Kristóf
/Director of Customer Relations
I have been talking to lab directors and managers about their challenges. Why can innovation still be slow in the age of AI? What are the biggest issues their teams face? What makes their life harder? I've noticed the same thing again and again: the real bottleneck is rarely the science. It's everything around it.
At Bishop & Co., my job is to understand the deep, often unglamorous challenges a lab is wrestling with, and then work out how we can actually help. The pattern I keep seeing is this: improving assay generation increases productivity, automating a workflow improves efficiency, but treating and optimising labs as a complete holistic operation improves the entire business.
Let me walk you through what that shift actually looks like.
From manual workflow to intelligent workflow
Most of the labs we work with and talk to produce a lot of data, while their workflows are still optimised for paper:
- Sample and result logging by hand
- Printing and then re-digitising spreadsheets that only one person really understands
- Manually planning sample placements
- SOP sign-offs on paper that have to be physically chased down.
I want to be clear: none of that is a failure of the scientists. It's just accumulated habit. But every one of those manual steps is friction standing between a scientist and the actual science. When you replace them with integrated systems (a Laboratory Information Management System, Laboratory Notebooks, automated workflows), the non-scientific work shrinks, and the people you hired for their expertise get to spend their time on the work they do best.
The trick behind intelligent workflows is understanding the processes in your lab and breaking them into smaller pieces that aren't tied to a single hyper-specific use case, so the same building blocks can be reused to digitise one process after another, rather than building unique workflows one by one.
Digitalisation isn't about making the lab feel more "high-tech". It's about removing friction from the tasks that were never the point.
From data collection to data intelligence
As I said above, labs can drown in data and starve for insight. They generate enormous volumes of it, structured and unstructured, and most of it lands somewhere it will never be looked at again.
Today the value lies in what you do with your data, once you've got it. This is where custom software made for you and your team's exact workflow can help.
With the right solution, you can:
- Capture each result at the source with a complete audit trail, so the record never depends on someone transcribing and checking measurements by hand. When an auditor asks, every entry is already defensible.
- Connect your records in one platform instead of scattered spreadsheets, so results can be linked across studies, trends surfaced, and anomalies flagged automatically.
- Get insight to the people who need it through automated reporting and live dashboards, so a decision that used to wait days for a manual report can happen the moment the data lands.
When labs make this jump, they can stop treating data as a problem to sort out and start seeing it as an asset. And it can be done without increasing error rates as volume climbs, as long as the digital foundations underneath (standardisation, workflow orchestration, automated validation checkpoints) are solid. Volume without chaos.
From busy scientists to an empowered team
I'd be glossing over the hardest part if I skipped this one. Almost every automation conversation eventually hits the same nerve: the quiet fear that this is really about replacing people, or stripping scientists of their autonomy.
I understand where it comes from, and I never wave it away. But it's the wrong way round. The way I put it to teams is simple: automation handles the repetition, AI handles the pattern recognition, and scientists handle the judgement. Automation can't replace domain expertise and human judgement, but it can create more space and time for it.
From fragmented tools to one connected lab
So far, everything I've described still happens in separate pieces: a better workflow here, cleaner data there. However, the biggest impact appears when those pieces stop being separate. Most labs run on a patchwork of disconnected tools, and every gap between them is where time and money leak out, and mistakes seep in. When you join those workflows into one connected system, you stop managing the lab as a pile of tasks and start running it as a single, coordinated operation. The lab keeps its own workflows instead of adjusting to a tool, and ends up with one connected system rather than another tool to maintain.
This is where custom software earns its place. Off-the-shelf tools force a lab to bend its process to fit whatever the product assumes, and the gaps between those products quietly become someone's manual job. We build the other way round: software shaped around the way a lab already works, connected to every instrument, system, and third-party app the team relies on.
That's the real shift: from digitising a few forms to running a connected lab. It's the difference between a lab that is simply busy and one that creates impact.
Final Thoughts
The labs that get the most from digitalisation are the ones that stop treating it as a handful of separate tools and start designing their operation as one connected system.
That's the conversation I love having with the labs I work with, because the wins are hiding in plain sight. If yours is busy but somehow never quite fast enough, the answer probably isn't another point solution. It's a better-connected lab.