- Price sensitivity to more expensive technology,
- Ambivalence towards new processes, and
- The risk of uncertainty that changes in production will result in little or no return on investment (ROI).
Although this has long been a problem for medium-sized companies, the analysts predict that it will get worse by 2025. The reason: The next wave of technology (GenAI, Digital Threads and Industrial Metaverse) will be expensive projects that will be fraught with many question marks. According to the analysts, these technologies are not yet sufficiently developed and have not yet been extensively tested in practice by the large manufacturers to gain the trust of smaller companies.
8. World domination by humanoid robots is not happening (for now).
There are now a dozen humanoids that are as advanced as Atlas from Boston Dynamics – and remote control is also making impressive progress. However, market researchers assume that humanoids will hardly get beyond this point without a radical change in AI.
ABI Research therefore predicts an AI-related turning point for humanoids will only occur between 2026 and 2027. Until then, providers should follow the successful example of Agility Robots and adopt the Robots as a Service (RaaS) rental model, the analysts recommend. The work of robots is equated with the hourly wage of human workers and metrics are created to quantify robot performance. They should also develop teleoperation capabilities to address edge cases during deployment and help decision makers determine benefits and minimize risks during deployment.
9. Data monetization remains difficult
Companies want to monetize the huge amounts of data they have and see Enterprise Data Fabric as the silver bullet to achieve this. However, ABI Research predicts that not much will happen in this area until 2025: the enterprise data fabric market is still relatively young and providers are focusing on data management solutions that use different data sources distributed across different environments as a first step towards monetizing data connect and integrate.
However, according to market researchers, this is not sufficient. In the future, data management solution providers would also need to provide AI solutions that help companies improve data tracking and data lineage analysis. This will be an important step towards providing data monetization services such as data-as-a-service or AI/ML predictive analytics models.
10. Hardly any AI-based solutions in the supply chain
Even if AI and GenAI are finding their way into all industries, according to ABI Research, AI-supported solutions will only be used in a limited and isolated manner in the supply chain. Traditional AI is primarily used for advanced data analytics, while leading providers would also start offering predictive analytics and system-generated problem solving for decision support.
According to ABI Research, these solutions are gradually being used by user companies. However, implementations are still at an early stage, with efficiency limited by poor data quality, inadequate system integration, lack of internal know-how and lack of trust in AI. According to the analysts, there are also GenAI solutions, mainly in the form of copilots and chatbots, whose main function is to query data in a more intuitive way. However, the solutions have struggled to achieve a radical return on investment (ROI) to encourage wider adoption.
According to ABI Research, many large software providers are currently conducting joint development projects with customers related to the supply chain. However, finding the best solutions will take time, so 2025 will continue to be a testing year for AI and GenAI in the supply chain.
