![]() ![]() Nickolaisen, however, said he thinks bringing humans into the loop has the potential to negate some of AI's power and drive. "Having the business intelligence, GRC and human-to-machine collaboration capabilities to see and manage the robot as a virtual team member is going to de-risk AI actions, while also ensuring that AI can continuously learn from human team members and managers how to do its job better and avoid ethical and moral issues, as well as bad decisions," she said. "A human in the loop is both the expert that can support the preproduction training of AI, as well as be the colleague and manager of the AI robot once in production." "Just as we have management and governance oversight of our workforce, AI should also be put under this umbrella," Goetz said. Machine learning is great at analyzing data to create models that make predictions, recognize patterns and automate decisions, but it lacks human reasoning capabilities, the report stated. ![]() Companies will bring humans back into the loopįorrester predicted that 10% of firms using AI will bring human expertise back into the loop in 2019. "Data needs to be interpreted outside of what database, file or table it comes from and be representative of environment, influences, intent, behaviors, decisions, actions and outcomes," Goetz said. Simply migrating data to the cloud for data scientists to work with ignores semantic design principles that allow AI to gain a deep understanding of the business and customer. "Data is a digital twin of the business, not digital exhaust," she said, explaining that CIOs must address the data problem in AI in a new way. Goetz named data quality as the aspect of AI that is most pertinent to CIOs - and the most essential of Forrester's AI predictions. "Data is messy, and it takes times and effort to cleanse data. "Data doldrums are and will continue to be on the list for the foreseeable future," said Niel Nickolaisen, vice president and CTO at human resource consulting company O.C. For this reason, Forrester said the tables will turn from AI to IA - information architecture - for the majority of firms that have already dabbled in some form of AI, as they realize you need an AI-worthy data environment to utilize AI. The firm predicted "data doldrums" will continue to drown the majority of firms embarking on AI in 2019. 1 challenge for AI adopters is sourcing quality data. Data quality will remain a challengeįorrester said the No. "Swapping out old algorithms with an AI algorithm only provides limited and short-term lift," said Michele Goetz, analyst at Forrester and co-author of the report.įorrester's 2019 AI predictions report focuses on its five top predictions, based on the thousands of questions Forrester clients have asked them about AI in 2018 and the firm's in-depth research. This will help enterprises rise above the widespread epidemic known as AI washing - i.e., when a company's brands and products claim they involve AI, but the connection is tenuous or nonexistent. What's that mean for enterprise AI journeys? Forrester predicted pragmatic AI - to augment, automate and personalize - will take hold in 2019, as CIOs let go of their grand, long-term AI ambitions and realize they have to work with what AI can do today, not what it will do tomorrow. The stakes for IT teams remain high, but Forrester believes things are looking up in 2019, as CIOs take a more pragmatic approach to digital transformation and focus on building a more durable and effective foundation upon which to innovate and scale operations. That's according to a series of Forrester Research reports that address the gap between IT ambition and execution in 2018 - especially when it came to implementing AI - and predict what's in store for 2019. Dreams met a harsh reality in the form of expenses, insufficient resources, cultural resistance and the realization that digital transformation is not easy. ![]()
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