R&D Digital Transformation: The Value of the Magical Middle

Meet in the magical middle

In today’s fast-paced digital environment, digital transformations never really end; they just keep evolving to keep up with the latest technological developments. This is especially true in the world of agile methodologies where instead of one big finish line at the end, we get many smaller finish lines along the way—each one delivering value to the organization and tangible benefits for users. That reward-packed magical middle is especially present in R&D digital transformations and the journey to lab of the future.

This means everything when it comes to change management, often cited as the hardest part of digital transformations. While communicating the long-term vision is essential to getting employees excited about the journey ahead, handing them several short-term wins that they can see and feel is the key to getting them fully on-board. I know at Albert Invent, our end-to-end R&D platform company, we’ve witnessed numerous I’ve witnessed short- and medium-term wins in the magical middle of R&D digital transformations—some of which can be realized in as little as a day.

R&D transformation quick wins

Within one day:

Faster shipment of samples with auto-generated safety data sheets (SDSs) and labels. Generating SDSs and product labels is a painstaking and stressful process that can take days or weeks to complete, significantly delaying a company’s speed to market. By automating this process, GHS-compliant documentation can be produced in seconds with the click of a button. Companies can begin auto-generating SDSs and product labels the moment a formula is transferred onto their platform during the onboarding process. This means lab and regulatory employees see and feel this benefit even before the entire solution is implemented.

Within one week:

Better raw material inventory management across locations, leading to significant cost savings. Did you know that more than 40 percent of most labs’ samples remain unopened due to over-ordering products that are in stock? This is shocking. By digitalizing the lab and using an inventory management solution, R&D teams gain visibility into all inventory items, including their available quantity on-hand across multiple locations and labs as soon as they are onboarded onto the solution. This means teams can immediately begin to assign tasks to certain locations with available inventory instead of purchasing new materials, which allows them to eliminate the cost of over-ordering and inevitable delays in sourcing and sending raw materials from third-party vendors. Materials managers can also set minimum quantities for critical or heavily used raw materials, automatically generating alerts when amounts are running low to avoid expensive rush fees and costly project delays. While teams may begin to recognize this benefit on day one, it may take them a few days before they have a raw material shortage that allows them to take advantage of this helpful feature.

Within one month:

A reduction in time, frustration, and mistakes by automating the collection of data from equipment. The amount of time a scientist spends manually transferring data on flash drives from one device to another is astonishing. We often call it the “sneaker network” because of all the walking a scientist must do during their day. By integrating lab equipment with an R&D digital platform, all data is automatically transferred to an accessible platform for scientists to instantly analyze results and insights that will inform their next iteration. The change from the “sneaker network” to automatic data collection directly from equipment may take some time and training before labs can implement this process, but most R&D organizations should be able to see a noticeable improvement within a month or so of moving over to the new solution.

Within one year:

Saving time and energy searching for historical data by tapping into insights from similar experiments. When data from past experiments is locked up in spreadsheets, siloed systems, and paper notebooks, many scientists must spend hours poring through lab records to figure out the best approach for their experiment. By implementing an end-to-end R&D platform, scientists can easily draw from past and current work within seconds, instantly learning from their peers’ past experiment results and eliminating redundant rework. While historical data is available immediately after an R&D organization’s data is migrated onto the new solution, the value of leveraging large amounts of historical data may take time as more and more data is aggregated onto the platform.

Minimizing experimental iterations by conducting AI-driven predictive simulations on different formulations. Scientists want to design the most efficient and effective experiment possible; however, without an end-to-end R&D platform, they often must rely on educated guesswork. With an end-to-end data ecosystem is in place, organizations can harness machine learning (ML) and artificial intelligence (AI) tools to simulate and predict which formulations have the greatest chance of success before they even approach the bench. This gives scientists the advantage of a stronger starting point with their design of experiments so they can achieve breakthroughs faster, and with less waste. While AI-driven insights can be accessed as soon as the data is migrated over to the new solution, AI will work best once an organization has accumulated a robust amount of data, which can take several weeks or months depending on the nature of work being conducted in the lab.

Embrace the change

Change doesn’t have to be painful, but it should be meaningful. Digital transformation will ultimately be necessary for every R&D lab. The key will be finding the tools that simplify the process while delivering something exponentially better than what already exists—and those that do so in short order. After all, who is willing to abandon processes that clearly work if there isn’t an immediate payback? There must be a return—in experience, in outcomes, in time spent, or on some other tangible measure—that ensures employee buy-in.

While the path of digital transformation in R&D is complex, it can be valuable, even for those that are the most resistant to change. The key is making the most of the magical middle by using tools that will deliver the mid-journey victories, such as improved efficiency, cost savings, and faster innovation.

Ultimately, the success of digital transformation in R&D isn't just about reaching an endpoint; it's about leveraging the continuous opportunities that arise throughout the process. In doing so, companies not only empower scientists to find new discoveries, but also position themselves to stay competitive in a rapidly progressing scientific arena, maximizing the benefits of both the journey and its final outcome.