Building a Future-Ready Marcom Tech Stack: My POV on What Matters Now
- Bianca Prade
- Feb 11
- 4 min read
Updated: Mar 20
AI has officially gone from “cool trend” to “mission-critical” in marketing and communications. But with shiny new tools popping up almost daily, it’s easy to fall into the trap of testing everything while never quite nailing a cohesive strategy. As the Edelman 2024 AI Landscape Report notes, the “ever-changing AI market” and “lack of clarity on enterprise-grade AI solutions” are big barriers for Marcom teams just trying to keep up.
Over the past year, I’ve worked with multiple clients to streamline their AI approach. Here’s my personal POV on the must-have building blocks of a future-ready Marcom tech stack—and how they align with key insights from Edelman (and a few other leading researchers).
1. LLMs + Generative AI as Your Content Accelerator
Why It Matters: We all know content is the foundation of every Marcom strategy. Whether it’s blog posts, ad copy, or social visuals, you need a pipeline that can keep up with audience demand. Enter Large Language Models (LLMs) and generative AI. They allow you to prototype campaign ideas and spin up drafts at lightning speed.
My POV: Start with a reputable enterprise partner that checks your data-security boxes. The Edelman 2024 AI Landscape Report emphasizes that “trustworthy AI vendors” are key for enterprise use. The last thing you want is your brand’s proprietary info floating around in questionable AI training sets. And remember: AI doesn’t replace your creative spark; it amplifies it. You’re still the human with the big ideas, but now you can iterate faster.
2. Analytics & Social Listening for Real-Time Insights
Why It Matters: If you’re not capturing and reacting to audience sentiment in real time, you’re missing valuable opportunities. According to the Edelman report, generative AI has “revolutionized analytics,” letting even non-tech folks quickly uncover consumer patterns or brand mentions across social and digital channels.
My POV: Invest in a social listening platform that can handle natural language queries. If your VP wants the brand’s sentiment in Southeast Asia right now, it should take seconds, not hours, to get a clear snapshot. This is especially handy when you’re juggling multiple campaigns across multiple platforms. A robust AI analytics tool means no more complicated dashboards or fiddly Boolean searches.
3. Ethical AI Guardrails
Why It Matters: Today’s audiences—and employees—care deeply about how data is being collected, processed, and used. Edelman’s 2024 AI Landscape Report underscores that enterprise leaders increasingly demand solutions that are transparent about data sourcing and usage.
My POV: Set up a formal “AI Governance Board,” or at least establish a monthly cross-functional meeting to handle big questions:
Which data is OK to feed AI models?
How do we detect and manage bias?
Who actually owns AI-generated content?
Clear guidelines build trust both internally (your teams know the rules) and externally (your audience sees you as a responsible data steward).
4. Seamless Collaboration Platforms
Why It Matters: Marketing and communications touch nearly every department—PR, product, design, legal, HR (the list goes on!). If your fancy new AI tools live in their own little silo, you’ll waste time and reduce impact.
My POV: Pick a hub—whether that’s Slack, Teams, or a specialized project management app—where everyone can brainstorm, plan, and track workflows in one place. The Edelman 2024 AI Landscape Report calls out the need for “integrated AI solutions” that fit into existing processes. The last thing your designer and data analyst want is to bounce between 10 different platforms just to finalize one campaign.
5. Human Upskilling & Organizational Buy-In
Why It Matters: As AI at Wharton & GBK Collective found, successful AI adoption hinges on teams actually using the tools, not just leadership purchasing the “best” software. And as Harvard Business Review points out, technology adoption is more culture than code—people need to see value in using it.
My POV: Budget for ongoing training. Host quick sessions where marketers can learn prompt-engineering hacks, or data analysts can share how to glean insights from a new dashboard. If the team doesn’t trust or understand the new AI tools, your ROI goes out the window. Embed training into your usual workflows—like making it a standing agenda item in Monday check-ins or monthly performance reviews.
Bringing It All Together
Think of your Marcom tech stack as a strategic command center. It combines:
Generative AI for fast, on-brand content,
Analytics for real-time customer insights,
Collaboration Tools that tie departments together, and
Ethical AI Guardrails to protect both data and reputation.
As the Edelman 2024 AI Landscape Report puts it, “Enterprise-ready AI can transform marcom, if you can find the right tools for the job.” From my vantage point, “the right tools” are the ones that:
Perform at scale,
Secure your data and build trust,
Reduce friction for creative teams, and
Adapt to your evolving brand needs.
Don’t chase every shiny AI object—craft a thoughtful ecosystem that helps you ideate, evaluate, and iterate faster. Combine bold creativity with data-driven decision-making, all under a solid ethical framework, and you’ll be well-positioned for the next wave of Marcom innovation.
Have questions or want to deep-dive into your Marcom tech stack? Let’s collaborate. After all, the tools are out there, but getting them to work together smoothly requires both a strategic vision and a commitment to the people who’ll use them.
References & Further Reading
Edelman. 2024 AI Landscape Report: The Communicator’s Guide to Finding AI Tools You Can Trust.
AI at Wharton & GBK Collective. Growing Up: Navigating Gen AI’s Early Years.
Mollick, Ethan. How Gen AI Could Change the Value of Expertise. Harvard Business Review.