Technology Intelligence and Roadmapping

Introduction and analysis -

Strategic planning and decision-making are knowledge intensive exercises at technology driven organizations, and it requires a constant flow of intelligence on customers, industry stakeholders and technology innovation. Acquiring and validating such dynamic information in a formal, organized process is called “Technology Intelligence”.

My research on technology intelligence and technology roadmapping, led me to a few academic papers (see references below) and I found that the paper by Kerr, C., & Phaal, R. – 2018 (link) was particularly relevant for today’s technology firms. It was interesting to me that the authors integrated the process of technology roadmapping into technology intelligence as roadmapping enables the identification of research and technology gaps.

In my opinion, the approach proposed by Kerr, C., & Phaal, R. – 2018 (link) is useful when an organization is either looking to make incremental upgrades to their existing technology offering or want to replace it with a totally newer version in the same domain (for e.g. Desire2Learn - a traditional Learning Management System vendor developing a product like Lynda.com). But, their approach is not so useful as a tool to discover "new technical breakthroughs" or “disruptive technology”.

Author’s technology intelligence framework assumes that an organization has in-house skills and expertise to identify or search potential technical solutions. This is a huge limitation of this approach as it does not focus on technology innovation in conjunction with signals such as consumer behavior and institutional venture funding from the beginning. It also relies heavily on search and questions asked by key stakeholders. Even the author acknowledged that the framework had an apparent weakness that it fails to recognize the “white space”. For e.g. before autonomous vehicle technology became mainstream by Tesla, Waymo & others - automobile engineers within R&D teams at incumbents like Ford, GM (with no software programming or machine learning algorithms knowledge) would not have searched for key technologies such as LIDAR, object recognition sensors and the use of relevant algorithms in context of an autonomous vehicle. This intelligence gap in disruptive automotive technology innovation is evident from the recent major investments made by the incumbents -

• Ford $1bn in Argo.ai (2017), $500m in Rivian

• VW Group $2.6bn in Argo.ai and

• GM over $1bn investment in Cruise

Based on my professional experience, own research on Silicon Valley ecosystem and knowledge gained from books on innovation management, I’m proposing a new framework (see below) on technology intelligence, combined with technology roadmapping to ensure we can identify potential research and technology gaps in all areas of business model innovation. The framework is inspired by the approach taken by Kerr, C., & Phaal, R. – 2018 (link).

Framework: Technology Intelligence and Technology Roadmapping – (see framework above)

There are 6 steps in the proposed framework -

Technology Intelligence

Step 1 – Gather intelligence

In this stage, organizations must continuously gather intelligence on how the consumer behavior is changing in their landscape and how the institutional funding (Venture Capitalists, Government, Investment Banks) is fueling the growth of specific technology innovations. For e.g., Electric car battery technology - private and government investments in battery technology research has not only led to lower manufacturing costs but also higher mileage per charge. Overtime, it is leading to greater consumer confidence in long distance driving, affordability and rise in the demand for the electric car.

Step 2 – Capture knowledge

To support the previous stage, organizations have to also stay on top of how their competitors are investing their resources and where are they finding new growth opportunities. There is also a need to monitor which industry or government regulations may, positively or negatively, impact their present or future growth. In addition, it is imperative that management teams have an ongoing dialogue with their key suppliers, partners to better understand how their business is evolving in this related ecosystem.

Step 3 - Key findings

In this stage, key findings are extracted by the senior leadership from step 1 and 2. They identify any correlations or dependencies from the gathered intelligence and build hypothesis to better align with the market evolution.

Technology roadmapping

Step 4 – Apply findings to business focused use cases

By testing the findings against the use cases, it becomes easier for the management to classify, prioritize, and validate its relevance and feasibility with specific areas of the business. For e.g., employee growth, development and training.

Step 5 - Apply findings to consumer focused use cases

Along with the previous step, organizations must also strategically align their key findings with one or more of the three areas of business model innovation – core competency, adjacent areas or white space. This consumer focused product/service classification helps organizations in assigning appropriate resources (skills, existing technology, operations, further R&D etc.) to each finding so they can continue to grow and strengthen their position in the market.

Step 6 – Management

To leverage the benefits of this process where market intelligence leads to technology roadmapping, organizations must formally -

• Initiate deeper research using service design approach

• Build a culture of continuous learning and experimentation

• Invest in new technology development and deliver in an iterative manner

• Implement any related process and practice changes

• Execute relevant strategic initiatives on an ongoing basis

References -

• Kerr, C., & Phaal, R. (2018). Directing the technology intelligence activity: An ‘information needs’ template for initiating the search. Technological Forecasting and Social Change, 134(February 2017), 265–276. https://doi.org/10.1016/j.techfore.2018.06.033 (link)

• Loyarte, E., Posada, J., Gaines, S., Rajasekharan, S., Olaizola, I. G., Otaegui, O., … Florez, J. (2015). Technology roadmapping (TRM) and strategic alignment for an applied research centre: A case study with methodological contributions. R and D Management, 45(5), 474–486. https://doi.org/10.1111/radm.12098 (link)