Supply chain data and manufacturing data helped many industrial organizations achieve what had been the pinnacle of success — lean manufacturing or just-in-time manufacturing. You order just the right amounts of raw materials to make the number of your products that are being ordered, and you keep very little inventory on hand. The model worked well in the predictable world and kept production costs down and maximized efficiency.
But like so many other things, the pandemic broke all that. The data that went into the system provided a retrospective of what the world used to be like, but not anything that could predict future demand.
“The world had perfected the art and science of just-in-time manufacturing and inventory management,” says Nitin Mittal, U.S. AI co-leader and principal at Deloitte Consulting. “Now organizations are realizing that they do need to hold some inventory at some warehouses so that they have product availability. That means organizations need to rethink what their underlying cost structure is going to be.”
This is one of the areas In which organizations are now employing technologies such as AI, to help provide a more accurate, real-time picture of the business, Mittal says. It is one of several use cases for enterprise AI he and co-author Irfan Saif examine in a new reportfrom Deloitte, The AI Dossier.
AI is one of several technologies that Deloitte says falls under the larger umbrella of digital transformation, and many of these technologies saw an acceleration in their deployment as companies worked to respond to the changing economic conditions caused by the COVID-19 pandemic.
“We have absolutely seen changes in how enterprises have approached AI and other digital transformation,” says Mittal. “Prior to the pandemic a lot of enterprises have been contemplating their AI journey and thinking about what investments could be made, how should they get started, and how could they actually undertake embedding AI in many of their business processes, systems, and applications.”
But the pandemic kicked that into high gear.
“It’s gone from the realm of experimentation to, frankly, the mainstream,” Mittal says. “A lot of our organizations are now actually focused around the use cases.”
Which is one of the reasons why Deloitte put together the AI Dossier that spells out some of these use cases where AI can immediately add value and also looks at the opportunities for particular use cases in specific industries.
The AI Dossier identifies the following six major ways that AI can create value for business:
- Cost reduction – By applying AI and intelligent automation solutions to automate tasks that are low value and repetitive, organizations can reduce costs through improved efficiency and quality. For instance, automating data entry.
- Speed to execution – By minimizing latency, organizations reduce the time required to achieve specific operational and business results. For instance, using predictive insights in the drug approval process to create a synthetic trial.
- Reduced complexity – By using proactive, predictive analytics, organizations can see patterns in increasingly complex sources that then improves understanding and decision making. For instance, predicting machinery maintenance needs in a factory setting.
- Transformed engagement – Changing how people interact with technology by improving technology responses. For instance, using conversational bots that understand and respond to customer sentiment.
- Fueled innovation – Using AI to enable new products, markets, and business models that redefine where to play and how to win. For instance, new product recommendations based on customer needs and preferences as mined from social media.
- Fortified trust – Securing a business from risks such as fraud and cyber-attacks. For instance, identifying and anticipating attacks before they occur.
“Organizations are adapting to the new normal, and AI is a part of it,” says Mittan. “It’s not necessarily the Holy Grail, but it’s a huge part of how organizations are adapting. It helps them predict, reduce costs, and optimize. It’s a catalyst.”
What to Read Next:
- Data Fabrics: What CIOs Need to Know
- How CIO Roles Will Change: The Future of Work
- Gaining Control Over Data Decay
- What CIOs Need to Know About Graph Database Technology
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