Data and Process Fundamentals Matter More Than AI
AI adoption is accelerating, but without strong data, processes, and strategy, it falls short. Marketers must prioritise fundamentals to unlock meaningful value from AI investments and integrations.
Most marketers, if they’ve not already adopted AI tools, are being pushed by senior leadership to try them out. After all, they unquestionably remain the shiniest toy on the block.
But we’ve also started to notice that some marketing teams are pushing back, because they’ve realised that they’re just not yet in a place to take advantage of AI in the right ways. If their attribution models aren’t working and they can’t integrate their CRM with the lead pipeline, AI readiness is a distant thought at most.
Other brands are still in the content creation phase, or are otherwise aware that they lack the necessary sophistication in their marketing. Midmarket brands, in particular, may want to use AI as a shortcut to steal a march on competitors, but are often not at the same level of AI preparedness as their larger enterprise counterparts.
Those midmarket companies would also be less likely to have significant red tape, so the opportunity is greater than for enterprise businesses, but they will need to be prepared before implementation.
That is actually a positive thing. If marketers are aware that they’re not yet in the right position to use AI effectively, it means they’re also aware of the challenges to their fundamental processes and underpinning systems.
Whether for consumer campaigns or B2B brands focusing on account-based marketing (ABM), any AI integration is only ever going to be useful if their systems are integrated and their data infrastructures are watertight.
Overall, that means we will probably see fewer “next big thing” AI purchases over the course of this year, in light of pressures to make the existing martech stack actually work ahead of any AI integration.
What True AI Readiness Looks Like
Even those marketing departments that think of themselves as ‘using AI’ are probably not – not really.
Having a ChatGPT account and using it to draft some copy, or employing it to generate a few iterations of their product image, is not using AI to its fullest potential to automate processes, segment audiences and save or make revenue.
In our experience, there are five steps to being genuinely AI-ready:
- Have the right technology:
Curiously, for such a tech-driven question, this is probably the one that matters least. Finding an AI platform that suits the business need is not difficult and, issues over the wide range of pricing models aside, vendors are – of course – keen to assist.
- Have the right quality of data:
It sometimes seems that every other organisation has forgotten the data basics when it comes to introducing AI. How clean is the data? How well was it captured? Are there missing fields and duplicated records? If ‘messy data’ is everywhere, AI has no good starting place and is doomed to fail from the start.
- Have people in the right space:
With all the debate over the growing ‘techification’ of marketing as a discipline, there has to be some level of technology acumen in the marketing team to get the best use of AI. The creative tools might seem easier to use, but often it’s the less glamorous, background process-enhancing automation tools that will deliver the greatest business value.
- Have the right processes:
If a key use case for AI is to improve a process, has it been actually defined and written down, or does it only exist inside one marketer’s head? Processes have to be built so that AI tools can understand them, and it’s always better to integrate AI into a process that’s already been optimised.
Some teams are pushed instead into using AI to create workarounds – which can help but are rarely as effective. Is it better (and for that matter, cheaper) to change a failing email system and bring in a new platform, or to build an AI-driven data warehouse to desperately try to integrate the existing one?
- Have the right strategy:
If the business strategy is to reduce costs, AI-driven automation might be the best bet. If it’s about increasing revenue, look at chatbots and other customer-facing tools. It’s vital to prioritise investment and ensure the conversation with senior leadership is about the overall strategy, not about the AI products that people are most excited about currently.
Integration is the Real Opportunity
Before rushing into AI, it’s usually more effective to get all the disparate parts of the marketing operation talking to each other, with fewer platforms doing more, and doing it properly.
Businesses that stick to the fundamentals, such as mapping workflows and spotting bottlenecks, are in a better position to understand and fix what’s broken.
Then, once those changes are implemented, they can use AI to optimise what’s already working. Otherwise, they’re just speeding up the chaos.
In truth, the shift to AI is very similar to the shift to digital that took place in the 1990s and 2000s. There was a significant effort to move filing over from physical paper into digital files, and at the time, it undoubtedly uncovered issues and gave an opportunity to reassess operations.
Where marketing is now is remarkably similar. AI is not a silver bullet or universal panacea. Where most people go wrong is to view it as a transformation tool; it’s not – AI is most effective as an optimisation tool for things that have already been transformed.
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