© 2026 Linelia.io.

Posts about ai

Tag: ai

  • All these tools. No answers. And now AI. So what now?

    All these tools. No answers. And now AI. So what now?

    Your marketing team runs a dozen tools. Probably more. Every one of them was bought for a good reason: to understand your customer a little better, to answer their questions a little faster.

    So why do more tools so often leave you with more dashboards and fewer real answers? And why, at exactly that point, is everyone reaching for AI to fix it? Sorting that out is what martech stack consolidation is really about, and it is the step most teams skip right before they make things worse.

    Quick definition first, because not everyone lives in this world. Your martech stack is simply the collection of software your marketing runs on. The analytics, the email platform, the CRM, the ad tools, the dozen dashboards nobody quite remembers buying. Stitched together, that pile is your stack.

    How your martech stack got away from you

    Nobody sets out to run a dozen tools. It accumulates. You start with two or three that clearly earn their place, then add one for a problem that felt urgent, then another a vendor swore would tidy up the last one. Scott Brinker’s industry map now lists more than 15,000 marketing tools to choose from, so there is always a defensible next purchase.

    Each decision makes sense on its own. The sum does not. Gartner has found that marketers use less than half the software they pay for. In my experience, buyers worn down by the sprawl now want fewer suppliers, not more. What you are left with is a system no single person fully understands.

    Nobody buys a bloated stack on purpose.
    You assemble it one reasonable yes at a time.

    Why AI makes a messy stack worse, not better

    Here is the uncomfortable part. Garbage in, garbage out. Sh*t in, sh*t out. You know the rule. You just never pointed it at your own tools.

    AI does not clean up a fragmented stack. It runs on whatever you feed it, and if that is duplicated records, three conflicting sources of truth, and definitions nobody agreed on, the model will hand you confident nonsense, faster and at scale. AI amplifies the coherence you already have. If you do not have any, it amplifies the chaos.

    AI does not fix a fragmented stack.
    It just lets you be wrong faster, and at scale.

    The consolidation bet I made (and the part I would reconsider)

    Across my years on the corporate side, at L’Oréal and later at EnBW, I kept making a version of the same bet. We pulled as much as we could onto Google. One ecosystem, one login, one version of the numbers. And it worked. Reporting got faster. The team stopped arguing about which dashboard was telling the truth.

    Would I make the exact same call today? I am genuinely not sure. Consolidation buys you coherence. It also buys you lock-in. The single source of truth that made us fast also made us dependent on one company’s roadmap and one company’s pricing. That trade is real, and anyone selling you “just simplify everything” is skipping the honest half of the conversation.

    Martech stack consolidation is about coherence, not fewer logos

    So the goal is not fewer tools for vanity. Ripping out six logos to feel lean is theatre. The goal is coherence: your people and your future AI working from the same clean inputs, the same definitions, the same single answer to your customer’s one question. That is the version of martech stack consolidation worth doing, and it is exactly the kind of unglamorous groundwork I end up doing with clients before anything clever gets switched on. More on how I work is on the services page.

    Five checks before you let AI near your stack

    Enough principle. Here is the practical part. Before I let a client bolt AI onto their marketing, we run the same five checks. None of them are glamorous. All of them decide whether AI makes you sharper or just wrong faster.

    1. One source of truth for the customer. Name the single system that holds the real view of a customer. If three tools each claim that job, you have none.
    2. Numbers that agree. Pull the same metric, say last month’s sales, from two different tools. If they disagree, your reporting is fiction, and AI will only scale the fiction.
    3. Tools someone actually uses. For every tool, name the person who logs in each week and the decision it drives. No name and no decision means it is a subscription, not a tool.
    4. Clean handoffs. Track every point where data moves by hand: export, reformat, re-import. Each manual bridge is where the truth quietly breaks.
    5. A real reason for every tool. If the honest answer to “why do we have this?” is “a vendor said it would fix the last one,” you just found the first thing to cut.

    Work through those five honestly and you will know what to consolidate and what to switch off, long before you spend a cent on AI. If you want a second pair of eyes on yours, that is a conversation I am happy to have.

    Your customer still only has one question. Build a stack that can answer it. If this struck a nerve, let us talk, or come find me on LinkedIn, where I think out loud about this most weeks.

    Common questions

    What is martech stack consolidation?

    Reducing and integrating your marketing tools so they share clean, consistent data, instead of running many overlapping point solutions.

    Should I consolidate my martech stack before adopting AI?

    Yes. AI runs on your data and definitions. Fragmented inputs produce fragmented output, only faster and at scale.

    Does consolidating onto one vendor create lock-in?

    It can. Coherence and independence are a genuine trade-off worth deciding deliberately, not by accident.

    How many martech tools is too many?

    The wrong question. The real test is whether your tools agree on the numbers and whether your team actually uses them.

  • Why Small, Senior Teams Are Winning in the Age of AI

    Why Small, Senior Teams Are Winning in the Age of AI

    Lately, I’ve had a few moments in projects where I caught myself thinking:

    This would have taken a full team a few years ago.

    Now it’s a conversation, a few iterations… and we’re already moving. Not because we’re cutting corners.

    But because AI is removing a lot of the “busy work” that used to justify large teams.

    This shift is already changing AI consulting vs traditional consulting in a very practical way.

    And that changes something fundamental:

    What companies actually need from consultants.

    5 things that are shifting right now

    1. More people doesn’t mean more progress anymore

    For a long time, adding people was the default answer.

    • More analysts.
    • More slides.
    • More capacity.

    Today, a lot of that work is simply… gone.
    Or at least massively compressed.

    Which leads to a slightly uncomfortable truth:
    More people often just means more coordination.

    And coordination rarely moves things forward.

    2. “Having seen many companies” is not the same as having run one

    This is probably the biggest gap I see.

    You can work on dozens of projects…
    and still never experience what it actually means to carry responsibility inside an organization.

    Because inside, things look different.

    Decisions are not clean.
    Trade-offs are real.
    Politics are part of the game.
    And once something goes wrong, you don’t move on to the next project.

    You stay with it.

    That changes how you think.

    And it shows very quickly in the kind of recommendations you make.

    3. Speed no longer comes from capacity. It comes from clarity.

    The old way of consulting:
    → more (junior) people = faster progress

    The reality:
    → more people = more alignment loops
    → AI removes a lot of the heavy lifting.

    What’s left now is:
    • understanding the problem
    • making decisions
    • moving forward

    And that doesn’t scale well with team size.

    4. A lot of “consulting work” was never really about solving the problem

    If we’re honest, a big part of traditional project work goes into things like:

    • internal alignment
    • status updates
    • formatting slides
    • preparing the next steering

    Necessary? Often yes.
    Directly solving the problem? Not really.

    In lean setups, most of this disappears.

    And suddenly, you see very clearly what actually matters.

    5. Impact becomes visible much faster

    In the end, the question is simple:

    Is anything actually changing?

    Not:
    • how impressive the deck looks
    • how structured the framework is

    But:
    • are decisions made
    • are things moving
    • does the organization feel it

    With fewer layers and faster cycles, that becomes visible very quickly.

    And it’s much harder to hide behind process.

    This is where the difference between AI consulting vs traditional consulting becomes very visible in real projects.

    Where AI really comes into play

    AI is not replacing consulting.

    But it is removing a lot of what used to justify large structures and massive costs.

    AI can:
    • structure messy topics in minutes
    • create first versions instantly
    • explore scenarios without long preparation

    Which means:

    Less production, more judgment.

    And that’s exactly where experience matters.

    Tools that make this possible in practice

    All of this sounds abstract until you actually use it in your day-to-day work.

    A few tools I rely on quite heavily right now:

    n8n
    For automating workflows that used to take manual effort.
    Connecting tools, triggering processes, moving data around without thinking about it twice.

    Onepage
    For getting from idea to something tangible very quickly.
    Landing pages, MVPs, simple setups that help you test and move instead of overthinking.

    LeChat, Claude and ChatGPT (incl. custom GPTs)
    For structuring thoughts, drafting first versions, exploring options, and pressure-testing ideas.
    Custom GPTs especially help me to reuse patterns, frameworks and ways of thinking across projects.

    Notion
    As a central place to structure ideas, notes, and ongoing work.
    Less about documentation, more about keeping things connected and accessible.

    PopAi (or beautiful.ai, if you prefer)
    For turning rough ideas into clean, structured presentations quickly.
    Not to create “perfect slides”, but to get to a point where you can discuss something real.

    None of these tools are magic.
    But combined with experience, they remove a lot of friction.

    And that’s exactly what changes how fast you can move.

    P.S.: If you’re interested in learning more about my tech setup, you might want to check out this post as well: The first 150 days

    Why this matters to me

    Because this is exactly why I enjoy my current setup so much.

    A lean structure.
    Real experience from inside organizations.
    And tools that remove a lot of overhead.

    It creates a way of working that feels much closer to reality.

    And, to be honest, much harder to fake.

    If you’re looking for a more direct, hands-on way to move topics forward, you can find more about how I work here: Linelia’s services.

    And if you’d like to exchange thoughts or explore a potential collaboration, feel free to reach out via my contact page or connect with me on LinkedIn.