The Supply Chain Evolution: How To Deploy AI Successfully

Forbes

3 days ago

Anise Madh is GM of Global Infor Practice at Wipro, a global leader in e-commerce and mobile solutions for Infor CloudSuite ERP M3 and LN.
AI is revolutionizing employee resource planning (ERP) systems and supply chain operations by making them more intelligent, predictive and autonomous. Because of its growing role in day-to-day business, AI is much more than an add-on; for many companies, it's becoming the backbone of supply chain management.
While challenges and roadblocks still exist, the rollout and nuances of AI applications are prompting a shift in how companies think and operate. To stay ahead, companies must adopt innovative approaches that streamline processes and improve operations throughout the supply chain.
Knowing this, the question isn't if you should adopt AI but how you can lead the charge. Being a trailblazer means being willing to explore this tool's full potential, experiment boldly and rethink what's possible.
Addressing AI-Related Deployment Challenges
When rolling out AI, it's important for companies to consider the hurdles they might face, including these three potential challenges.
For some AI and generative AI tools, a lack of clarity exists around the inputs the software requires to perform its capabilities. AI's opacity, often referred to as the "black box" problem, stems from the complexity of its neural networks. Data fed into these systems passes through layers of hidden nodes, performing thousands of computations that are largely inscrutable.
This uncertainty is precisely why AI must operate under human oversight, with clear guardrails to define its appropriate use. As Boston Consulting Group noted, human supervision is essential to managing the risks associated with generative AI, but it isn't enough on its own. It must be paired with thoughtful design and well-defined processes that help people detect and de-escalate issues when they arise.
While continued economic uncertainty, policy changes and market fluctuations impact far more than just AI implementation, they do play a large part in how successful this tech rollout may be. Take a look at the ups and downs of the market from recent trade wars.
Plus, other potential challenges like natural disasters and supplier issues remain. AI's predictive models can struggle to account for uncommon or rapidly evolving events, which could lead to gaps in forecasting accuracy and delayed response strategies.
Organizations can incorporate flexible contingency strategies to help with this. For example, a flexible contingency strategy might include a diversified supply chain with backup vendors in various regions. Coupled with AI-based scenario planning, this tactic can help companies respond to natural disasters, policy changes or supplier challenges. Pre-vetted alternatives and adaptive workflows help businesses pivot quickly during unforeseen circumstances.
By combining AI capabilities with proactive risk management, organizations can better navigate market fluctuations and maintain operational reliability in an increasingly complex global landscape.
Another potential roadblock is having the right team in place for implementation, both internally and externally, and identifying who will run point on ongoing fine-tuning and maintenance of the AI models after deployment. Bain & Company found 44% of executives surveyed lack in-house AI expertise, which is slowing its adoption.
Overlooking who runs AI leads to challenges with ownership and managing time-sensitive updates or fixes. Addressing this means bringing in or developing people who truly understand AI, can question its outputs and spot issues like hallucinations or bias before they become problems. Without this level of talent, businesses risk losing control over their AI systems, which can compromise reliability and performance as well as trust from stakeholders.
Best practices and guardrails are also crucial for maintaining strong AI governance. Strategies like requiring chain-of-thought explanations from AI tools help enhance transparency and accountability. What's more, establishing well-thought-out verification processes ensures AI insights effectively support decision making.
Ultimately, overcoming challenges and ensuring sustained success with AI requires skilled human oversight and strong governance measures that extend well beyond an organization's initial implementation.
Bridging Today's Capabilities With Tomorrow's Innovations
I believe AI's role in the supply chain is poised for a significant transformation, evolving from a decision-support tool to a more autonomous platform capable of proactively recommending actions across areas such as forecasting, planning and delivery. As AI continues to evolve, I expect organizations to lean on it more to simplify day-to-day operations, cut down on repetitive manual tasks and boost accuracy in forecasting and planning. This shift toward AI-driven decision making will reshape conventional supply chain management approaches. It could also pave the way for smarter and more adaptable logistics strategies.
Businesses can start by trialing one or two high-impact AI integrations, like demand forecasting or logistics optimization, and gradually expand their scope. Doing so can allow them to modify their AI implementations while addressing the challenge of building a talent pipeline. The key is to prioritize strategic AI adoption. Companies may also consider using cloud-based advancements to integrate current capabilities with future innovations.
Final Thoughts
There's no doubt that AI's integration into supply chain management and logistics operations is reshaping business. Whether it's helping to redesign processes or improve how industry leaders forecast and make decisions, AI is helping boost efficiency, accuracy and agility.
These tools can help companies anticipate disruptions by streamlining operations and providing elevated insights to inform decision making in an increasingly complex world. The journey isn't without its hurdles, but they are also signs that it's time to evolve. Roadblocks are opportunities for improvement, and companies integrating AI into their ERP and supply chain operations wisely can position themselves for lasting competitive success.
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