Although manufacturing and tech firms have made substantial investments in AI (artificial intelligence), the results have been “mixed,” according to a recent report from Lux Research.
The report notes that companies have been “hampered” because they’ve been using traditional or outdated processes to manage AI solutions. Lux Research’s new report, titled “Stage-Gating Your AI Innovations to Success,” outlines the different challenges businesses face when integrating modern AI solutions. The report recommends ways companies can change their innovation processes to “improve their success rates with AI.”
Dr. Shriram Ramanathan, Director at Lux Research, said that it’s “imperative” to not apply “old thinking” to current problems, especially when integrating AI technologies.
Ramanathan explains:
“The fundamental challenge lies in that the underlying logic in an AI solution is intricately tied to the raw data that it provides insights on. While this allows AI solutions to adapt easily to continuously changing environments, it is also AI’s Achilles’ heel. AI solutions quickly start deviating from their original purpose the minute they are deployed in the real world. Companies need to implement a continuous and ongoing developmental effort to keep their AI models current.”
As covered earlier this month, Lux Research had published another report that covered how companies are now able to more effectively apply digital technology to “enhance the consumer journey for their benefit.”
The report noted:
“Traditional digital solutions like digital marketing and e-commerce have laid the groundwork for the disruptive potential of AI and IoT.”
Jerrold Wang, Lux Research Analyst and lead author of the report (The Digital Transformation of the Consumer Journey), argued that emerging tech such as AI and IoT can potentially “further move the needle in consumer personalization through data collection, thus creating deeper value for CPGs and their supply chains.”
The report from Lux revealed that innovative digital technologies are being applied to improve human-machine interaction. Examples include advancements related to computer vision, voice recognition, and natural language processing, smart cameras and sensors, and augmented reality (AR).
While these are all positive developments, there are still certain challenges related to effectively integrating AI solutions. Lux Research’s latest report mentions that companies have been “instituting Stage-Gate processes to manage innovations for decades.” But they’re not “well-suited” to AI applications because it’s “difficult to lock down a definition for the final product, as the adaptability of AI by its nature prevents a clear definition for a finished product.”
The report added:
“The lack of a clear product definition leads to uncertainty in the business case. This also makes it difficult to define key performance indicators that can measure the effectiveness of an AI application. … there are [also] challenges in keeping AI products current due to its dependence on external data.”
Lux claims that the Stage-Gate model may be applied to AI after making a few modifications. The company recommends defining the product use case “as narrowly as possible while planning for a broad range of scenarios and outcomes.”
It further suggests defining appropriate KPIs to assess the actual ROI of an AI solution “while keeping in mind that these solutions are not traditional software plays but rather bridge the physical and digital worlds.” Lux Research states that businesses need to plan for “real-world deployment” early during the development stage to “get a clearly defined product.”
This can be done by adding or using real-world data while developing the product and also by creating solutions that can be easily tracked and updated. Companies must also make plans to stress-test their products regularly so that they can identify and fix problems during the early stages, the Lux team recommends.
Lux Research predicts:
“Digital transformation of physical industries will evolve more and more toward a framework that emphasizes the simultaneous development and deployment of software solutions.”
