Chances are that you use some form of artificial intelligence in your daily life. Virtually all smartphones are equipped with a personal digital assistant, and their making their way into dozens of new devices.
While artificial intelligence might be easy to accept as a consumer, it takes a much more involved decision-making process when enterprises apply AI, machine learning and deep learning to their business.
That’s becoming increasingly important as AI-related startups rack in mountains of cash to generate hype and help move the technology ahea, according to Lux Research, which issued a new report calling on decision makers to be patient when investing in AI.
The Boston-based business research firm’s report on making decisions based on thorough research of AI and related technologies tells companies that while the technology is improving, some is just too immature or limited — especially for tasks that move beyond pattern recognition and to those that require long-term human reasoning.
Rather than focus on marketing and the novelty of the technology, companies should first:
- Understand outcomes Ai will provide for their business
- Focus on it’s capabilities instead of the hype
- Research to understand if the technology is mature enough to avoid risk
- Identify challenges to implementation and maintenance
Despite emerging solutions to mitigate challenges in implementation, many organizations struggle to quickly implement the technology.
“With all of these advancements in machine learning and data analysis, it easy to understand why AI has become such a hyped technology,” the report notes. “However, what is less clear is how these performance gains translate into useful business objectives as well as which applications are achievable with today’s AI technology and which are not.”
In order to wade through the sea of artificial intelligence vendors and projects, clients need an outcome-focused framework to determine which applications to focus on and how to mitigate challenges when implementing the technology, the organization said in the report.
Taking a technology-first approach, the organization said, will lead to many failed efforts. In many cases, the technology is still years away from the full solutions promised, and cultural elements present significant challenges to successful implementation.
“As the number of “AI” vendors and projects – many of which claim to use the latest deep learning or machine learning techniques – continues to skyrocket, clients need an outcome-focused framework in which they can determine which applications to focus on with today’s tools and how to mitigate challenges preventing successful implementations,” the report said. At the end of the day, it doesn’t matter what elaborate algorithms a product uses; instead, clients should strive to understand what artificial intelligence can actually do for them.”