Autodesk and Altimeter have conducted a global research project that has revealed that digitally mature companies are taking their business’ innovation processes to the next level by embracing the next wave of digital disruption known as “convergence.”
“Convergence is the blending of previously separate technologies, processes, and data to create new combinations of products, services, and experiences,” says the report. These new combinations are having a direct impact on reshaping current industries and structures within them. Most businesses today acknowledge that convergence will impact them in some form.
The global pandemic has brought about an acceleration of digital transformation technologies. Innovative practices like virtual communications, personalized digital engagement, immersive modeling, rapid prototyping, and efficient collaboration are now mainstream due to workforce transformation during the COVID-19 pandemic.
So what comes next?
Altimeter and Autodesk surveyed 749 leaders from companies in the US, UK, Germany, France, Japan, and China across Architecture, Engineering, Construction (AEC), Design & Manufacturing (D&M), and Media & Entertainment (M&E) industries. What they found is the disruption caused by the pandemic has created two classes of companies:
- Low-Level Digital Maturity — this first class of companies is still struggling to implement innovative practices and are still in the process of catching up.
- High-Level Digital Maturity — this second class of companies had already implemented the latest innovative practices and have thrived during the global pandemic. This class of companies is now prepared for the next wave of digital disruption, a practice known as convergence.
The research found that 58 percent of respondents have either given thought to convergence or acknowledged it is one of the most critical parts of their business.
The global research revealed a key takeaway—that digital maturity is a strong predictor of how much a company believes it will be impacted by convergence. To determine the degree of digital maturity respondents were asked to self-select one of the following stages to define their organizations: (see above
- Stage 0 — Digital transformation is not a priority for us, nor do we expect it to become one in the short term.
- Stage 1 — We’ve just begun to build a business case for digital transformation.
- Stage 2 — We’re starting to understand customer journeys, improving digital skills, mapping processes, and seeing early traction.
- Stage 3 — We’ve begun to digitize our operations at scale, but modernizing platforms and processes is happening on a departmental basis.
- Stage 4 — We have digitized operations and are now focused on integrating them so that data can be used more strategically across the organization.
- Stage 5 — We have laid a strong digital foundation and are now focused on leveraging data and AI to optimize processes; products and services; and customer experiences.
For companies that said they were stage 5 (the highest level of digital maturity), 59 percent said convergence was one of the most critical influences on their business. As digital maturity decreases, so too does the recognition of the importance of convergence as a disruptive trend.
The report notes the logic of less mature companies seeing converge as less important to their business operations makes perfect sense. This is partly because they are playing catch-up on digital transformation. Their eyes are focused on the next steps for them. If you look at the chart above there is a wide gap between stage 5 firms and the rest as only stage 5 firms are mature enough to be looking at the next possible steps to take with digital transformation and convergence is a big one.
The report suggests that less-mature firms shouldn’t wait to think about convergence because they can still catch up with other digital transformations while laying the foundation for convergence. The argument is that the earlier a company lays the foundation for convergence the deeper the rewards once they get to that stage.
Levels of Convergence
The research has revealed four levels of convergence:
- Process Convergence — prior discrete processes and workflows are now being connected across business for greater efficiency and integrated goals
- Technology Convergence — technologies like Cloud, IoT, AI, Supply Chain Management, AR/VR are converging to create new cases and solutions.
- Data/Information Convergence — prior silos of data or information is now more accessible and being used across business and industry to power a range of new products and services.
- Industry Convergence — prior discrete industries are becoming more similar and connected, creating new opportunities for value creation.
The use of these convergence levels enabled the Autodesk and Altimeter researchers to chart the impact of each type of convergence and how it varies by industry, as some industries are further along in convergence on these different levels. Here at Architosh in our research studies and reports, we offer the industry, we would also say these levels are the major vectors of convergence and that they are aided along by various institutional alignments and market dynamics. For example, in the EV (electric vehicles) industry transformation, government policies like infrastructure bills aimed at increasing EV charging stations are forms of institutional alignments that overlay market dynamics like private investment in the formation of more EV companies. These two items compound each other creating exponential curves on charts and vectors that represent direction and acceleration.
Despite the clarify of benefits coming from convergence, some firms face significant challenges in implementing the steps needed in order to fully prepare themselves for convergence. These include:
- Culture — where firms fall on the spectrum of resistance to the encouragement of change. Many firms, particularly those with legacy systems and practices, tend to have high levels of resistance to change.
- Data for Machine Learning — many companies are concerned about feeding AI engines outdated or biased data and there is general concern about deploying AI incorrectly.
- Designing for Convergence — convergence requires designers and project planners to be far more forward-thinking, including expansive, visionary, and disciplined thinking than they have deployed in the past. This requires training and the use of tools that can help facilitate this kind of thinking.
- Hiring the Right Skills — As with other disruptive innovations, hiring people with the right skills to thrive with areas of convergence is challenging. These skills are scarce and in high demand at the start of the disruption curve.
“By assessing their digital maturity, and top investment priorities, companies can use this data to benchmark themselves and chart a roadmap for transformation,” says Charlene Li, Founder and Senior Fellow at Altimeter. “Doing so can effectively enable businesses to prepare for any type of digital disruption, and even thrive because of it.”