I’m writing this because most organisations say they want to be data-driven.
They recruit smart people, invest in analytics, and talk about evidence-based decision making. Yet when I walk into a large company, I often see the same pattern.
Talented people struggle with basic data tasks. They cannot answer fundamental questions about their own business. Why is this still happening?
Here’s the thing. It’s not a lack of intelligence or motivation. The real issue is structural.
Over the years, most large organisations, and the software as a service (SaaS) vendors they rely on, have kept people away from raw data. Everything is hidden behind polished front-end interfaces.
Dashboards, forms, and reports create the illusion of access. But they rarely allow users to see or shape the underlying data.
If you grew up using SaaS tools, you probably never learned SQL (Structured Query Language), database design, or how data actually flows. You were handed a set of features and told to get results.
But if you never saw the data, you were never taught how to think about it, question it, or challenge the assumptions baked into those tools.
This is not an accident. The business model of many SaaS providers depends on keeping customers inside their environment.
True data ownership is discouraged. Exporting or connecting to the raw database is made hard, expensive, or sometimes impossible.
As a result, the skills needed to work directly with data do not develop. People become expert at navigating menus, not at understanding what is underneath.
It’s common to blame users for not being data literate. In reality, the systems have been designed to suppress that literacy.
If you have never had the opportunity to work with the data layer, how could you develop the right instincts?
There is another way.
Agentic-first tools and MCP (Multi-Component Platform) first environments, like Recall or Agent.io, take a different approach. They empower users, or their AI agents, to work directly with the data.
Instead of hiding everything behind a front-end, these platforms facilitate direct access, exploration, and automation at the data layer. You can build low-friction automation, iterate on workflows, and actually see how your data is structured and moves.
This shift matters.
When people, or agents, can interact directly with data, they develop intuition, ask better questions, and spot errors earlier. Data literacy becomes a natural outcome of working with the right tools, not something that must be taught separately.
So what does this mean for leaders?
Stop blaming your teams for not understanding data. Look at the systems and vendors you have chosen.
Ask yourself:
If you want data-literate teams, you need to create the conditions for data literacy to flourish.
That means choosing tools and practices that expose, not hide, the data layer. It means rewarding curiosity, experimentation, and hands-on learning.
Truth is, this stuff is genuinely hard. But the first step is to stop blaming the user and start fixing the system.
The next generation of agentic-first tools is already making this possible. The rest is up to us.
This section adds context using only the validated quotes provided, so you can see how the market is shifting around the same problem: data access, lock-in, and the economics of SaaS.
If your organisation still treats “data literacy” as a training problem, you may be solving the wrong thing.
One reason this is surfacing now is that AI agents change how software gets consumed. The old model assumed more employees meant more seats and more licences. That shaped product design, pricing, and how much access you got to your own data.
A recent LinkedIn post captured the shift bluntly:
“In February 2026, the software sector lost roughly $2 trillion in market cap in less than 30 days. No recession. No rate shock. No accounting scandal. The market simply decided that selling software by the seat — the model that defined two decades of tech — is structurally broken. Here's the core of what's happening: SaaS was built on a linear equation: more employees = more licenses = more revenue for vendors. It was elegant, predictable, and produced gross margins of 70–85% that no other industry could touch. AI agents break that equation at the root. They don't need seats. They operate via APIs, execute tasks autonomously, and a single agent can replace what previously required dozens of individual licenses.”
Leadership implication:
A lot of teams try to fix the mess by buying another platform to “unify” data. That can help. It can also create another layer of copying and another point of lock-in.
Chris O’Neill put it like this:
“So, you're just another customer data platform? No. And this misconception is costing companies millions. Here's what most people think a CDP does: scrape data from different places, copy it into a separate pool, and call it a 'single source of truth.' Sounds logical, right? Customers are important, so having all their data in one place must be better. But there’s a problem: you're creating yet another silo. More copying, more transfers, more delays, more lock-in with a single vendor. At GrowthLoop, we don't copy your data anywhere. We’re an intelligent data and AI platform that sits on top of your data cloud where it already lives. No duplication, no lag time, no vendor lock-in.”
Leadership implication:
It’s easy to read all this and assume SaaS is dead. That is not the most useful conclusion.
Dirk Wakeham offered a more grounded framing:
“I’ve been asked that question multiple times in the past 48 hours. The short answer: no. The better answer: software is evolving — fast. Public SaaS valuations have been under pressure. AI has introduced real uncertainty. When the marginal cost of 'intelligence' drops toward zero, investors naturally ask: 𝑊ℎ𝑎𝑡 ℎ𝑎𝑝𝑝𝑒𝑛𝑠 𝑡𝑜 𝑎𝑝𝑝𝑙𝑖𝑐𝑎𝑡𝑖𝑜𝑛-𝑙𝑎𝑦𝑒𝑟 𝑠𝑜𝑓𝑡𝑤𝑎𝑟𝑒? But history suggests we’re at an inflection point, not a funeral. In prior platform shifts — client/server, cloud, mobile — the narrative was similar. Incumbents were questioned. Multiples compressed. And then a new category of value creation emerged.”
Leadership implication:
You can respond without turning this into a multi-year transformation programme.
Start with a simple system audit:
Then choose one workflow to rebuild as an agentic workflow:
If you do this well, you stop treating data literacy as a personal virtue.
You treat it as an outcome of a system that is designed to be learnable, inspectable, and improvable.
No validated links were provided in this run.