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    From BI Tools to AI Agents: The Next Evolution in MarTech

    tom-stromJanuary 11, 2025

    I still remember the first time a client asked me to build them a "real-time dashboard." This was 2014, and the request felt revolutionary. They wanted to see their marketing data update automatically, without refreshing a spreadsheet. We built it, they loved it, and for a while, that felt like the future.

    Ten years later, I realize we were just scratching the surface.

    The Dashboard Era: Beautiful, But Passive

    For most of the 2010s, the marketing technology stack revolved around one core promise: visibility. Tools like Tableau, Google Data Studio, and later Looker gave marketers unprecedented access to their data. You could finally see what was working and what wasn't.

    But here's what nobody talks about: visibility doesn't equal action.

    I've sat in countless strategy meetings where we'd pull up a beautiful dashboard, everyone would nod at the insights, and then... nothing would change. The gap between seeing a problem and fixing it remained enormous. You still needed someone to:

    • Interpret the data correctly
    • Decide what to do about it
    • Actually implement the changes
    • Monitor the results
    • Adjust based on new data

    We were optimizing the first step while the other four remained manual, slow, and prone to human error.

    When Automation Entered the Chat

    Around 2018-2019, we started seeing the first wave of "smart" marketing tools. Platforms like Albert.ai and Phrasee promised to use AI to optimize your campaigns automatically. The pitch was compelling: set it and forget it.

    The reality was messier.

    These tools worked within narrow constraints. They could optimize ad spend or email subject lines, but they couldn't understand your business context. They couldn't tell you why something was working or pivot strategy when market conditions changed. They were sophisticated automation, not intelligent agents.

    At Campanja, we built optimization engines for Netflix and Zalando that could automatically adjust campaigns based on performance data. They worked remarkably well for specific use cases. But every time a client wanted to expand beyond the original scope, we'd hit a wall. The system wasn't thinking—it was following complex rules we'd programmed.

    The Agent Shift: From Tools to Colleagues

    The transformation happening right now is fundamentally different.

    Modern AI agents—powered by large language models like Claude—don't just execute predefined rules. They reason, adapt, and communicate. They can:

    • Analyze data in context, not just patterns
    • Explain their reasoning in plain language
    • Handle ambiguous requests
    • Learn from conversation and feedback
    • Integrate insights across multiple data sources

    Here's a real example from building Cogny:

    Old approach: Client logs into a BI tool, sees that campaign performance dropped 15% last week, screenshots the data, sends it to their analyst, waits for a report, schedules a meeting to discuss findings, finally implements changes a week later.

    New approach: Client asks their AI agent: "Why did performance drop last week?" The agent analyzes the data, identifies that a competitor launched a similar product, reviews pricing data, checks search trends, and suggests three strategic responses—all in about 60 seconds. The client picks an option, the agent implements it, and monitors results automatically.

    The difference isn't speed alone (though that matters). It's that the agent is doing the analytical thinking that previously required a skilled human. It's moving from "show me the data" to "what should we do about this?"

    What This Actually Means for Marketing Teams

    I've been in marketing long enough to be skeptical of "revolutionary" promises. But this shift is different because it changes who can do sophisticated marketing.

    Previously, effective data-driven marketing required:

    • A data team to build dashboards
    • Analysts to interpret them
    • Strategists to decide actions
    • Execution specialists to implement
    • Project managers to coordinate everyone

    That's expensive, slow, and limited to companies with resources.

    AI agents collapse that stack. Not by replacing those roles entirely, but by making their capabilities accessible to smaller teams. A two-person marketing team can now operate with capabilities that previously required ten people.

    The Uncomfortable Truth About Jobs

    I need to address the elephant in the room: yes, this will change job requirements.

    If your primary value is generating standard reports, finding obvious patterns in data, or implementing straightforward campaign changes, AI agents will do your job faster and cheaper. That's not a comfortable truth, but it's true.

    But here's what I've observed working with dozens of marketing teams: the best marketers were never doing just those tasks. They were:

    • Understanding deep customer psychology
    • Crafting compelling narratives
    • Building relationships
    • Making strategic bets on uncertain outcomes
    • Navigating organizational politics
    • Identifying opportunities machines can't see

    Those skills are more valuable than ever. AI agents don't replace strategic thinking—they remove the drudgery that prevents you from doing more of it.

    The question isn't "will AI replace marketers?" It's "will marketers who use AI replace those who don't?" And the answer to that is almost certainly yes.

    What Makes This Time Different

    I've seen enough "next big things" in marketing to be cautious about hype. So why am I convinced this is different?

    1. The cost curve is collapsing

    Running sophisticated AI analysis used to require massive infrastructure investments. Now, API calls to Claude or GPT-4 cost cents. The economic barrier to AI-powered marketing is gone.

    2. The models are general-purpose

    Previous AI tools were narrow specialists. LLMs can handle almost any marketing task: analysis, writing, strategy, research, optimization. You're not buying a point solution; you're getting a flexible intelligence.

    3. The interface is natural language

    You don't need to learn SQL, Python, or complex BI tools. You just describe what you need. This democratizes access dramatically.

    4. The improvement curve is exponential

    These models are getting better every few months. Claude 3.5 is meaningfully smarter than Claude 3. GPT-4 was a major leap from GPT-3.5. The trajectory suggests capabilities will continue expanding rapidly.

    Building for the Agent Era

    At Cogny, we're betting our company on this transition. But we've learned some hard lessons about what works and what doesn't when building agent-based tools.

    Lesson 1: Context is everything

    Generic AI assistants are impressive but limited. The magic happens when you give agents deep context about your business, data, and goals. That's why Cogny connects directly to your data warehouse—the agent needs to understand your specific reality, not general principles.

    Lesson 2: Reliability trumps sophistication

    An agent that's right 95% of the time but wrong in unpredictable ways is worse than one that's right 85% of the time but you know when to double-check it. We focus obsessively on making Cogny's reasoning transparent and its limitations clear.

    Lesson 3: Humans in the loop, not the middle

    The goal isn't to remove humans from decision-making. It's to remove them from the tedious work that prevents good decision-making. Cogny suggests and explains; humans decide and refine.

    Lesson 4: Start with well-defined use cases

    The temptation is to build a general "do anything" agent. That sounds good but works poorly. We started with specific jobs: analyzing campaign performance, identifying optimization opportunities, generating insights from customer data. As those work well, we expand scope.

    What This Means for Your MarTech Stack

    If you're leading marketing for a company, here's my honest advice on navigating this transition:

    Short term (2025-2026):

    • Start experimenting with AI agents for analysis and reporting
    • Identify repetitive analytical tasks that consume team time
    • Train your team to prompt and guide AI tools effectively
    • Keep humans in decision loops but reduce manual work

    Medium term (2026-2028):

    • Integrate AI agents into core workflows, not just experimental projects
    • Rethink team structure around strategic work vs. execution
    • Invest in systems that give AI agents better context about your business
    • Build competitive advantage through better use of AI, not just access to it

    Long term (2028+):

    • Expect fully autonomous campaign management with human oversight
    • Focus team hiring on creative strategy, not analytical execution
    • Differentiate through proprietary data and strategic insight, not operational excellence

    The Real Revolution

    Here's what gets me most excited: this shift isn't about technology replacing humans. It's about technology finally matching how humans actually think and communicate.

    For decades, we've forced marketers to think like databases—learning SQL, building queries, structuring data in rigid formats. That was always backwards. Humans think in stories, questions, and context.

    AI agents let us work the way we naturally work. You don't need to translate your question into a database query. You just ask the question. The agent handles the translation, finds the data, performs the analysis, and explains the answer in plain language.

    That's not a minor improvement. That's fundamentally changing who can do sophisticated marketing and how fast they can move.

    What We're Building Toward

    The future I'm building toward at Cogny isn't one where AI does all the marketing. It's one where AI handles everything that doesn't require uniquely human judgment, creativity, or relationship skills.

    Imagine a marketing team where:

    • Routine performance analysis happens automatically
    • Campaign optimizations are suggested and implemented in minutes, not weeks
    • Customer insights are surfaced proactively, not discovered through manual exploration
    • Cross-channel strategy adjusts dynamically based on real-time data
    • Team time is spent on creative strategy, not operational execution

    That's not science fiction. Most of that is possible today. It just requires changing how we think about marketing tools—from passive dashboards we query to active agents that work alongside us.

    The Question You Should Be Asking

    The question isn't whether AI agents will transform marketing. They already are.

    The question is: how fast will your team adapt?

    Because here's the uncomfortable reality: the companies that figure this out first will have an enormous advantage. They'll move faster, learn quicker, and operate with leverage that traditional teams can't match.

    You can wait and see how it plays out. But by the time the transformation is obvious to everyone, the competitive advantage will be gone.

    We're at the beginning of this shift, not the end. The tools are still rough, the best practices are still being discovered, and there's huge opportunity for teams willing to experiment and learn.

    This is the most exciting time to be in marketing technology since the digital revolution itself. Not because the technology is impressive—though it is—but because it finally lets us focus on what makes marketing actually matter: understanding people, crafting compelling messages, and building things people want.

    Everything else? Let the agents handle it.

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    About Tom Ström

    Tom is CEO and co-founder of Cogny, where he's building the future of AI-powered marketing automation. Previously, he co-founded Campanja, where he built AI optimization platforms for companies like Netflix and Zalando. With over 11 years in growth hacking and marketing technology, Tom has been at the forefront of the industry's evolution from manual campaigns to AI-driven marketing.

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