Systems of Record in the AI Age
Data management requires specialised workflows for each dataset. These exist in multiple different tools in the tech stack.
A Single System for Data Management
By breaking down the barriers to automation, AI allows data to be used much more productively.
Rebirth of SaaS
The major SaaS platforms hold the detailed business context needed by AI. For most ops teams, AI is just a different way of using existing apps.
The Context Imperative
AI needs detailed business context to work effectively. That context already exists. It just needs to be collected and organised.
The AI Adoption Slow Lane
As AI becomes embedded within tech stacks, the key challenge is to find a workflow that allows both humans and agents to interact with apps.
The SaaS Doom Bubble
According to the stock market, SaaS is legacy technology. Some SaaS companies do have an AI problem, for most, AI is just another feature.
Beyond Dependency: The Martech Risk
Martech is integral to the success of any marketing program. Yet, that synergy of technology and strategy creates governance and security risks.
Beyond Marketing: Know Your Customer
Product teams often resent the involvement of marketing in product strategy. Yet, examples of what can go wrong without marketing input are plentiful.
AI in Practice: Test Early And Often
Early adopters have discovered the key to making agentic AI work. Iterative improvement is essential to getting the most from the technology.
Demand Agents: The Need for Machine Learning
Humans learn through doing. AI is no different. A culture of experimentation and iterative improvement is key to building a production-ready agent.