy at unprecedented speed and scale.

Related terms:

Conway's Law

Conway’s Law states that organizations designing systems are constrained to produce designs mirroring their own communication structures. For example, separate sales, marketing, and support teams often yield a website organized into Shop, Learn, and Support sections—reflecting internal divisions rather than user needs.

Few-Shot Prompting

Few-shot prompting leverages AI’s pattern recognition by providing a handful of examples in the prompt, enabling the model to identify patterns and generate responses that match your intended style or format. This real-time approach achieves consistent, domain-specific outputs without needing massive datasets or model fine-tuning.

Zero-Shot Prompting

Zero-shot prompting is the most basic form of AI interaction where questions are posed without any examples or guidance, relying entirely on the model’s pre-trained knowledge. This baseline approach immediately tests raw capabilities, revealing both its breadth and limitations.