DEV Community: Operelio

Is the spreadsheet warrior a dying breed?

Daniel Pank — Mon, 05 Oct 2026 10:40:29 +0000

Love it or hate it, Excel is one of those skills everyone claims to be proficient in on their CV. No one checks what it even means, but it feels weird not to have it there, like people will assume you look at a spreadsheet like it's a Martian landing in your garden. So why is it so important to businesses to be proficient with Excel?

Why businesses still run on spreadsheets

It's because pretty much every business at any level uses spreadsheets in some form. Spreadsheets have been the backbone of a lot of businesses, and moving away from the spreadsheet culture is difficult when it's all some people have known since its creation.

Excel came out in September 1985, on the Macintosh. By May 1996 Microsoft said more than 30 million people were using it, which works out at about five new users a minute for eleven years. They haven't published a number since, so nobody outside Microsoft knows what it is now. Forty years on, they're still adding their newest AI features to it.

The biggest study of what employers ask for is a bit old now. In 2014 Burning Glass went through nearly eight million middle-skill job adverts from a single year and found spreadsheets and word processing were a basic requirement in 78% of them.

So what has happened in the twelve years since? Two newer sources disagree. Lightcast, which is Burning Glass under a new name, still has Excel as the second most in-demand digital skill across fifteen countries, behind Microsoft Office itself, and mentions of the 365 suite it sits inside grew 195% in a year. ITJobsWatch, which counts UK IT vacancies rather than all of them, has Excel in 2.27% of permanent ads in the six months to September 2026, down from 4.76% a year earlier, with its rank sliding from 43rd to 69th.

Both are right, they're just counting different jobs. Lightcast covers every kind of job across fifteen countries. ITJobsWatch only covers IT roles in the UK, where Excel was never the main skill anyway. So there isn't one number for how much Excel is needed today, and if someone gives you one, check which jobs they counted.

What Excel is actually used for

Five jobs cover most of it:

Transferring and storing data, like contact lists or engagement rates.
Finance modelling and forecasting.
Reporting. The weekly numbers somebody pulls, tidies and pastes into a deck.
Reconciliation. Two exports side by side, and the job is finding what doesn't match.
The list that turned into the main record for something, because it was quicker than asking for a new field somewhere else.

That last one is where problems start. No one plans to run part of the business from a spreadsheet, it just happens because the spreadsheet was already open. Keeping that list clean, and working out whose job that is, is covered in Cleaning data: what it means, and who ends up doing it.

The three levels of Excel

On the surface, Excel is a list function with copy and pasting data from one point to another. The level above that is using Excel to interpret data, such as building graphs. The advanced level is summarizing it, which is what a pivot table does, and projecting forward from it, which is what FORECAST and TREND are for. So how do you do this? Excel has basic functions like removing duplicates or find and replace. Take it up a step and you're into VLOOKUP territory.

Even VLOOKUP has changed. Microsoft has since released XLOOKUP, which does the same job and can look left as well as right. But it isn't in Excel 2016 or 2019, so if your company is on one of those, you'll still need VLOOKUP.

So what does it mean to be proficient?

Does it mean you know how to copy and paste or delete rows? How to do a formula? This is where users go wrong. High level Excel is a skill which is highly valuable to learn, and is why many people attach skilled in Excel to their CVs.

Being proficient in Excel means knowing your way round a spreadsheet and pulling data out of it. But the bit that counts most is knowing roughly what the numbers should say before you open the file, like noticing a total is wrong because it's about double last month's. You can learn the formulas in an afternoon. That part takes years in the job.

The people who were sure they were fine

There's a body of research on this, and it isn't comfortable reading if you've got Excel on your CV. Ray Panko at the University of Hawaii spent years collecting the results of audits on real working spreadsheets, rather than what the people who built them said about their own work.

Errors turned up in 11% of spreadsheets in the largest audit, and in 86 to 91% in the most thorough ones.

The gap between 11% and 91% comes down to how hard the auditors looked. The more carefully they checked, the more errors they found.

Now look at the last row. Before any checking, the people who built the spreadsheets put the chance of an error in their own work at 10%. Every audit found more than that, and the most thorough ones found errors in nearly nine out of ten.

One of the best known spreadsheet failures happened here in the UK. Between 25 September and 2 October 2020, Public Health England lost 15,841 positive covid test results. The results were being passed between systems as .XLS files, the format Excel used before 2007, which stops at 65,536 rows. Everything past that row got dropped without anybody being told. Around 48,000 people who'd been in contact with a positive case were never called. It wasn't a problem with anyone's Excel skills. The file format was just too old for the amount of data going through it.

So is the spreadsheet warrior dying?

No. Spreadsheets are still how data gets from one system to another when the two don't connect. CRMs export to CSV, data providers send you spreadsheets, and finance systems export to a grid. People still email spreadsheets to each other because it's the one format everyone can open. And plenty of businesses, especially older ones, still run on them, even though they know the alternatives exist.

People have been predicting the end of the spreadsheet for as long as there have been products sold to replace it. The one doing the rounds at the moment is Gartner's: by 2026, more than 70% of finance organizations will have moved off spreadsheets as their main planning tool. But look at what it actually says. It's about one department, finance, doing one job, planning, and only about which tool they use first. And 2026 is this year.

It's the same every time. Someone predicts the next thing will replace spreadsheets, the date comes, and people are still using them. What is changing is how much of the work you do by hand. The AI now built into spreadsheets is good at the tedious bits, like writing a formula, and bad at the part that takes experience, like spotting that a total looks wrong. It also brings a new problem: when it makes up a value, it looks just like a real one.

So no, the spreadsheet warrior isn't dying. The boring parts of the job are slowly getting automated, and what's left is the part you were hired for.

Handing over the repetitive part

Spreadsheets are great for fixing a big file in one go. They're not great when you're doing the same fix every month, by hand, from memory, with no record of what you changed. That's the part we built Operelio for. You can remove duplicates, merge files and clean up headers in one pass, and you get a report of what changed, with any removed rows kept in their own file. All of that is free to use, and How to remove duplicates in Excel walks through it step by step. You still decide what needs fixing, you just don't have to do it by hand every month.

It doesn't replace anyone either. A tool doesn't know what your numbers should add up to, so someone who knows the business still has to check what comes back.

So keep Excel on your CV. Everyone else has it on theirs and nobody checks. Just know that what makes you good at it is spotting the total that looks wrong, and that skill, like the spreadsheet, isn't going anywhere.

Originally published on the Operelio blog. What's the worst spreadsheet error you've caught, or missed?

The CRM import problem

Daniel Pank — Thu, 01 Oct 2026 09:47:42 +0000

Many companies still do manual imports, usually a file or files come in and believe me, they can come in from everywhere. At larger enterprise companies, there's a team or individual who is the one that gets sent these files to import. At smaller companies, this is usually a sales operations or CRM administrator who handles this.

These teams or individuals are treated like the most powerful people at a company. I've seen c-suite bow down to these Salesforce admins or sales operations individuals in the hope that the data will be imported quicker. I've seen sales managers tell their reps who are asking for more data to go outbound to, that they will have to wait until Dave (not a real person), the importer, decides their data is worthy of importing.

Why the importer is held in such high regard

Why does this happen? To break it down into two perspectives, and I have been on both sides of it, we will look at the importer role first.

Importing data into a CRM is tricky business, especially with the complexity around CRMs now and all the different conditions that come with them. You have to make sure the data you are working with is all organized and formatted correctly, complex field mapping across tons of custom fields which are now available in a CRM, every record assigned to the right owner, and make sure you are selecting the right options to not overwrite anything important already in the CRM. Then one field mapping is slightly off or the import fails, and you have to start over again from the beginning.

Meet Dave

Dave, and we'll use him as an example, is usually an experienced operator. Dave knows CRMs and has likely been in that role for a number of years. Companies don't let individuals fresh out of university have the keys to their entire platform which runs the business.

Dave gets files sent in from marketing, sales, GTM divisions and anyone else that wants their data importing. This is where the challenge arises for Dave, and the bottleneck. Dave will likely have a backlog of files waiting to be imported and will usually prioritize which files go in based on when they were submitted, to keep things fair. Everyone in the company will be aware they need to get files across and will have an idea of roughly how long they have to wait until the data is in the CRM.

Every CRM has its own particulars

CRMs don't all behave the same, and the differences show up most at import time. Salesforce's Data Import Wizard takes 50,000 records at a time, so a bigger file means splitting it up or moving to Data Loader, a separate desktop application. HubSpot imports every row as a brand new record if your file has no unique column it recognizes, so a list you know is clean ends up doubled. Pipedrive wants custom fields created before the import rather than during it, and only a global admin can reverse an import, within 48 hours. There's loads of issues that can arise, and they are all things Dave has to hold in his head at once. If he's moving between two CRMs, he's juggling both sets, which is covered in Risks of migrating your CRM.

Looking at the file first

Before the import can even happen though, Dave must actually look at the file. Most of the time files that are sent aren't clean, ready to be imported data; they are usually a mess which has come from a marketing event or from a data provider. Most businesses have never settled whose job it is to clean that file before it gets to Dave, which is what Cleaning data: what it means, and who ends up doing it is about.

This is where Dave has a decision: import as much as possible and spend a few hours cleaning up the file to capture more data, or import what's possible in its current form. Either way, Dave gets a bashing from the reps who claim their data is bad, or loses hours on one file. With a backlog of files to go in already, most of the time it's a balance for Dave on capturing as much as possible without spending hours cleaning the file, and you can forget verifying it.

So usually there's a data loss in what's been captured between the file and the CRM. It's just an accepted practice of the data import problem.

The other side of it

Now from the other perspective of those sending files to Dave. Because a lot of these have never even seen an import page on a CRM, they have no idea the magic Dave does to get the data that's on a spreadsheet in front of their faces ready to go in the CRM. This means that they have no idea what would make Dave's life easier and what they could do before sending the file.

I have seen companies implement an education process whereby any files being sent to Dave have to be in a very specific format and have the exact columns in the exact right order for it to be imported. While this does solve the issue marginally, suddenly every person sending a file must constrain what could be a rich data list into columns which may not capture all that they wanted. While the education piece works and makes Dave's life easier, it still ends in the same result, which is a loss of data.

The three routes companies take

Have a team of people doing the imports, screening and cleaning the files before they are imported. This works at mass volume and when the budget is there. However, it usually means giving extra responsibilities to sales ops, RevOps or CRM admins and adding to their workload.
Automate the system where data links directly from one place straight to the CRM through an API or similar. This is usually the solution many companies adopt when going through any digital transformation. Suddenly, you can get that data from a provider straight into your CRM and cut Dave out of the equation (sorry Dave).
Use AI to do the import process for you. This is like the more SaaS based automation but takes it a step above automating the system directly, whereby you use AI workflows to adapt the data coming in and then it gets imported to the CRM. This is also what a lot of the experts are doing now, using AI transformation on data as part of the automation project.

The second one does present the same issues. When it comes to systems mapping data to each other, systems aren't really set up to send and receive data in the right formats. So what you end up with is usually a worse result, where you click one button on one system, let's say a data provider, and then only half of that gets into the CRM and it turns out it's overwritten an enterprise account's contact and taken all of the notes off it.

This is where the experts usually come in to do a data quality audit and make sure the systems are speaking to each other in the right way. It does work, but it takes months, it isn't cheap, and at the end of it you've handed the keys over to those experts, who charge to maintain it and adapt it when you need them to. Should you automate your lead flow? goes through the costs of that in more detail.

The third one can work extremely well, where you have a system that adapts to different file types and levels of information and can even enrich that data before it enters the CRM, a job The hidden gap in migrating your CRM data covers in full. However, it presents a new list of challenges which we haven't covered before.

Three challenges with putting AI in the middle

First, while AI has become increasingly advanced over the years, it still struggles with spreadsheets. It has a habit of inventing data, or saying it has done something when it hasn't. The reason it struggles is because of the volume of data there is and can be on a spreadsheet. AI can work well on a lower volume spreadsheet and do it exactly as you want, however ramp up the volume and this is where it struggles and starts inventing values or only doing half a job.

Second is the cost. Parsing a large spreadsheet uses tokens. The AI has to read and understand each value and what everything means before it can do the transformation, then actually do the transformation, then send it on its way. Tokens aren't cheap at volume, and this is how companies get saddled with a huge usage bill they weren't expecting.

Third, where your data actually goes. Handing your incoming data to an AI to transform and sort it means a third party is processing your customers' personal data, which makes them a sub-processor. So you need to check their contract: what they keep, for how long, where it sits, and whether any of it gets used to improve the service. Ask, and get the answer in writing. None of this is legal advice and your own agreement with them is what settles it.

That goes for any tool, including ours: find out what leaves your workspace, and get them to show you, not just tell you.

So how is the data import problem solvable?

It's by combining the three together. Morphing Dave, the automated system and AI together so that each one has a role to play and doesn't overstep. That's how we've built Operelio:

Dave is the toolkit. 17 tools for the cleaning and verifying Dave used to do by hand: merging files, fixing headers, removing duplicates, checking email addresses. None of them use AI, so nothing gets made up. The CRM Formatter maps your file to your CRM's fields and shows you exactly what will happen before anything is imported, rather than handing you an error file afterwards.
Trails is the automation. Files arrive in one place by email, upload or API, get cleaned and verified the same way every time, and go into the CRM on a schedule you set.
Bridgeant is the AI. It runs the tools rather than reading every value in your file. Out of a trail it can plan a cleanup and run it for you, once you've seen the plan and confirmed it. Inside a trail it sits just before the CRM and holds anything you've told it shouldn't land. It can stop a push, but it can never force one through.

Out of a trail, you run the cleanup and Bridgeant gets the push ready for you to send. In a trail, it all runs on its own, so Bridgeant checks each push before anything reaches the CRM.

When Bridgeant reviews a push, what leaves your workspace is counts, column names and CRM field names, plus the instructions you typed yourself. None of the actual data in your file is sent.

Why all three work well together

The reason all three work well together is we are taking the best parts of each system. Dave's level of experience and knowledge, the automation's consistency and reliability, and the AI reasoning to react, flag and adapt when things aren't going right.

Put them together and each one covers what the other two can't, in something repeatable and easy enough to use that it gets to the end goal. Solving the data import problem into CRMs.

Originally published on the Operelio blog. Is there a Dave where you work, or are you Dave?

Cleaning data: what it means, and who ends up doing it

Daniel Pank — Mon, 28 Sep 2026 08:54:46 +0000

To start with, what does clean data mean? Cleaning data can mean clearing a database or system of invalid fields, duplicate accounts, wrong or missing data, an overload of data, and spelling errors. It takes on many different meanings depending on the type of business, the department it's being mentioned in and where the data sits. Data can sit across many platforms like website analytics, inventory management, HR systems, SRMs and CRMs, and marketing engagement tools. Pretty much every business uses and relies on data in some form.

Businesses live and die by their data quality and quantity. Every business wants clean and reliable data in every system they use, as it allows informed decision making up and down the channels. Quantity alongside quality is also important: if a business does not have enough data, they cannot make accurate decisions. So clean data is about both the quantity and the quality. By cleaning your data, you then get an accurate measurement of the quantity you actually have. Both go hand in hand.

What's the impact of unclean data on a business?

Bad and unclean data can be dangerous for a business to have. It can send an entire marketing campaign in the wrong direction, make wrong product decisions, report inaccurately, and it becomes hard to trust your data once you spot a few bad examples. This costs money, time and trust in the short and long run.

If you ask anyone in an organization, they will say the main place bad and unclean data lives in any business is in the CRM. Some examples of it:

Sales team metrics and KPIs take a hit on outbound. Let's say each rep has a list of accounts to go outbound to, and 20% of that data has the wrong number or the wrong website for the company. That's extra time wasted from the rep's point of view and a loss of activity.
It isn't just marketing's problem. Take a marketing person running a mailing campaign to a list. If 15% of those email addresses are unverified, you risk tanking your domain rating from a deliverability perspective, and everybody else in the business sends from that domain too. Google asks bulk senders to keep their spam complaint rate under 0.3%, so there isn't much room for error.
Dashboards give wrong readings. Most leaders rely on dashboards to do the heavy lifting and to keep them informed. If the data funnelling into the dashboard is bad, so are the readings, and off that, bad decisions are made.

You'd expect there to be a reliable figure for what all of this costs, but there isn't one. The two most quoted are Gartner's, that poor data quality costs an organization at least $12.9 million a year, and IBM's, that it cost the US economy $3.1 trillion in 2016. Neither comes with a method you can check. Gartner puts its number down to its own 2020 research, which it hasn't published. The IBM one appeared in a Harvard Business Review column with no workings behind it, and it works out at roughly a sixth of US GDP that year.

A smaller study from 2017 is more useful. Tom Redman, Tadhg Nagle and David Sammon asked 75 managers to take 100 records their own department had recently created, and mark each one against the handful of fields that department actually depends on.

47% of freshly created records had at least one critical error. Only 3% of departments scored as acceptable.

These were records the department had only just created, entered by the people whose job it is to get them right, and nearly half already had a mistake in a field that mattered.

So how do you clean data across an organization?

Cleaning data can be done in many ways. How you do it depends on timing and budget. Either you keep it running in the background or you do it in one go, and the ongoing version splits into three.

1. Set the culture

Ingrain in every user the habit of checking their data quality and accuracy at all points. Salespeople must keep their lists accurate and up to date at all times, marketing must do the same. If every channel of the business is focused on not letting bad data creep in, it's easier to spot when bad data appears, and you have multiple people flagging it.

Upsides

Data first culture means with everyone focused on data, you can be sure the standard is the same throughout the organization, accurate data is flowing and users are ready to throw out bad data.
Continuous improvement.

Downsides

It can take away from other responsibilities and be used as an excuse for not fulfilling other duties.
Hard to enact, especially at larger organizations.
Requires the correct tools for everyone with a data first responsibility, for example enrichment, data verification and cleanup.
Takes time to show effectiveness.

2. Dedicated role

By having a team or individual dedicated to keeping the data clean, it then means the responsibility falls on them to check, clean and verify data across an organization.

Upsides

Easier to manage data when there's an assigned responsibility.
Likely an expert in the field of data, which means they or the team become the source of truth across the organization.
Easier to make serious improvements to both the quality and quantity of an organization's data with a dedicated role or team.

Downsides

Expensive to do, and their ROI isn't directly distinguishable, as other departments will pick up the benefit.
Everybody else stops owning it. The moment one person is accountable for data quality, it's nobody else's problem, which is the opposite of what the first option is trying to build.
It becomes a queue. One person can't keep up with every team at once, so the work gets prioritized and some of it never gets done.

3. Technology and AI

There are plenty of tools out there, AI ones included, that can help keep your data clean and verified within your CRM and other systems. They don't replace the first two options though. Whether it's the whole business or one person doing the cleaning, they still need something to do the work with. The main types are:

Verification. Checking that an address, a phone number or a company still exists before anybody acts on the row.
Deduplication and merging. Finding the same person or company written two or three ways, and deciding which one to keep.
Standardization. Making one field say the same thing in every row, so that reports group the way you expect.
Enrichment. Buying in what you never had in the first place, usually priced per record.
Monitoring. Watching for bad data as it arrives, rather than finding it a quarter later.

Upsides

Eases the pressure of responsibility.
Able to process high levels of data and connect different sources together.
Speed. It's much quicker to have technology and AI processing data than a human.

Downsides

Expensive to run and operate at scale.
AI can manipulate data and invent data, so it still needs close supervision.
Set-up time can take a while, and so can getting the business used to it.
Requires maintenance, which means a person or a team to adjust it for any new conditions.

By doing any one of these three, you are making sure your data is being cleaned, and you can trust what you are working with, to some extent. By implementing all three, you have an ecosystem which means your data is constantly being prioritized.

One-off cleanup

Some organizations don't make cleaning an ongoing priority, or they do it in conjunction with the three above. What this involves is, say every quarter, doing a cleanup across their organization whether it be in sales lists, marketing lists or HR records. This then becomes the responsibility of each department to make sure their data is fully accurate and in check. The reason this can work is because it acts almost like an end of financial year for each team and department to get their data sorted.

Another example of a one-off cleanup is during a CRM migration, or any system migration. This is usually smaller scale, and it's the one time nobody argues about whether the cleaning is worth doing. The hidden gap in migrating your CRM data covers what gets missed even then.

Upsides

Much more cost effective as each department has ownership over their own data.
A step towards setting the culture, without committing to it.
Easier to see gaps during the cleanup per department.

Downsides

Data goes out of date quickly, and waiting too long between one-off cleanups can mean there's a period where the data is not optimal. You do a cleanup every quarter and by month three, the data is already diminishing or diminished.
The tools to clean data and verify it are still required in some capacity, which has a cost and a training angle that comes with it.
When cleaning isn't directly tied to employees' targets, it can mean a rushed job is done on any cleanup. For example, a salesperson who has targets to hit would rather be selling than cleaning data, even if it meant they would be working with better data.

Cleaning what is already in the CRM

A CRM is built to work on one record at a time. That's fine day to day, but it's slow when you need to fix thousands of rows at once, which is why cleanup inside a CRM either never happens or gets handed to an outside firm as a fixed price project.

That's the part we built Operelio for. You export what's in the CRM, find the duplicates across the whole file, check every email address and standardize the fields you report on in one pass, then push it back over the originals, matched on email so existing records are updated rather than duplicated. What it can't do is merge or delete the duplicates already sitting inside your CRM, because a push updates a record and can't collapse two into one. That part stays an in-CRM job. How to clean up your CRM data walks through the round trip end to end.

A tool doesn't decide who owns the job though. It makes the job smaller, but somebody still has to do it.

So overall, whether you do an ongoing cleanup or a one-off policy or both, it is good practice to clean your data and put the steps in place to be able to achieve that clean data title.

Originally published on the Operelio blog. Who owns data cleaning where you work, and does it actually get done?

Building your own CRM is the easy part

Daniel Pank — Thu, 24 Sep 2026 12:09:33 +0000

Since the boom of AI there has been a drastic rise in companies building their own CRMs. We are going to explore the reason for it, and look back at what caused it.

There is a number behind this, although it is not about CRMs specifically. McKinsey's 2026 State of AI survey asked 1,719 people across 97 countries, and 32% said their organization had decided against buying at least one piece of software because they had built it themselves with AI coding tools instead. That share was highest in technology and healthcare. McKinsey does not break the answers down by type of software, so nobody can tell you how many of those were CRMs. What it does tell you is that a third of organizations have already made that call about something.

The one I built during Covid

For some context, back during Covid, I was working at an outsourced marketing and sales agency in an operations role. We didn't have a centralised CRM and most of the time any work was being done on spreadsheets, or we were using a client's CRM. I decided to create a centralised CRM and started looking at providers. HubSpot, Salesforce and Pipedrive were all evaluated and I came to the same conclusion every time: while HubSpot and Pipedrive had free plans, the scale the CRM would operate at meant implementing one would cost the business thousands per month, and none were very customizable for different client use cases and different types of data. In the end, I decided we would build our own.

At the time, unless you were a developer or could pay a developer to build the CRM, it wasn't a viable solution to build it from scratch. What I decided to do was use a workforce management platform with lots of customizability and turn it into a CRM which could be customized based on the different clients and was going to be easy to navigate for the sales reps.

It worked once built out but needed tweaking constantly, and because I had built it, I was the only one who knew how to customize it and get it working when things weren't right. This meant all data imports had to flow through me. I did eventually end up training another person how to customize it and import, however that was years later.

The point is, would I have been better off accepting the higher cost for the well-known providers and trying to make it work? Margin says no. The effort that went into building your own CRM says yes.

Why it is happening now

So why is the craze now building your own CRM if it's not worth the effort? Well, the simple answer is, it's a lot less effort now to build your own CRM with AI help.

Companies have always had different use cases for their CRM, whether it's tracking deals, inbound and outbound data for reps, or client management. Take HubSpot for example: their feature list is so huge that it covers almost every scope a business could require from a CRM. The caveat is that it comes at a cost, with constant bolt-ons for different features.

Six years ago I had two separate objections to the big providers: too expensive, and not shaped like us. Today a small team can build the shape it wants without a developer on staff. So it's now a cost problem, not a customizability problem, because that one has been removed.

What it actually costs

When we look at the cost side, it's clear the big providers have ramped up their prices, and Salesforce's price history is the best documented. It did not raise list prices at all for seven years. That freeze ended in August 2023 with an average rise of 9% across Sales Cloud, Service Cloud, Marketing Cloud, Industries and Tableau, which Salesforce justified by pointing at 22 releases, thousands of features and billions of dollars of research and development, including what it called recent generative AI innovations. A second followed on 1 August 2025, averaging 6%, that time only on Enterprise and Unlimited editions. Two increases in three years, after seven with none.

August 2023 was within months of generative AI arriving, and Salesforce used that same technology as part of its reason for the rise.

The reason they can keep doing it is that the big providers know that once you are in their ecosystem, it's harder to get out of than in, so they can hold you to ransom unless you want to do a costly migration to a new platform. Risks of migrating your CRM is what that bill looks like when somebody finally pays it.

Businesses no longer have to be held to ransom as they can build their own CRM from scratch and make it suit their needs perfectly, the exact same concept I had when building out the workforce management system into a CRM. So it's not a new concept, it's just that the moat and the willingness to change providers have been lowered drastically.

When you compare the cost of running your own CRM against one from a big provider, the sum people reach for is licence fees against build hours. That sum is wrong, because the build hours stop and the job does not. A provider's cost is a subscription: it recurs, it rises, and it arrives as an invoice somebody has to approve. A CRM you build costs you the build once, and then costs you a person for as long as you run it.

A CRM needs the same four things however you got it. Your data and the way you work are yours in both cases. Under that sit the software itself, keeping it online, and being able to show a regulator what it does with personal data. Buying is a decision about who carries those three.

Same four layers either way. What changes is who answers for the bottom three, and whether that arrives as an invoice or as a person.

The provider's bill is visible, and somebody argues about it every year. The bill for the CRM you built is invisible, and nobody argues about it at all.

What the numbers say

So, if anyone can build their own CRM, why aren't the big providers panicking?

The market has spent 2026 changing its mind about exactly that, in both directions. Salesforce's share price fell about 30% over the first four months of the year, on the fear that AI would undermine software sold by the seat. It then rose 22.6% in a single day in late August when Agentforce numbers and raised guidance landed, and even after that it is still down for the year. HubSpot beat its own second-quarter figures on both earnings and revenue in August, and its shares fell 21% in after-hours trading anyway. Whatever you think a share price is telling you here, it was telling you something else a few months earlier.

The customer numbers point the other way, though not cleanly. HubSpot reported 306,446 customers at the end of June 2026, up 14% on the year, with revenue up 20%. The reason the shares fell anyway is in the detail: net new customers came in at 7,000 against the company's own target of 9,000 to 10,000, and management flagged longer sales cycles and more cautious buyers for the rest of the year. So more companies are signing up than ever, and fewer are signing up than the company expected. Both are true, and the second one is what the market traded on.

The reason goes back to my original argument: it's the effort required to maintain your own CRM, not build it. Building your own CRM works fine, the same as it did for me. However it's the hidden cost of having to maintain it, being the only one who knows how it works, and almost becoming the CRM manager as a full-time job. Unless you plan to stay at the company for 10 years, or you own the company, there's a risk attached to it. The person or team who built it are the only ones who know how it works and how to change it safely.

AI has made building it easier, but it hasn't made it any easier to be the one person who knows how it all works. It won't sit in a compliance review for you, keep it online at 2am, or take the call from an angry client.

In a small company the risk is small, and it makes it worth it rather than paying huge amounts to the big providers. For medium-sized and large enterprise companies, the risk isn't always worth it. Having a reputable provider as a failsafe means outages in service, employees leaving and data compliance all fall on the provider, and companies are willing to pay for the privilege of not having to deal with it. It's much easier to explain to a client that you didn't get back to them because Salesforce was down, than to say your own in-house CRM messed up. There's a trust factor the big providers instil which isn't easy to remove or replicate.

So what is the right choice?

Well, it depends on your use case and the size of your business, and your budget. My belief is that if you're a startup or small company of less than 25 to 40 employees, then yes, it makes absolute sense to build your own CRM, build it out to suit what you need and scale it as you scale.

Just don't build it in isolation. Build it with others, train as you go, have failsafes so the reliance is not on one person, and accept that the drop in spend on a CRM provider means more time and effort allocated to your own CRM.

If you are a mid-sized or larger business, unless there's a specific need or it's an obscene cost per year to use the big providers, building your own sounds exciting and looks like it will save the business money long term, but the problems will start stacking up. You will have to worry about maintenance, compliance and failsafes, and what happens if the team or the individual who built it resigns in the morning. CRMs for larger businesses are the lifeblood they run on, and it takes a very driven management team to enact that level of change across an organization.

Getting data into one you built

Build your own CRM and you take on a job nobody plans for: getting data into it. There is no native importer, no validation anybody else wrote, and no template for your record shape, because your record shape did not exist until you invented it. A big provider's importer rejects a malformed row and tells you why. Yours does whatever you told it to do, because you wrote that part as well.

This is actually why I built Operelio. You set up a custom template with your own field names and required fields, every row gets checked against it, and the API runs the cleanup on a schedule, so every import isn't going through one person like it did for me. How to connect Operelio to a custom CRM walks through it end to end.

Ultimately, the fact there is now less of a moat to having a CRM in the first place can only be a good thing. It means businesses now have the option of choice, and not just between which provider to go with. Will it mean the big providers start lowering their prices to reflect the reduction in the moat? Probably not. What it will lead towards is better service and more options from the big providers, hence the examples of HubSpot acquiring companies into its ecosystem to evolve their product line and capabilities. It bought Frame AI for conversation intelligence in January 2025 and folded it into Breeze, then XFunnel that October, which watches how a brand shows up in answers from ChatGPT, Claude and Perplexity.

The best part of it all is now the argument is not which provider do I go with, it's do I go with a provider or do we build our own.

Originally published on the Operelio blog. Have you built your own CRM, or talked your team out of it? I'd like to hear how it went.