Last year, I wrote an article for HackerNoon about remote R&D and how it can help businesses accelerate their pace to innovation. This year, I’ve revisited the topic and come across some mind-blowing stats and facts about the global R&D market, which inspired me to explore further:
To answer these questions and to better understand how R&D projects differ from mainstream software engineering projects, I’ve talked to Bogdan Popescu, Technical Director of R&D Embedded Division at Rinf.tech, a Romanian technology solutions company.
Bogdan has more than ten years of hands-on experience with R&D project development and has made his way from R&D Program Manager to R&D PM to Head of R&D. In particular, he specializes in managing R&D projects across AI/ML, Virtualization, and IoT/embedded domains.
My current role covers two main areas. On the one hand, I’m in charge of all projects running in our R&D and responsible for their delivery to customers, meeting deadlines and quality criteria, building trust with customers, and paving the way for long-term collaboration. That’s my main focus.
However, on the other hand, my job is to identify potential new technology use cases that might be able to help other companies solve their tech limitation issues and have a positive impact on their business growth. From this perspective, we build proof-of-concept (PoC) projects, which imply a high level of experimentation and playing around with technologies and tools.
Currently, we have around 60 people in our R&D team that are involved in current customer projects and PoC projects development. For customer projects, we typically recruit engineers from our available talent pools in Eastern Europe.
For PoC projects, we don’t hire permanent resources. We pick people with required skills from existing customer teams (who temporarily have a limited work scope or want to volunteer to work on R&D projects as their pet projects) to run PoC experiments. They see it as a way to advance their knowledge and boost professional growth, as most PoC projects are very challenging and require non-trivial approaches.
I’d say that each R&D project is sort of a terra incognita for us. Mainstream projects are normally well-defined, while in R&D projects, we have many unknowns, we have no idea of the final project scope and size. We may (or may not) know the destination, but we have no clue how to get there.
As such, any R&D project is an iterative process where we have initial hypotheses and start building software components to validate them or have lessons learned for informed decision-making. R&D projects are cyclic as we must revisit assumptions several times, pivot, improve, correct – it typically takes a few iterations before getting the real result.
This is an example of a digital twin PoC solution built at Rinf.tech R&D that uses HoloLens to enable remote control of the robotic arm.
In our R&D, we use a lean, step-by-step approach, i.e., we start small and scale. As such, the budget is added incrementally as the scope becomes clearer.
Our PoC projects help build long-term strategic relationships with our customers. When they come to us with great business ideas (that couldn’t be brought to life previously due to technological limitations) and see us find a workaround experimentally – they’re amazed. As a result, they get more confidence in our capabilities.
Another takeaway is – you don’t have to underestimate R&D projects. I’ve seen many times in my career when a project that starts as a small PoC, one eventually transforms into a robust software engineering project due to new ideas and solutions being identified.
I wouldn’t say there is direct competition between us. As we don’t know what solutions other R&Ds are working on, it’s hard to tell if we’re trying to build the same things or not.
Our approach is applicable to industrial domains mostly. We build and deliver commercial industrial solutions. We don’t experiment for the sake of experiments. First and foremost, we experiment to add value to our client engagements. Tech giants can afford to launch R&Ds just to enrich academic research on the latest technologies or to build new products from scratch, spending huge budgets.
We do have plans to contribute to academic knowledge by sharing our PoCs and their use cases. By the way, some time ago, our R&D team contributed to Intel’s white paper on deep learning.
So, while we have a different R&D focus, the philosophy is similar: we try to build new things with existing technology.
R&D helps businesses turn their current tech limitations into future business opportunities, capitalize on innovation, and gain a strong competitive advantage.
Mostly enterprises. They outsource their innovation projects to avoid heavy upfront investments in staffing and retaining an expensive in-house R&D team, and building internal expertise and domain knowledge. Enterprise tech decision-makers choose to piggyback on 3rd party expertise, as they appreciate getting external feedback before asking for a bigger budget and committing to full-fledged product development.
Another group of companies that leverages our R&D capabilities is SMBs. Their customer journey with us starts when they see our R&D PoC demos and realize that’s exactly the solution that can help fix their most significant problem or differentiate in the market. They want us to conduct more in-depth research, and that’s when they decide to outsource their pilot R&D project.
Startups, however, rarely use external R&D. For most of them, a technology product is at the heart of their business and monetization model, so they prefer to accumulate knowledge and develop deep domain expertise in-house.
We have mixed approaches. Everything depends on the project. For some projects, we grant full IP rights to the client. For others, we have shared IP. Let’s say the client comes to us with ready-made software components, and we only do the integration work. In this case, software components IP will belong to them, and we’ll keep IP for the integration solution we’ve built.
Wrapping up, the evolution of software development tools and processes and the rise of 5G, IoT, edge computing, and quantum computing will further push integrations, deployments, and search for new business models, technologies and standards – and that’s where R&D can be the main catalyst of change.
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