Why Technology Improvement Is Predictable (Yes, Really)

The timeline of innovation across history tends to feel like a volatile lottery, with new technologies surging into the limelight only to disappear just as quickly, while others quietly build momentum beneath the surface. R&D leaders and innovation strategists grapple with the challenge of figuring out which emerging technologies will truly transform industries and which are destined to fizzle out.

R&D executives have always faced intense pressure to drive innovation while meticulously managing risk and budget. They are expected to deliver cutting-edge products quickly, while it is no secret in the industry that rushing the development process can lead to inadequate testing or market analysis, potentially harming long-term success. Simultaneously, they must balance innovation with budget constraints, navigating the "tightrope" between funding advanced technologies and controlling costs.

And to top things off, the sheer scope of emerging technology is also overwhelming. The number of distinct technology domains has exploded from just 215 in 1900 to 265,000 by 2023, which in reality is just "too much data to process and not enough time" for human experts. This information overload makes it challenging to identify which new technologies truly matter.

Moreover, the rapid advancements in areas like AI, Industry 4.0, and IoT force R&D leaders to constantly scan the horizon. They must pick winners that offer long-term benefit while avoiding those that could quickly become obsolete – a high-stakes gamble under tight time-to-market demands. 

Organizational silos can also hinder innovation, as it requires cross-functional alignment that is often time-consuming and difficult to achieve in large firms. In some industries, like automotive manufacturing, a cultural reluctance to change slows the pace of tech adoption compared to faster high-tech sectors, making it harder to justify and integrate new solutions. R&D leaders also report a lack of confidence in addressing high priorities such as understanding the market potential of emerging technologies and the speed to maturity of early-stage tech.

Relying on news trends and scientific publication counts for technology strategy might seem safe, but technologies often begin trending only when they are already winning solutions, putting late adopters at a disadvantage. On the other hand, focusing too much on  certain over-hyped or over-discussed technologies can lead to disappointment when they fail to deliver. At GetFocus we often say, "trend monitoring shows you where the herd is going but the herd is often wrong". Experts themselves, while great at filtering information and strategizing, struggle with too much data to process and not enough time and therefore are at "risk of bias".

A significant challenge also lies in the adoption of early-stage technologies.

75% of R&D leaders report that only about half of innovation projects they attempt to transfer to the business for scale-up and production actually get adopted by business partners.

This is because finding the right market applications and getting executive buy-in is challenging without precise estimates of market impact. Finally, demonstrating positive financial value for R&D digital transformation projects is often not easy, as these projects rarely link directly to revenue growth. This makes it challenging to successfully advocate for an increase in R&D funding.

Here’s a fundamental “GetFocus” insight (because this is the core context behind our methodology): all technology improves, and for the most part, this improvement follows an exponential curve. Think about the relentless increase in computing power or the decreasing cost of solar energy. However, the crucial element often missed is that each technology improves at its own distinct rate. Some technologies experience rapid advancements, doubling their performance in a short period, while others progress at a much more gradual pace. Understanding these different rates of improvement is key to predicting the future technological landscape and making informed strategic decisions.

This underlying concept isn't just theoretical. In 2018, GetFocus worked with MIT to build the world’s first objective technology forecasting system. MIT researchers embarked on a mission to empirically measure progress across 28 diverse technology domains, realizing that while there were many theories about technological advancement, there was a lack of concrete data quantifying how quickly technologies were actually getting better. This rigorous approach led to the identification of functional performance metrics that could track the actual improvement in a technology's capabilities over time. This research formed the foundation for understanding and calculating a Technology Improvement Rate (TIR), a measure of how quickly a specific technology is advancing.

So, where does this crucial data on technological improvement come from? The answer lies in patent data. Patents serve as an invaluable and globally standardized record of technological progress for several key reasons:

• A repository of early signals: Before a technology even hits the market, its fundamental principles and advancements are typically documented in patents. This makes patent data an early indicator of emerging trends, far preceding market reports or widespread adoption.
• Comprehensive global scope: Patent systems are designed to capture virtually all technological progress happening worldwide. This provides a global perspective, eliminating the blind spots that can arise from focusing solely on regional trends or specific publications. The number of distinct technology domains has exploded, highlighting the need for a comprehensive data source like patents.
• Standardized structure: The structured nature of patent documents, with sections describing the invention, the prior art it improves upon, and the reasons for the improvement, makes them ideal for analysis using artificial intelligence.
• Perfect for AI processing: The standardized format of patent data makes it highly suitable for analysis by AI.

To calculate a technology's Improvement Speed, two key metrics derived from patent data are crucial: Cycle Time and Knowledge Flow.

1. Cycle Time essentially measures how frequently new generations or significant advancements of a technology are developed. It’s the time it takes to take a "step" forward in technological development. A shorter cycle time indicates a more rapidly evolving technology.
2. Knowledge Flow quantifies the magnitude of the improvement or the "size" of the step forward that a new invention represents compared to previous ones. A high knowledge flow signifies a more significant leap in technological capability.

By analyzing the Cycle Time and Knowledge Flow from within patent data, it becomes possible to objectively measure a technology's historical rate of improvement and, crucially, to predict its future trajectory. This approach moves beyond subjective trend watching and provides a data-driven understanding of which technologies are truly winning the race. As GetFocus's forecasting method demonstrates, understanding these metrics allows for a complete analysis of any technological domain, eliminating blind spots and enabling the early identification of winning technologies. This quantitative insight helps avoid the pitfalls of relying on the often-misguided "herd" mentality in technology adoption. 

GetFocus can instantly provide an overview of emerging technologies, spot winners early, and compare and analyze technologies quickly. This speed is a significant advantage over traditional methods that can take months.

The ability to predict technology improvement, grounded in empirical data and rigorous analysis, offers a significant competitive advantage. By understanding the underlying rates of progress, R&D leaders can make well-informed investment decisions, avoiding the pitfalls of investing too early in hyped technologies that may not mature as quickly as expected, or too late in rapidly advancing fields. This predictability allows organizations to outperform competitors who lack such forecasting capabilities, enabling proactive strategic planning and the identification of disruptive technologies before they become mainstream. Instead of reacting to market shifts, you can anticipate them, ensuring your organization stays ahead of the curve. As clients like Moët Hennessy have experienced, achieving more in a week with GetFocus than previously possible in nine months, gaining these objective and quantitative insights can dramatically accelerate R&D efforts and lead to more impactful outcomes. GetFocus helps companies like BASF identify key disruptive technologies.

Curious to see the future of technology unfold? Try GetFocus on a technology you’re currently tracking and witness the power of predictable improvement firsthand. Discover how analyzing Cycle Time and Knowledge Flow can provide you with the edge you need to navigate the complex world of innovation with confidence.