AI has truly revolutionized process automation software—especially as providers rushed to capitalize on the increasing awareness of the technology after the introduction of Chat GPT exactly one year ago. Nearly every major provider of intelligent automation software has integrated generative AI into their platform as a way to simplify the development process.
As Chat GPT turns a year old on November 30, Automation Today heard from Maxime Vermeir, senior director of AI strategy at ABBYY about his thoughts on how generative AI has affected the intelligent automation space and what’s in store for AI in general heading into 2024.
Automation Today: Generative AI was clearly the technology that garnered the most attention by intelligent automation providers and touched off a bit of a land rush to get it integrated into existing solutions. Was it worth it?
Maxime Vermeir: The C-suite’s sticker shock to the hidden costs and ecological impact of using generative AI will knock reason back into their AI agenda. They’ll discover that the business challenges they’re facing can be solved with narrow AI applications—90 percent of it originating from needing access to, and human-like understanding of, their own data and processes. Using generative AI today to search and summarize data consumes 10 times the energy of a normal search – it’s simply unsustainable and is not relevant for most business cases.
AT: What will most businesses realize about AI?
MV: The focus will shift from generative AI to more specialized, contextual AI/ML solutions that address specific business problems effectively. These tailored solutions promise high accuracy and straight-through processing in real-world scenarios. Unlike the broad strokes of Generative AI, specialized AI digs deep, offering precise solutions to complex business problems.
AT: Can you give us an example of this?
MV: A Transport & Logistics case study I saw [showed how] a highly accurate AI-model, trained on thousands of bills of lading extracted data from 44 million bills of lading issued every year, processed by at least nine stakeholders at 12 touchpoints. The tasks the C-suite believes they want to use generative AI for can be easily solved today using alternative AI models like that. As a result, smaller AI models will rise in prominence in an effort to offset energy requirements, opening opportunities for AI companies to guide customers towards more efficient approaches to AI.
AT: Do you see private industry and governments providing more guide rails for AI in 2024?
MV: As businesses and regulators demand more transparency in AI decision-making, advancements in Explainable AI will gain momentum, helping to demystify complex AI models and foster trust among users and stakeholders. Also, as AI technologies continue to permeate various sectors, regulatory bodies will likely ramp up scrutiny to ensure ethical use and data privacy. This will also include measures to ensure that claims made by AI vendors are accurate and verifiable.
AT: How will organizations adjust to the evolving environment?
MV: Businesses will have to address the gaps in understanding between AI teams and the business side of the organizations they serve, as the lack of understanding and validation of AI results within operational contexts will become more apparent. Businesses will structure AI teams and tool usage to incorporate business knowledge that can better guide AI goals and interpret results. Inversely, business users will come to better understand the meaning of artificial intelligence and the various technologies that support it, such as machine learning, in order to drive successful outcomes.
