The post-AI era - Epicor: A new industrial landscape emerges
There’s a big question in AI.
Okay, there are many big questions in artificial intelligence: which large language model (LLM) should an IT team use, which AI models need to be integrated together and how multi-modal should our multi-model approach be, how expansive are the vector database services underpinning our AI going to be and have we eradicated bias, hallucinations and put up enough information guardrails to be able to build AI intelligence in a robust enough environment?
But there’s (arguably) one core central question i.e. when we will be able to talk about the post-AI era when the new and existing breeds of information-centric smartness become an embedded form of IT utility that manifests itself in all applications with no more fanfare than, let’s say, a spellcheck?
Arturo Buzzalino, VP of products & innovation at Epicor suggests that the post-AI era will see a revolution of automated processes — one that is poised to reimagine the landscape of industrial technology.
“In this new industrial technology landscape, established business software like Enterprise Resource Planning (ERP) will serve as a vehicle for widespread adoption of automated processes and LLMs,” said Buzzalino.
He thinks that these advancements will radically change — and improve — day-to-day life for those operating in narrow and industry-specific spaces.
“For example, in manufacturing, post-AI automation will bring the ability to make live, informed supply chain decisions. In practice, an ERP system running on an LLM platform will have the ability to understand the live availability of materials and expected demand to automatically deliver actionable insights that optimise the manufacturing and supply chain operation,” he added.
What it means for developers
Thinking about what these changes mean for software developers, Buzzalino suggests this shift signifies a transformation in how they approach and interact with technology.
“The integration of AI will streamline coding processes, enabling developers to write and debug code more efficiently through AI-assisted tools,” he proposed. “Developers will also need to focus on creating more intuitive user interfaces and experiences as they use AI to facilitate natural language interactions between humans and machines. This evolution will demand new machine learning and AI integration skills, broadening traditional software development’s scope.”
Where will we go next then?
Buzzalino thinks that AI is going to reach even further than simply ‘automation’ as technology becomes more intertwined with human behaviour.
“For example, empowering workers to talk to machines in plain language rather than code, so they can have a conversation with a machine and ask it for exactly what they’re after. This gives everyone superpowers that they’ve never had before – efficiencies will increase as will return on investment. The post-AI era will unlock value in ways akin to an industrial revolution,” he noted.
Making these comments in line with Epicor’s 2024 Agility Index research study, Buzzalino points out that nearly half of surveyed companies across the so-called ‘make, move and sell’ industries cited concern over escalating costs as the foremost challenge confronting supply chains, with more than half using artificial intelligence, automation, or machine learning for at least one supply chain management application to address.
Make, move & sell
Notably, a higher percentage of businesses (63%) that identify as high-growth – defined by revenue growth of 20% or more over the past three years – have already integrated generative AI into their respective supply chain operations to manage cost and operational challenges.
When workers are empowered to spend more time innovating—what humans do best—that’s where the real value creation happens. That is agility,” said Vaibhav Vohra, chief product and technology officer at Epicor. “Our 2024 Agility Index underscores the growing adoption of AI and other automation technologies as an essential factor in enabling supply chain businesses to better thrive and compete. These cognitive capabilities are coming together to empower workers and their businesses to more readily adapt to shifting market conditions and better serve their customers.”
Nucleus Research surveyed more than 1,700 supply chain management leaders worldwide to understand how they are using powerful technologies like artificial intelligence and machine learning to thrive while navigating challenges like supply chain disruptions, escalating costs and skilled labour gaps.
Survey respondents indicated they are integrating generative AI into digital supply chain operations across various functions such as product descriptions, customer service chatbots, natural language querying, reporting, and in-application assistance. Specifically, the adoption of generative AI in customer service chatbots, noted by 72% of organisations, is highlighted as the most prevalent use case.
According to survey respondents, the greatest hope for the impact of automation technologies lies in increased efficiency and productivity (32%), cost savings (26%), and improved supply chain automation (23%).
