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Most labs have gone digital, at least to some extent. Far fewer have done it in a connected way, and that gap is where the real cost hides.

Over the past decade, many life sciences organizations have done the initial work of digitalization. The spreadsheet moved into a database, the logbook became an app, and manual steps went online. Every one of those was progress. But digitalizing one tool at a time leaves you with dozens of systems that each hold a piece of the picture and none that hold the whole thing.  

The data is there. Turning it into something you can act on still means logging in and out of a dozen places and stitching the results together by hand.

A connected lab solves a different problem than a digital one. The goal is operational simplicity: the workflows, data, and partners a lab already relies on, brought into one environment that works as a whole. That environment is the SciSure Scientific Management Platform (SMP).  

Getting there is harder than it sounds. Here's how we approach it.

Why simplicity comes first

At SciSure we talk a lot about operational excellence. The lab running at its best: compliant, efficient, reproducible. It's what every organization strives for. It's also genuinely hard to reach, and most attempts to reach it head-on make things worse.

The problem is, when you map out operational excellence as an end state and try to build toward all of it at once, you hand scientists a system heavy with process and tell them they have to use it. They don't. They work around it. And when scientists work around the system, the data you needed never makes it into the system in the first place.

That’s why at SciSure, we start somewhere more achievable. Build something a scientist understands, sees value in, and will actually use. Earn adoption first. Once researchers are genuinely engaged, data starts flowing to the people who depend on it: lab operations, EHS, procurement, leadership. Simplicity comes first because it has to. You crawl, then walk, sometimes jog, then run. Most labs are trying to sprint from the start.

Build what we know best, connect to the rest

That principle shapes what we build and what we don't.

We know our core. We know the problems we're best placed to solve, and we put our energy there. For everything else, there's already a tremendous amount of excellent technology in the market: startups doing remarkable work, established tools scientists already trust. We don't need to build a weaker version of any of it. We'd rather connect to the best.

The way we decide starts with the problem, never the product. Out in the field, our first question is some version of:

“Don't tell me what solution you want, tell me where the friction is”.  

What's slow? What breaks? What have you already tried, and why didn't it stick? From there we work backward to the cause, and the fix is either something we build into the platform or something we reach through a partner.

That's what SciSure Integrations and our partner marketplace are for: a growing ecosystem of trusted, best-in-class tools that connect directly into the SciSure SMP, so the best solution to a given problem is already wired into the lab software integration your team works in every day. There's discipline in it, too: when a problem sits outside our lane, we say so, and point you to someone better placed to help. Pre-built integrations and add-ons mean you don’t waste time or money getting the right solution.

Why it isn't just a bigger pile of tools

There's a fair objection to all of this. If the problem is too many disconnected tools, how is a marketplace full of tools any different? Isn't that just a tidier version of the same mess?

It could be, without a filter. So we run a hard one. Every partner goes through a rigorous qualification process: are they trusted? do they complement what we do? can they genuinely solve a customer problem? Then we test the integration itself before it's available to anyone. If it doesn't deliver what we promised, it doesn't ship.

The aim is for the marketplace to be the first place a customer looks when a new problem lands, ahead of a search engine or an AI prompt, because whatever they find there is already vetted and already connected. And importantly, we hold ourselves to the same standard as everyone in it. If a partner offers a better answer than ours, they should win, even when it costs us the work. That kind of competition keeps every tool in the ecosystem sharp, ours included.

What counts as "best" shifts with the customer. An early-stage startup and a top-20 biopharma rarely need the same answer to the same problem. A connected lab built on an open ecosystem carries options for both, and as a company grows and its needs change, it can move from one partner to another without ever leaving the environment its data already lives in.

The network behind the marketplace

The strongest part of the ecosystem is the part you can't see on a feature list: the relationships behind it. By the time a customer brings us a problem, we've usually already sat in the meetings, held whiteboard sessions, tested the integrations, and learned where each partner is genuinely strong.

A customer came to us not long ago with an unusually specific problem and asked whether we knew anyone who could provide expert consulting. We did. We made the introduction, and the partner happened to have exactly the right expert in house. They solved it in less time than it would have taken the customer just to evaluate vendors and decide who to call. The risk was gone almost immediately, because the homework was already done.

That depth comes from real time spent together. I was on a panel discussion recently with four of our partners, and the four of us joked about how many meetings we sit in together. That's the point, though. The work of making integrations actually function, and staying ahead of where the field is heading, happens long before a customer is in the room. By the time we walk through the door together, we're already in solution mode and battle tested.

The connected lab in motion

Picture a lab where the systems keep pace with the science. Stock runs low and the reorder is already moving. A delivery lands and inventory updates itself. Every material is accounted for from the moment it's ordered to the moment it's safely disposed of, and the full record is there to review without anyone assembling it by hand.

That's a truly connected lab, and it's nearer than most organizations think, because the pieces already exist. What's been missing is the connective tissue between them. Here's where we're building it first:

• Procurement into inventory. In most labs these sit in separate systems, leaving someone to reconcile what was ordered against what's on the shelf against what's been used. We're closing that into one flow: an order moves through approval into live inventory in SciSure, then on to the partners who handle hazardous waste: materials tracked from before they're ordered to the moment they safely leave the building, with safety, training, and compliance carried the whole way.

• Inventory under one roof. Instead of separate tools for assets, chemicals, and biomaterials, we're bringing lab inventory management together and connecting it to the instruments and monitoring systems around it, so the system knows whether a sample was held at the right temperature, or an instrument serviced on schedule. That's the live data that tells you whether your science is compliant and reproducible.

• Sustainability on the same connections. Because SciSure already tracks so much of what happens in the lab, it becomes the source of record for specialist sustainability partners: real carbon and reporting data drawn from what the lab is already doing, rather than estimated after the fact.

Each of these works on its own. Connected, they compound: the same order that updates your inventory also feeds your compliance record, your safety tracking, and your sustainability reporting, with no extra effort from the scientist who placed it.  

That compounding is what a connected lab actually delivers, and it's where the value an organization has been missing finally starts to surface.

Learn more about how SciSure works with sustainability partners to make lab sustainability measurable and actionable.

From one lab to the whole organization

For a single lab, all of this makes for a better day's work. For an organization running many labs, it changes what leadership can actually see, and the business outcomes they can achieve.

A global business with labs across dozens of sites has usually accumulated dozens of tools to match: site by site, group by group, none of them speaking to each other. Leadership can't see across them without commissioning a manual extraction exercise, and by the time the picture is assembled, it's already out of date. Connecting those tools through one platform opens the view: across every lab, site, and study, top down, in close to real time.

That visibility is what lets leadership act with confidence. Where resources are tied up and where they're sitting idle. Which sites are running at capacity and which have room to take on more. Where compliance risk or gap in lab inventory management is building before it becomes a problem. Decisions that used to rest on quarter-old snapshots and best guesses can be made on what's actually happening now.

The simplest path to a connected lab

Operational simplicity is where this all starts. Get it right with tools scientists actually adopt, data that flows where it's needed, trusted partners connected into one environment — and the rest compounds from there: excellence, organizational visibility, and the clean, connected data that any AI technology worth using will depend on.

That last point matters more each month, but it rests entirely on the first. An AI engine is only as good as the data it can reach, and a connected lab is what finally puts that data in one trusted place. The labs that get there won't be the ones that bought the most tools. They'll be the ones that made the tools they have work as one.

That's what the connected lab ecosystem is really about: not more software, but less friction. Science moving faster because the systems beneath it finally move together.

Ready to see what operational simplicity looks like in your lab? Talk to our team about bringing your workflows, data, and partners into one connected environment.

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Every lab order requires decisions about products, suppliers, availability, inventory, and data. ZAGENO's Jim Spang explores the complexity behind a seemingly simple purchase and how better-connected systems can protect researchers' time.

A scientist needs a reagent. They find it, order it, and get back to the experiment. At least, that's how lab ordering is supposed to work. In practice, every order for laboratory supplies can involve a surprising number of decisions.

Is this the right product? Is it already somewhere in the lab? Which supplier has it available? Is there a preferred supplier? What happens if it's backordered? Is an alternative acceptable? When will it arrive? And what happens to the purchasing data once the material reaches the lab?

One thing I've learned working with partners across the life sciences ecosystem is that the complexity isn't usually in any one step. It's in the handoffs between them.

That's the hidden complexity behind scientific purchasing, and it's why product discovery, suppliers, ordering, and inventory increasingly need to work together.

What lies behind a seemingly straight-forward lab order

Scientific purchasing carries a level of complexity that is easy to underestimate. Product specifications, availability, storage requirements, compatibility, shelf life, and supplier constraints can all influence whether an item is right for a particular experiment.

So the researcher isn't simply asking: Where can I buy this?

They're asking: Which product meets the scientific need, is available when I need it, and fits the way my organization buys?

And that's before an item ever reaches the cart. The breadth of scientific materials adds another layer. Laboratory supply chains can span routine consumables, chemicals, reagents, controls, specialized equipment, and custom products, each with different considerations around sourcing, storage, stability, and delivery. 

The paradox of supplier choice

Scientific research depends on a broad supplier ecosystem. Labs may purchase routine consumables from major distributors, specialized reagents directly from manufacturers, chemicals from niche suppliers, custom products through quotes, and equipment through entirely different channels.

As organizations grow, researchers can find themselves moving between supplier websites, catalogs, punchouts, and purchasing routes simply to identify the right option.

More choice is critical. Scientists shouldn't have to compromise an experiment because an organization wants to reduce the number of vendors it manages.

But choice also creates more decisions.

A product may be available through several purchasing channels with different pricing, availability, or delivery timelines. A preferred supplier may be the right choice under normal circumstances but not when a critical item is backordered. A specialty product may only be available from a supplier the organization has never used before.

The objective shouldn't be to eliminate scientific choice. It should be to make that supplier ecosystem easier to navigate.

Centralizing access across suppliers can preserve choice without requiring researchers to navigate every supplier, catalog, or purchasing channel separately.

At ZAGENO, our lab supply marketplace brings together more than 50 million products from 6,000+ scientific brands, allowing researchers to search and compare products, suppliers, availability, and delivery information from one place.

The complexity still exists. The scientist just doesn't have to manage all of it manually.

Product discovery is where the hidden work begins

The amount of work that takes place before an order is submitted is easy to underestimate.

Sometimes a researcher knows the exact catalog number they need. Often, it’s something less precise:

• The experimental objective they're trying to achieve
• The type of reagent or consumable required
• The technical characteristics that matter
• A product they've used before that is now unavailable
• An existing item for which they need an acceptable alternative

That turns a simple search into a product discovery problem.

Researchers may need to compare specifications, locate alternatives, determine which suppliers have stock, evaluate lead times, and reconcile all of that against internal preferences or purchasing requirements.

Increasingly, AI can reduce some of that work. Natural-language search, product recommendations, chemical structure search, and alternative-product identification can make large scientific catalogs easier to navigate while leaving the scientific decision with the researcher.

We've explored this further in our article on how AI is changing scientific product discovery and purchasing.

But the principle is straightforward: technology should help researchers get to the right choice faster, not give them another interface to manage.

Sometimes the best purchasing decision is not to purchase

Before searching suppliers, there is another question worth answering: Do we already have it?

Inventory levels, consumption, storage capacity, expiration dates, safety requirements, and compliance considerations can all influence whether another order should be placed. SciSure's Jon Zibell explores that upstream context in What Procurement Needs to Know from the Lab Before an Order Is Placed.

That context matters. If a reagent is already available elsewhere in the organization, another order can create unnecessary spend and increase the risk of unused material expiring. If inventory is approaching a critical level, earlier visibility can help avoid a shortage that interrupts an experiment.

This is where lab inventory management becomes part of the purchasing decision, by providing visibility into chemicals, biological samples, reagents, consumables, equipment, stock levels, and locations, helping teams understand what they already have and what they actually need.

The best purchasing experience, then, isn't simply better access to external supply. It's connecting what the lab already knows with what it needs to buy next.

‍Ordering and inventory shouldn't require the same data twice

Once the right product has been found and ordered, another handoff begins. The information used to purchase the material needs to become useful inventory information when that material enters the lab.

When those systems operate separately, people become the integration layer.

Someone receives the package, identifies it, enters information into another system, updates inventory, and may also have to add chemical, storage, safety, or expiration information.

Each manual handoff creates work. It also creates another opportunity for information to be delayed, entered inconsistently, or missed.

SciSure explored this issue in Procurement and the Lab Are Already Connected. Your Software Just Doesn't Know It Yet. The organizational connection between purchasing and inventory has always existed. The opportunity is to make the systems reflect it.

This is why connecting systems increasingly matters as much as the capabilities of any individual platform.

ZAGENO's approach to scientific procurement orchestration connects purchasing with suppliers, enterprise systems, and scientific workflows, while SciSure's integration framework connects research tools, instruments, databases, and external applications. Neither system has to become the other. They need to exchange the right information at the right moment.

The order may be complete in the purchasing system. For the lab, that's where the next part of the workflow begins.

How SciSure and ZAGENO connect purchasing with chemical inventory

A big part of my role at ZAGENO is looking for what I call the "better together" story: where two companies solve adjacent problems and create more value by connecting what each does best.

The ZAGENO-SciSure partnership is a great example.

• ZAGENO focuses on the complexity involved in finding and buying scientific supplies: product discovery, supplier access, comparison, purchasing workflows, and ordering.
• SciSure focuses on the materials once they become part of laboratory operations, connecting inventory with research, lab operations, safety, and compliance.

The integration between ZAGENO and SciSure connects those two sides by synchronizing procurement activity with chemical inventory.

Purchasing data can become structured, inventory-ready chemical records rather than having to be recreated manually after an order reaches the lab. That reduces repetitive data entry, improves inventory accuracy, and creates a more continuous flow of information from ordering into laboratory operations.

It's the type of partnership I look for because the value doesn't come simply from connecting two pieces of software. It comes from removing a handoff that someone would otherwise have to manage.

The best partnerships create wins for everyone: us, our partner, and the end customer.

Here, that end customer is ultimately the research team.

What does a better lab order look like?

A researcher identifies a need.

Before another product is ordered, inventory data can help determine whether the material is already available somewhere in the organization.

If it needs to be purchased, the researcher can search across relevant scientific suppliers, compare appropriate products and purchasing options, identify an available or preferred choice, and place the order.

The resulting purchasing information can then feed the inventory process instead of being recreated manually when the material arrives.

Over time, that connection improves decisions on both sides.

Inventory data can show whether products already exist across locations, whether stock is approaching expiration, and how consumption is changing. Purchasing data can provide visibility into prior orders, suppliers, availability, and alternatives before a shortage becomes an experiment delay.

Neither dataset tells the complete story on its own. Together, they provide a more accurate view of supply and demand across the research organization.

That matters for cost control, inventory accuracy, and experimental continuity. It also means researchers and lab operations teams spend less time reconciling information across systems.

The future of lab purchasing should be invisible

Life sciences organizations are already managing a growing combination of research software, inventory tools, suppliers, enterprise systems, and AI.

Deloitte's 2026 Life Sciences Outlook identifies productivity, resilience, digital transformation, and external partnerships among the issues shaping the industry's priorities. 

Adding more disconnected technology isn't the answer. Making the ecosystem work together is.

For researchers, the ideal experience isn't learning how to become better purchasers.

Scientists shouldn't need to understand supplier structures, catalog integrations, purchasing policies, inventory databases, and system architecture simply to get the materials required for an experiment.

Those complexities are real, but increasingly they can happen behind the scenes.

At ZAGENO, our job is to make the world of scientific products and suppliers easier to navigate. SciSure helps organizations manage the materials, research data, lab operations, safety, and compliance surrounding those products once they enter the laboratory.

By connecting those capabilities, SciSure and ZAGENO removes the friction between purchasing and inventory, allowing each platform to remain focused on what it does best while creating a smoother experience for the scientist.

Because the goal isn't simply to make lab ordering easier. It's to keep the complexity of getting materials out of the way of the science they make possible.

About the author

Jim Spang is Director of Business Development & Strategic Partnerships at ZAGENO, where he leads collaborations across the life sciences ecosystem. His approach centers on finding the "better together" story between complementary organizations and building partnerships that create meaningful value for research teams and the businesses that support them.

Between June and August, we sat in three conference rooms with almost nothing in common. BIO in San Diego in June, CSHEMA in Austin in July, and LOFM West back in San Diego in August. Biotech executives, Campus Safety Directors, Lab Operations and Facilities leaders. Different audiences, different regulatory pressure, and very, very different budgets.

Two moments from those rooms are worth putting side by side.

At CSHEMA in July, a senior EHS leader described adoption as his single biggest buying criterion. Not module coverage. Not price. If the people who need to use a system will not use it, it does not get selected.

Three weeks later at LOFM West, an entire session was given over to what looks like a different problem: how lab operations leaders translate their work into cost avoidance, risk reduction, and ROI, in language a finance team will approve.

They are the same problem seen from either end. You have to prove a system is worth buying, and then you must prove people will open it. Neither half is a feature, and both are getting harder on a smaller budget than last year.

Three rooms

BIO International Convention 2026

June 22 to 25, San Diego, CA

The industry stating its ambitions. Roughly 20,000 attendees from more than 70 countries, across 135 sessions in 18 focus areas ranging from AI and Digital Health to Biomanufacturing and Science and Regulatory Innovation. BIO is where the sector describes what it intends to build over the next five years.

CSHEMA 2026

July 18 to 22, Austin, TX

Where the ambition meets a real building. Campus environmental health and safety professionals, accountable for chemical inventories, inspections, and regulatory reporting across hundreds of labs they often do not directly control. Public institution procurement scores things the private sector treats as optional, so the questions asked here are more specific than almost anywhere else.

Lab Ops & Facility Management for Biopharma West

August 11 to 13, San Diego, CA

Where it gets operational. Around 70 senior lab operations and facilities leaders, roughly half at Director level or above and about 80% from drug developers. The agenda was built around site consolidations, hiring freezes, and doing more with less.

Trust is now the first question, not the last

At BIO, AI was everywhere. The AI Summit opened the program, sixteen further sessions dealt with it explicitly, and the through-line was acceleration: better molecules, faster targets, shorter development timelines. Coverage from the event also surfaced open questions about which use cases actually return on the investment, and a widening adoption gap between large biopharma and smaller companies.

A month later at CSHEMA, the same technology showed up in a completely different register. In the vendor AI sessions, the first audience question in both cases was not about features. It was about validation and hallucination. Where does this output come from, how do we know it is right, and what happens to our data. The vendor that gated its AI behind explicit controls and an accuracy disclaimer read as credible. The vendor that led with an expansive capability list did not.

By LOFM West, that had settled into something practical. The pre-conference workshop was framed as a toolkit rather than a vision, covering inventory logging, equipment issue tracking, SOP drafting, and predictive maintenance. One session examined what 12 million equipment bookings reveal about real utilization versus perceived demand, which is only answerable if the underlying data is clean.

Discovery-side AI is still selling potential. Operations-side AI is already being asked to show its work.

If you are evaluating tools right now, three questions held up in all three rooms:

• What is the output grounded in?

• Can it be cited back to a source record?

• Where does the data live, and where does a human stay in the loop?

Adoption is the gate, not the feature list

The CSHEMA comment that opens this piece came from the campus safety side, where the buyer and the daily user frequently sit in different departments with different definitions of success.

At LOFM West, the same idea wore a different name. An entire afternoon track was built around scientist engagement and buy-in for inventory and asset management systems, with a separate session on bridging the gap between scientists and lab ops. The framing was consistent: the barrier is behavioral, not technical.

Budget pressure makes this sharper rather than softer. LOFM did not treat cost constraint as a temporary condition; it was in the summit's own subtitle. Long implementations are no longer an acceptable cost of doing business, and nobody in this market has convincingly claimed the ground of fast, low-disruption migration. When there is no budget for a second attempt, a system nobody opens is not a slow start. It is the whole loss.

EHS and Lab Ops buy the system. Scientists use it. Both halves must work, or the investment does not return.

This is why we treat adoption as something to measure rather than assume, and implementation as the real starting line for ROI rather than a cost you absorb before the return begins. When a hazard lookup drops from days to minutes, that is not a feature claim. It is a signal that people are opening the system instead of routing around it. It is the same failure mode behind why ELN and LIMS adoption stalls at enterprise scale, and the reason we treat the scientist experience as a design requirement rather than a nice-to-have.

The gap starts at the purchase order

At CSHEMA, four separate institutions presented tools they had built in-house because commercial options did not fit. When organizations would rather maintain their own software than adopt yours, the gap is usually integration, not features.

The LOFM agenda described the same fragmentation from the operations side: siloed data systems, scattered equipment logs, spreadsheets standing in for a single source of truth, inventory data disconnected from the workflows that generate it. It is the kind of fragmentation that quietly increases organizational risk long before anyone calls it a problem.

Procurement is where it costs the most, because it sits at the very front of the chain. Most organizations know what they ordered. Far fewer know where it is, what condition it is in, when it expires, or what happens to it at end of life. Everything downstream depends on data that was already captured the moment the purchase order went out: hazard classification, storage assignment, MAQ and Fire Code and Tier II reporting, and disposal.

That gap was the substance of our joint session with ZAGENO at LOFM West. When a material is sourced through a procurement platform, that information should flow directly into chemical inventory with the relevant data attached rather than being retyped by someone three weeks later.

In ChemTracker, procurement exports are uploaded and mapped, AI standardizes chemical names and normalizes quantities and units, your team reviews and approves the results before anything is imported, and each record is enriched with hazard classifications, physical properties, storage handling, and SDS. MAQ, Fire Code, and Tier II reports then become push-button rather than a reconciliation project.

Duplicate data entry disappears for the person doing the work. For the organization, inventory accuracy stops depending on whether someone remembered to log the box.

Connecting procurement to inventory is not about adding another system. It is about building the bridge between the ones you already run.
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What we are carrying into the rest of the year

Three rooms, one conclusion: the distance between what an organization buys and what its people actually use is where the money goes. Closing that distance is not a feature problem.

• Lead with grounding, provenance, and human review when talking about AI, not with a capability list

• Treat adoption as a design requirement and a measured outcome, not a change management afterthought

• Assume every buyer is building a financial case, and make that case easier to build rather than harder

• Solve for connection between systems, because fragmentation is what organizations quietly pay for twice

If you are working through any of this, whether that is a chemical inventory that no longer reflects reality, a procurement-to-inventory gap you have been patching manually, or a business case you need a second pair of eyes on, we are happy to talk it through.

Up next: find us at I2SL in Boston, September 14 to 16, and at Future Labs Live USA in Philadelphia, October 28 to 29.