Most marketing teams don’t have an AI problem – they have a sprawl problem. The average stack carries somewhere between 20 and 40 tools, and according to the 2023 Gartner Marketing Technology Survey, marketing leaders actually use about 33% of their stack’s capability. That’s not an AI gap. That’s a waste problem dressed up as a strategy problem.
Before incorporating a new tool, undertake a basic check. For each monthly subscription, marketing pays, ask: name a feature or function the tool has that no other tool can do? You will identify “zombie” tools, that is, a tool exists and is used, inappropriately, where a perfectly functional existing tool is already available. This is the first gain in efficiency. A single AI platform that carries out three or four tasks is cheaper than the combined cost of running three or four single-feature systems and it demands less labor to manage.
Build For Interoperability, Not Impressions
New AI tools may look impressive, but if they cannot integrate with the rest of your tech stack, they will just create more work for your team. Hence, the first question to ask when considering an AI solution for your tech stack should be: “How do I access the API?”. Then, you will need to determine whether the output of an AI tool can connect to your existing systems and processes. Finally, it is necessary to create efficient workflows. This implies that the AI tool should integrate with your existing systems for tracking, analytics, or automation to ensure that the output of the tool is put to use and that any necessary follow-up is triggered. When generating content in a broader sense, the output can only be valuable if it can be turned into content and distributed automatically.
Solve The Lost File Problem Before It Kills Your Velocity
This one is underappreciated. As generative AI becomes more commonplace it becomes easier to create images, copy variations and video assets en masse, so the number of digital assets living within a team’s environment explodes. If there’s no intelligent way to sort and find those assets then instead of locating and reusing a file you’ll often waste time looking for something you know you have a few variations of somewhere.
AI-powered asset categorization within the digital asset management system does this by automatically adding descriptive metadata to files based on visual and semantic properties. A marketer trying to track down a product image featuring a solid color background doesn’t stand a chance of recalling the file name but they can search for some keywords and the file will come to them. Computer vision and language tools are fast. Using ai image search to isolate specific visual files from a library of generative media will save you hours per week rather than a few minutes.
Shift From Creator To Editor
The narrative that “AI will replace marketers” is not accurate and can be misleading. A better perspective is that your team should transition from creating content from scratch to guiding and overseeing the content generation by AI. This implies that you need to focus on the aspects of the work that play to your real comparative advantage: creativity, intuition, strategic judgment, and an understanding of your audience and your brand. Making sure that the work reflects your brand and your strategy is an incredibly valuable skill and not one that’s easily automated.
In practice, this looks like building workflows where large language models handle the initial copy, predictive analytics tools surface which content angles are likely to perform, and humans step in to apply brand judgment, edit for tone, and make the calls that require actual context. The people on your team who are good at strategy and editorial judgment become more valuable. The people who were mainly doing repetitive production work need to develop new skills. That’s not a crisis – it’s a job description update.
Workload automation handles the mechanical layer: scheduling, tagging, resizing, reformatting. When that’s running on its own, your team’s attention goes where it actually matters.
Create A Single Source Of Truth For Your Data
This is where a lot of AI implementations quietly fail. Companies plug in powerful AI tools but feed them generic inputs. The output is technically functional but feels off-brand, misses the customer’s actual language, or doesn’t reflect how the company has positioned itself over time.
The fix is a centralized data layer – a source of truth that includes your brand voice documentation, historical campaign performance, customer interaction data, and messaging guidelines. When your AI tools draw from this instead of a clean slate, the outputs are specific to your audience, not statistically average across everyone else’s.
SaaS integrations between your CRM, content tools, and analytics platforms make this possible without building anything custom. The goal is one place where the relevant data lives, and every tool in your stack references it.
The Stack Is Infrastructure, Not A Feature List
Efficiency in a marketing stack isn’t about how many AI tools you have. It’s about how few tools you need to do the job well, and how cleanly they work together. An interconnected system where AI handles the repetitive layer, humans handle the judgment layer, and data flows without friction is genuinely more capable than a bloated stack where each platform operates in isolation. Build for that, and the ROI takes care of itself.
