Appendix
Generative AI by industries
This Infosys survey covers 12 sectors across Belgium, Denmark, Finland, France, Germany, Iceland, Luxembourg, Netherlands, Norway, Sweden, and the UK, allowing us to compare how extensively and effectively each industry is using generative AI.
Although most companies are investing in this technology, there is a wide range of spending among sectors. While small differences exist between industries in the frequency of implementation, the only statistically significant differences exist in use cases that create business value. Here we find that the automotive and insurance industries report having significantly more use cases that create value than in the telecommunications, life sciences, healthcare, and the energy, mining, and utilities industries.
Figure 13. Generative AI adoption and business value by sector
Source: Infosys Knowledge Institute
Appendix — Generative AI by sector
Generative AI Radar: Europe
More than a third (36%) of automotive respondents expect generative AI to have the most positive impact on improving user experiences and personalization. Only 5% consider content a key area that will benefit from generative AI, which is significantly fewer than the survey average of 16%.
Automotive: Most positive impact expected from generative AI
Potential use cases
Autonomous vehicle training — Create virtual environments and synthetic data for realistic simulations
User personalization — Power in-car personal assistants, and predict preferred routes and dashboard settings
Product development — Accelerate design and delivery stages, as well as data synthesis and pattern detection
Marketing optimization— Develop customer-centric content and track marketing investments
Maintenance — Create predictive maintenance models built on data from truck and auto parts
Advisory notices — Generate logs and advisory notices from data collected from the vehicle
Road freight — Plan effective and efficient delivery routes for goods
Nearly 40% of consumer packaged goods (CPG) respondents expect generative AI to have its biggest positive impact in content generation and creativity, bucking the trend found in other industries. Overall, the largest group of respondents in our survey (36%) expect generative AI to have the biggest positive impact in enhancing user experiences and personalization. For CPG respondents, that number is only 17%. The proportion of CPG respondents that expect the same in other areas, such as operational efficiency and product development, is too similar to draw any differences.
CPG: Expectations of most positive impact from generative AI
Product descriptions — Create search engine optimization-friendly and engaging product descriptions, enabling mass personalization
QR codes as digital art — Transform QR codes into visually appealing brand identifiers
Visual merchandising — Optimize planograms based on customer behavior and product data
Inventory management — Create logs and recommendations for stock management, including demand forecasting, stock allocation, and risk assessments
Consumer research — Generate synthetic customer data to test retail scenarios and produce summaries and reports
More than 40% of respondents from the energy, mining, and utilities sectors expect generative AI to perform best at enhancing user experiences and personalization — similar to all industries. However, strikingly fewer (13%) expect product development to offer the greatest value compared to the overall survey result of 25%. Significantly more respondents from these sectors (36%) expect improved operational efficiency and automation to be where generative AI has the largest positive impact, compared to 24% overall.
Energy, mining, and utilities: Most positive impact expected from generative AI
Grid management — Analyze data, such as consumption patterns and load distribution, to optimize grid performance and predict problems
Renewable energy — Help integrate renewable sources by predicting output and optimizing resource allocation
Demand forecasting — Enhance the accuracy of demand forecasts, allowing for efficient resource allocation and cost savings
Resource optimization — Simulate mining scenarios to optimize resource allocation, potentially saving costs and time
Environmental impact modelling — Simulate the environmental impact of various mining methods
Healthcare and life sciences respondents were even less likely to think generative AI would have its greatest positive impact on content generation and creativity. Only 8% of respondents expect this outcome, compared to 16% overall. For expected value in other areas, healthcare and life sciences respondents did not significantly differ from the overall survey averages.
Healthcare and life sciences: Most positive impact expected from generative AI
Drug, gene, and protein sequence design — Accelerate drug discovery by designing molecules and proteins with specific properties and optimize synthetic gene design for biomanufacturing
Personalized treatment plans — Analyze a patient's medical history and other factors to generate customized treatment plans
Enhanced medical imaging — Improve the accuracy of medical imaging techniques like CT and MRI by automatically identifying abnormalities
Patient triage — Use chatbots trained on specialized LLMs to provide first-line triage, assessing symptoms to send the patient to the appropriate human professional
Risk management — Use data gathered to model future epidemics and pandemics and predict outcomes based different theoretical inputs
Compared to companies overall, high-tech respondents differed little in their opinions on operational efficiency, content generation, and product development. However, they were more likely to think that generative AI’s biggest positive impact would be in user experiences and personalization. Almost 50% of respondents expected generative AI to shine here; the overall average was 36%.
High tech: Most positive impact expected from generative AI
Code management — Review code and create reports that can be used by the engineers, business analysts, and product managers
Software creation — Generate code, review existing code and act as an assistant for developers
Software analysis — Analyze created code to identify bugs and suggest fixes. Analyze code for adherence to guidelines, creating consistency
Business analysis — Create reports that can be used by the wider organization, including product managers and business analysts
Automation — Automate repetitive tasks, freeing up humans to do more creative or higher-level tasks
Insurance and financial services firms are generally in line with the survey average for where generative AI will have its greatest positive impact. The only exception is that respondents from these two sectors were slightly more likely to think that generative AI would have its most positive impact on user experiences (44% compared to the survey average of 36%).
Insurance and financial services: Most positive impact expected from generative AI
Data analytics — Generate reports to highlight points for narrative storytelling, and flag up anomalies
Code management — Create reports that can be used by the engineers and developers to minimize incidence of bugs
Software creation — Generate code, review existing code, and act as an assistant for developers
There is not enough evidence to say the sentiment of logistics and supply chain management companies were different from the survey average. While it might appear that respondents from this sector were more evenly split on where generative AI would have the most positive impact, this could be a result of fewer companies from this sector responding to the survey.
Logistics and supply chain management: Most positive impact expected from generative AI
Optimization — Automate supply chain tasks and dynamically adjust shipping routes and prices based on real-time conditions
Risk management — Analyze data on geopolitical concerns, weather, industrial unrest to create dashboards that surface issues, and suggest mitigations
Supplier management — Chatbots can facilitate interactions with suppliers and create reports based on insights from supplier data and interactions
Trends insight — Summarize information from external sources including newspapers to understand trends that impact suppliers
Price intelligence — Gather information on competitor pricing and costs and use chatbots to produce insights and reports
Manufacturing respondents were much more likely to expect generative AI to perform best in product development and design. More than 40% of respondents in this sector held this sentiment compared to just 25% overall. Conversely, significantly fewer manufacturing respondents felt the same way about user experiences and personalization. Less than a quarter of sector respondents expected generative AI to shine in this area compared to more than a third overall.
Manufacturing: Most positive impact expected from generative AI
Design and maintenance — Accelerate product design and improve maintenance by forecasting equipment failures
Quality, production, and inventory — Predict product defects, optimize inventory through demand simulation, and aid production planning
Cost management — Reduce costs by streamlining maintenance and design
Inventory management — Simulate scenarios such as weather-driven surges in demand and use historical data to fine-tune production schedules and optimize stock levels
User feedback — Collate reports of feedback from customers to produce recommendations for further optimize design
Sentiments in retail and hospitality were statistically similar to the survey overall. About the same proportion expected generative AI to excel in each of the four areas of potential benefit.
Retail and hospitality: Most positive impact expected from generative AI
Content generation — Create tailored content and campaigns for personalized customer engagement
Data analysis — Speed up analysis of structured data (sales figures), unstructured data (customer feedback), and emerging trends to offer deeper market insights quickly
Customer service — Help overseas visitors plan visits including booking attractions
Translation — Help overseas visitors who don’t speak the local language
Energy management — Analyse energy use patterns to manage energy use on retail premises
Social listening – monitor social media and generate reports based on user conversations and emerging trends to fine-tune content and personalization
Nearly half of all telecommunications respondents expect generative AI to have its most positive impact on user experience and personalization. This proportion is just over one-third among companies overall. Additionally, significantly fewer telecommunications respondents indicated they expect generative AI to perform best at product development and design, with less than 15% holding this view. The overall figure is 25%.
Telecoms: Most positive impact expected from generative AI
Enhanced risk management and maintenance — Automate responses to network and infrastructure irregularities through continuous real-time data analysis
Customer support — Help users with billing queries and orders
Engineer support — Train generative AI on the network topology so that engineers can be guided through tasks with interactive guidance
Software development — Help developers create specialized code and applications more quickly and efficiently
Network optimization — Analyze network data and conditions and generate insights to streamline resource deployment
Network security — Track threats and assess vulnerabilities by analyzing network traffic to identify malicious activity
