There's a strange paradox happening in B2B marketing right now.
Obviously, nearly early every CMO we talk to believes AI will transform their marketing.
The data supports that feeling. According to recent data from eMarketer, 98% of mid-market marketers expect AI to improve their results. But only about a third have actually embedded it into their daily operations.
Most are stuck somewhere in between conviction and action. They know AI matters.
They just don't know what to do about it. And they want to run, not walk.
At Mighty & True, we've spent the last few years helping mid-market tech companies navigate this gap. We've run our own experiments, with both client campaigns, and internal workflows.
And we've developed a point of view on what actually works.
The short version: most companies need to slow down in some areas and speed up in others. The companies that win won't be the ones who adopted the most AI tools.
They'll be the ones who developed the best judgment about where AI creates real leverage.
The Problem With "AI Strategy" Right Now
Truthfully, here's what's happening in the market.
Every martech vendor has added AI features. Every conference is talking about agents and automation. Every LinkedIn feed is full of people claiming to have cracked the code.
And yet: 64% of marketers say the market is too saturated with tools. Nearly half feel pressured to adopt new technologies even when they're not sure which ones deliver value. 61% struggle to integrate AI tools into existing workflows.
The result? A lot of expensive experimentation that doesn't connect to business outcomes. Or worse, paralysis. Teams that are so overwhelmed by options that they don't do anything at all. Or maybe even doubly worse, teams that have a reflex to go fast. Buy things and build a lot of new infrastructure. This is not a good idea.
We think there's a better approach. It starts with being honest about where AI actually creates value versus where it's mostly hype.
Where to Slow Down
When we start working with a marketing team, we often tell them to pump the brakes in a few areas:
Don't chase full automation. The vision of AI running campaigns end-to-end, without human oversight, isn't realistic for most B2B marketing contexts. The feedback loops are too slow, the attribution is too messy, and the stakes of getting it wrong are too high.
For now, the right model is human-in-the-loop: AI accelerates the work, humans make the judgment calls. That's not a limitation, it's appropriate given where the technology actually is.
Don't overhaul your entire stack. We see teams tempted to rip out existing tools and replace them with "AI-native" alternatives. That's almost always a mistake. The switching costs are high, the learning curve is steep, and the promised benefits rarely materialize in the short term.
A better approach: maximize the AI features already in your existing tools. HubSpot, Salesforce, and most major martech platforms have been adding AI capabilities. Start there before you start shopping.
Don't expect AI to solve strategic problems. AI is excellent at execution. It's much less helpful for the strategic work that differentiates your marketing, positioning, messaging, creative direction, understanding what your market actually wants.
These need to start with a human point-of-view and then move to using AI and automation to help test and inform a strategy.
If you're hoping AI will tell you what to do, you'll be disappointed. AI can accelerate how you execute a strategy. It can't replace the work of developing one.
Where to Speed Up
That said, there are areas where we push clients to move faster than they're comfortable with:
Workflow automation. This is where the real leverage is. Not AI writing better original copy, but AI handling the connective tissue between tasks, the manual steps that eat up your team's time.
Think about where your team spends hours moving information between systems. Content approval workflows. Campaign reporting. Meeting summaries and follow-ups. Asset handoffs. These are ideal candidates for AI-assisted automation.
Content repurposing. If you're creating any long-form content, webinars, podcasts, reports, event recordings, you're sitting on a goldmine of raw material. AI is remarkably good at atomizing this content into multiple formats: social posts, email snippets, blog summaries, short video clips.
Most marketing teams dramatically under-leverage their existing content. AI changes the economics of repurposing.
Research and intelligence. Competitive analysis. Market monitoring. Summarizing industry trends. Synthesizing customer feedback. These research-heavy tasks are perfect for AI acceleration.
You still need human judgment to decide what matters. But AI can dramatically speed up the gathering and initial synthesis of information.
First drafts and variations. AI won't write your best work. But it's excellent for generating starting points, first drafts of emails, multiple variations of ad copy, initial outlines for content.
The key is treating AI outputs as raw material, not finished product. Your team's job shifts from blank-page creation to editing and refinement.
A Three-Tier Framework for AI Adoption
Based on our work with clients, we've developed a simple framework for thinking about AI adoption:
Tier 1: Embed AI into existing workflows (now)
These are the highest-leverage, lowest-risk applications. Focus here first:
- Use AI features in your current tools (CRM, marketing automation, analytics)
- Automate content repurposing from long-form to short-form
- Accelerate research and competitive intelligence
- Generate first drafts and content variations
- Summarize meetings, calls, and customer interactions
Most teams can see meaningful productivity gains within 30 days by focusing on Tier 1 applications.
Tier 2: Build AI-assisted systems (next 6 months)
Once you have Tier 1 working, start connecting the pieces:
- Create workflows where AI outputs feed into subsequent tasks
- Build prompt libraries and templates for recurring use cases
- Develop internal guidelines for when and how to use AI
- Train team members on effective AI collaboration
- Document what works so knowledge doesn't stay siloed
This is where experimentation becomes institutionalized... where AI stops being something individuals play with and becomes part of how the team operates.
Tier 3: Expand to higher-complexity applications (12+ months)
With a foundation in place, you can explore more sophisticated use cases:
- Campaign optimization with AI-driven testing and adjustment
- Personalization at scale for nurture sequences and outreach
- Predictive analytics for lead scoring and account prioritization
- More autonomous workflows for low-stakes, high-volume tasks
These applications require more setup, more data infrastructure, and more organizational readiness. Don't start here, but test and plan for it.
The Flexibility Principle
One thing we've learned: there's no one-size-fits-all approach to AI adoption.
Some industries, healthcare, financial services, government, have regulatory constraints that require more human oversight. Some companies have data infrastructure that makes AI integration straightforward. Others are starting from scratch.
The teams that succeed aren't the ones following a rigid playbook. They're the ones that understand their own context, their constraints, their capabilities, their culture, and build an approach that fits.
That's why we've built flexibility into how we work with clients. Not every team needs the same level of automation and AI involvement. The right answer depends on the specific situation.
What This Means for Your 2026 Workflows
If you're a CMO thinking about AI for the coming year, here's our honest advice:
Don't panic. The gap between AI hype and AI reality is wider than most coverage suggests. If you're already using AI for discrete tasks, drafting content, summarizing research, generating variations, you're not behind. You're where most competent marketing teams are.
Do prioritize workflow automation. The biggest gains aren't from better outputs. They're from faster processes. Map where your team's time actually goes and look for automation opportunities.
Do invest in prompt engineering and context management. The organizations that get the most from AI are the ones that have figured out how to give AI the right context consistently. Documented brand guidelines, structured briefs, clean data. This infrastructure work pays compounding dividends.
Do run controlled experiments. Pick a specific use case, define what success looks like, and measure the results. Your own empirical data will be more valuable than any vendor's case study.
Don't try to do everything at once. Focus on Tier 1 applications. Get those working well before expanding. Sustainable adoption beats aggressive adoption every time.
The Bottom Line
AI will continue to transform B2B marketing. But the transformation will be more gradual and more incremental than the headlines suggest.
The companies that win won't be the ones that adopted every new tool. They'll be the ones that developed genuine competence. That built systems, trained their teams, and learned what actually works for their specific context.
That takes time. That takes experimentation. And that requires a realistic assessment of where AI is today versus where it's going.
If that sounds like a lot of work, it is. But it's work that compounds. Every experiment teaches you something. Every successful workflow creates capacity for the next one.
The teams that start now will have a significant advantage in 12 months. Not because they'll have more tools, but because they'll have more knowledge.
That's the real AI roadmap: learn by doing, build on what works, and stay flexible as capabilities evolve.
Mighty & True helps tech companies scale their marketing through expert team support, strategic playbooks, and AI-enhanced workflows. Get in touch to discuss how we can help you build a realistic AI roadmap for your team.
