Welcome to my personal blog about marketing technology. I am Alan Chatfield, Marketing Operations Consultant at CRMT Digital.
On these pages, I discuss how marketers can make the most effective use of their martech stack.
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AI prices are going up. More expensive models at least answer one of the big unknown questions holding back corporate AI investments.
Discover how effective use of technology can boost marketing performance
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.
AI needs detailed business context to work effectively. That context already exists. It just needs to be collected and organised.
Martech is integral to the success of any marketing program. Yet, that synergy of technology and strategy creates governance and security risks.
2025 has been the year of agentic hype, if not of agentic rollouts. However, AI agents are seeing adoption as part of broader automation flows.
Maximise marketing efficiency and unlock the value of marketing ops
Data management requires specialised workflows for each dataset. These exist in multiple different tools in the tech stack.
By breaking down the barriers to automation, AI allows data to be used much more productively.
Product teams often resent the involvement of marketing in product strategy. Yet, examples of what can go wrong without marketing input are plentiful.
Early adopters have discovered the key to making agentic AI work. Iterative improvement is essential to getting the most from the technology.
Commentary on enterprise technology and it's impact on business
AI prices are going up. More expensive models at least answer one of the big unknown questions holding back corporate AI investments.
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.
According to the stock market, SaaS is legacy technology. Some SaaS companies do have an AI problem, for most, AI is just another feature.
Humans learn through doing. AI is no different. A culture of experimentation and iterative improvement is key to building a production-ready agent.
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