Innovating with purpose: What you should know before launching new technologies

Dragos Munteanu 02 May 2024 3 mins
RWS Genuine Intelligence
Launching an ‘industry-first’ or innovative new technology can spell the difference between market leadership and obsolescence. The tantalizing glow of emerging tech often lures businesses into a frenzy of adoption, yet the rush to implement without purpose can result in a tangled, disproportionate tech stack – riddled with risks that offer disappointing customer experiences.
 
The eagerness to harness these models often outpaces their maturity. Businesses, in their haste, may undervalue the necessity of robust testing and the potential support needed to launch these models effectively within their current technological matrix.

Purposeful innovation

In our recent Genuine Intelligence ™ report, which explores the future of human-machine collaboration, we delve into the theme of ‘purposeful innovation’. While we appreciate the need to launch new products and solutions ahead of competitors, our take is that it’s important that companies don’t innovate for the sake of it. It isn't about being first to adopt the latest technology; it's about being sensible in your technological footprint. It starts with understanding the technology landscape, being open to new advancements, and rigorously evaluating potential solutions against your business's specific needs and capabilities. Each step needs to be verified and optimized for the enterprise before integrating the next.
 
So how can you build a framework that supports purposeful innovation?
  • Set clear objectives and boundaries: The first pillar of purposeful innovation is clarity. Establishing clear objectives, such as enhancing operational efficiency or customer experience, is essential. Equally crucial is setting boundaries—defining what areas of the business are 'off-limits' for certain technologies to prevent overreach.
  • Never stop testing and learning: Before the romance of a new technology sets in, build a robust test-and-learn culture. Small, controlled experiments can reveal more about the potential of a technology than its glossy marketing material. Use feedback to improve the technology's alignment with company goals, values, and customer expectations.
  • The integration conundrum: One of the greatest challenges of innovation lies in its successful integration into current operations. A new technology should complement and enhance existing systems without becoming a burden or point of failure. Thoughtful planning and analysis play a lead role in this endeavour.
  • Safeguard against the shadow: Proprietary technology can present a unique set of challenges, particularly in the AI space. Paying close attention to aspects like data governance, model explainability, and vendor lock-in is essential to avoid falling victim to the shadow use and potential hazards of proprietary solutions.
  • Cultivate an innovation ecosystem: No organization is an island. Especially in the rapidly evolving tech landscape. Cultivate partnerships with academic institutions, start-ups, and other businesses. This broader ecosystem can provide insights, training data, and shared responsibility for the development and integration of new tools.
The allure of new technologies can be overwhelming, but the key to successful innovation isn't just about being quick – it's about being smart. By adopting a strategic, test-and-verify approach, enterprises can harness the power of cutting-edge tech without succumbing to the pitfalls of hasty adoption.
 
Business leaders, now is the time to innovate like you mean it. The future is not waiting, and neither should you. But remember, in the relentless sprint towards progress, it's the measured, purposeful steps that often win the race.
 
Learn more about innovating with purpose in our Genuine Intelligence™ report.
Dragos Munteanu
Author

Dragos Munteanu

VP of Research & Development
Dragos manages the end-to-end development life cycle of RWS's machine translation products, with a focus on continuous translation quality improvement via innovation in algorithms and advancement in scalability. He has extensive knowledge in statistical machine translation, machine learning and natural language processing. Dragos has over ten years of experience in the translation industry with significant contributions as both scientist and product manager. He has a PhD in computer science from the University of Southern California and an MBA from the University of California Los Angeles.
All from Dragos Munteanu