Strategic Innovation Research with AI and Text Analysis

Strategic innovation can be seen as an evolutionary adaptation mechanism that allows organizations to thrive and adapt through a rapidly changing landscape. One of the most important components of any innovation strategy is the ability to understand the market, identify emerging trends, and define potential avenues for technological development. Text analysis coupled with AI technologies can play an important role in this process as they can reveal customers' needs, identify promising areas of research, expose the gaps in the existing discourse.

In this tutorial, we will demonstrate how text analysis and its AI-based implementation in InfraNodus Innovation and Trend Research app can be used to enhance strategic innovation and consulting workflows.

Structural gap indicates a potential idea for innovation

Our approach is based on addressing the six pillars of strategic consulting:

  • 1. Understanding the Customer (creating the value)
  • 2. Defining the Trends in the Market (studying research and competition)
  • 3. Revealing the Structural Gaps (looking for untapped opportunities)
  • 4. Defining the Strategy (e.g. routine vs disruptive)
  • 5. Establishing Cooperation across Organizational Units (e.g. via a networked knowledge base)
  • 6. Leadership and Accountability (implementation and deliverables)


Understanding the Customer: Will Innovation Create Additional Value?

Innovation will have value if can provide some benefit for potential customers. This benefit can be manifested through some other tangible or intangible benefit: better quality, increased resilience, savings, higher productivity, efficient technology, and so on. That's why the first step is to study the problems that customers have and how they are currently solving them.

For example, if we're in the business of data science, we could gather feedback with an open-survey survey to extract the main topics and problems that exist in this field. We could then see if there are any potential avenues for developing innovative products and improving existing technology. Using InfraNodus, we can identify and visualize the main topics that bother our potential B2B and B2C clients:

Analyzing open survey responses of customers who work in data science

The visualization above shows the main concepts that come up in open survey responses and the topical clusters they form. InfraNodus then applies GPT AI to generate category names for each cluster. Therefore, it can be seen that most of our customers' problems are related to:

  • • lack of documentation for datasets
  • • time it takes to perform work
  • • data matching
  • • data cleaning

These are concrete indications where we can generate added value for our potential clients: for example, we could create a platform for exchanging quality clean datasets with validated documentation.

The workflow above is based on the demand-pull approach, which is based on identifying customers' problems and figuring out how the company's technologies can solve them. If an identifiable market does not exist yet, this same workflow can also be used for a supply-push approach: developing a technology and then finding or creating a market. In this case, we can study adjacent market discourse first to reveal any gaps that our technology could fulfill.


Defining the Trends in the Market: Research and Competitive Intelligence

We can use text analysis to study the current market discourse in order to identify the trending topics and — more importantly — structural gaps. Multiple sources can be used to conduct this study: scientific papers on a certain topic, search results in Google Scholar, corporate websites, industry exhibition catalogs.

InfraNodus has multiple import apps that can get the data from the leading scientific databases, such as Arxiv and PLOS, Google Scholar, scrap websites, news, analyze your own data in plain text files or spreadsheets. For instance, here's a visualization that analyzes the texts from the Hannover Messe expo — a leading European event on manufacturing and technology:

Analysis of the main topics from Hannover Messe expo

The visualization above visualizes the topic descriptions for Hannover Expo 2023. InfraNodus first builds a graph of concepts to show how they are related in a particular discourse and then applies GPT AI to generate category names for the topical clusters identified. After combining some of the insights derived by the AI and adding qualitative interpretations of topical clusters, we can identify the following main topics at the Hannover Expo 2023:

  • Global Data Strategy & Software Development (29%)
  • Climate and Energy Sustainability (21%)
  • Digital Infrastructure & Materials (12%)
  • Autonomous Robotics Technology (12%)
  • Logistic Economy & Production Efficiency (11%)
  • Manufacturing Automation (10%)
  • Other (5%)

Even though Hannover Messe is a manufacturing trade event, most of the topics are in the realm of data and software development. It is no surprise: machine learning workflows and software technology can optimize manufacturing processes and help save energy and resources, so it is a trending topic. Coupled with our insight into the poor quality of machine learning datasets obtained in the previous section, a potentially interesting innovation could occur in the field of improving machine-learning datasets that could be shared by various manufacturing industries. Alternatively, we could offer technology that saves hundreds of hours of work for data scientists in these companies helping them clean the data and process it in a much faster and more efficient way.

A similar approach can be used to perform in-depth competitive intelligence research. For example, we can compare the market analysis we just performed to corporate mission statements. InfraNodus has a "differential" comparison mode that reveals the differences between the two discourses: i.e. what is present in the market discourse that is not present in the company's discourse. This difference presents an opportunity for further growth.


Revealing the Structural Gaps: Finding Untapped Opportunities

The advantage of text network analysis is that it not only allows us to see the main concepts and connections between them but also — the structure of the discourse. Once the structure is available to us, we can reveal the structural gaps: topical clusters and high-level ideas that should be connected but are not yet, pointing to potential innovation areas.

InfraNodus has a built-in structural gap analysis tool that identifies those clusters and proposes AI-generated ideas to bridge them together:

Structural gap indicates a potential idea for innovation

In this case, InfraNodus proposes a link between the topic of "Energy Sustainability" and "Manufacturing Automation", using AI to generate a business idea: an automation process that would focus on efficient energy use.

The structural gap approach is a very powerful way to detect lacks and, thus, opportunities in the current market, because it takes into account the topics and clusters that already exist in the industry but connects them in a new way.


Implementation: Building a Knowledge Graph

Once we defined customers' problems and market opportunities, as well as the structural gaps in the current market discourse, we can proceed to implement this strategy into practice. One of the most important elements of any R&D process is cooperation across organizational units. Frequently, various divisions within an organization tend to focus on securing resources for their individual objectives, rather than prioritizing the overall strategic vision. Therefore, it's important to implement organization-wide knowledge base that can allow every unit to see the big picture. A knowledge base can be used to synthesize the data and informational resources available in an organization and facilitate cross-disciplinary exchange.

InfraNodus can be used as a knowledge management system. Its enterprise solution can ingest and process internal documents (which can be stored on the organization's own servers) and reveal the connections and gaps between them. Think of it as a summary of your organization's knowledge where only the most important parts are shown. The knowledge graph built using text network analysis technology can be used to identify the main clusters of ideas and the gaps between them. Those gaps can then be targeted to produce innovation through cross-disciplinary collaboration. The knowledge graph can also be used to estimate correlation between the ideas generated in the R&D process and the current market discourse.


Try It Yourself

Try this approach yourself using InfraNodus and your own data:

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