April 4, 2024

Unlocking Potential: AI Workflows at FedEx & Cisco

What if your enterprise workflows could be 25% more productive? Integrating AI into business workflows, from logistics to software development, can significantly improve processes.

3 min read

Meet our Editor-in-chief

Paul Estes

For 20 years, Paul struggled to balance his home life with fast-moving leadership roles at Dell, Amazon, and Microsoft, where he led a team of progressive HR, procurement, and legal trailblazers to launch Microsoft’s Gig Economy freelance program

Gig Economy
  • FedEx's AI initiatives, like Package Fingerprint and SenseAware, boost tracking accuracy and supply chain resilience, enhancing service and expanding clientele.

  • Cisco uses AI for faster software development, utilizing tools like Co-Pilot Chat to speed up onboarding and boost productivity.

  • FedEx and Cisco have demonstrated that AI integration is crucial for competitive advantage, improving efficiency, user satisfaction, and fostering innovation.

Staff writer

From AI to FinOps, our team's collective brainpower fuels this blog.

Artificial intelligence is more than a passing trend, with about 53% of businesses applying AI solutions to improve their production processes. We are starting to see more and more enterprise business leaders adopt AI into their workflows. There’s a push toward building efficient systems that balance humans and intelligent machines to create an enhanced productivity and innovation ecosystem. This integration is crucial in today’s fast-paced business world, where staying competitive often hinges on effectively leveraging advanced technologies.

To effectively adopt AI, adopting a holistic view of business operations is essential to identify where these advanced technologies can seamlessly integrate and address specific needs. Rather than simply applying AI for novelty, its strategic application can yield substantial benefits. Let's see some examples of how AI can be thoughtfully integrated into enterprise workflows!

Transforming Supply Chain Dynamics: FedEx's AI-Driven Approach

Thanks to digitalization, the logistics and supply chain management sector produces vast amounts of data annually, making AI necessary to convert this data into business insights.

Tomas Ohlson, Founding Engineer at Einride, a Swedish-based logistics and technology company, said, “Anywhere where we have mass amounts of complexity and data, it’s a natural place for machine learning solutions to be the next step you take.”

Artificial intelligence can offer practical solutions to streamlining logistics and supply chain workflows. AI applications like predictive analytics can seamlessly integrate into larger workflows. Consider a retail store that aims to forecast high-demand clothing items for the upcoming season to optimize stock orders. An AI system can analyze past sales, online browsing habits, social media trends, weather forecasts, and fashion trends to predict consumer needs accurately.

Machine learning algorithms identify patterns in data related to clothing demands. These algorithms offer insights such as increased demand for lightweight jackets as spring nears or the popularity of specific colors following social media trends. This foresight aids in efficient inventory management, allowing stores to order stock proactively and avoid rushes and potential stockouts. Moreover, this targeted approach reduces the risk of overstocking unsellable items.

Similarly, AI-powered route optimization algorithms enhance existing route planning systems, boosting delivery efficiency, cutting transportation costs, and lessening environmental impact. Comparable to using Google Maps for daily navigation, AI algorithms in this context calculate the most effective routes for each delivery vehicle. These algorithms account for the shortest paths and potential delays, like traffic or construction. More efficient routes equate to reduced time on the road and lower fuel consumption, resulting in decreased operational costs for companies and supporting broader environmental sustainability initiatives.

These practical AI implementations harmonize with the operational framework and enrich customer experiences in the logistics and supply chain.

A great example of a success story is FedEx's implementation of AI through its FedEx Dataworks initiative. They created a centralized hub to collect data from their global network. Then, they introduced initiatives like Package Fingerprint and SenseAware, which used AI to provide accurate and precise data about package transits. 

Package Fingerprint, aptly named, leverages AI to collect comprehensive data on a package’s size, shape, weight, and route through the FedEx network, effectively creating a unique 'fingerprint' for each parcel. This advanced system allows FedEx to meticulously track each package, ensuring its location is always known and facilitating smooth and problem-free delivery.

Building on this technology, FedEx’s SenseAware represents a further advancement in AI-driven package tracking by monitoring environmental conditions. This feature is particularly vital for sensitive shipments like pharmaceuticals or perishable items. The SenseAware IoT device, affixed to the package, transmits real-time temperature, humidity, and light exposure data. FedEx and its customers benefit from this capability, allowing them to track the shipment's condition and location throughout its transit. This continuous monitoring ensures the integrity of the products during their journey, and should environmental conditions shift unexpectedly, proactive measures, such as rerouting the shipment, can be promptly implemented.

This strategic integration of AI and machine learning has made their supply chain more resilient and responsive, with visible ROIs regarding improved service levels for critical deliveries like vaccines and an expanded customer base.

The AI Edge in Coding: From Bug Detection to Co-Pilot Chat

From logistics and supply chain management to software development, AI’s footprint in workflows is just as visible. The influence of AI on software development processes can be seen. With automated testing, code generation, and bug detection, software development has become very rapid, and the entire industry is experiencing a fast-paced culture. Tsavo Knott, Co-founder and CEO of Pieces, highlighted the dynamic nature of today's software development landscape and commented, "Developers are jumping around a lot, especially with the advent of open source.”

As a result, when new developers are onboarded to a project, there’s a huge amount of information to understand and pick up quickly. AI can come in handy here to combat the speedy environment with tools like Co-Pilot Chat. Now, developers can ask questions about projects they are being introduced to and learn the nuances quickly. In the past, they’d have to sit and take long knowledge sessions from previous people working on the project to comprehend all the different tools they used. 

AI tools like these seamlessly add to the software development workflow to help coordinate and make completing tasks easier; having a better understanding with the help of AI results in increased productivity. This can be measured easily. Software firms use tools like Jira to track their work, and looking back at these logs after adapting AI tools will show a clear boost in performance.

AI in Business: Making Every User Interaction Count

Around the globe, it's agreed that measuring the effectiveness of AI in improving operational efficiency is crucial for businesses. But how that’s done is a topic of conversation. To create compelling business cases for AI implementation, we must change how we look at ROIs and understand the real, practical value these solutions bring.

Emmanuel Walckenaer, CEO of YSEOP, recently discussed how enterprise leaders gauge the success of AI programs to boost productivity. While quantifiable outcomes such as Return on Investment (ROI) are fundamental, qualitative aspects like user satisfaction hold equal weight. Walckenaer noted that when end-users find AI solutions enjoyable and user-friendly, the benefits often surpass expectations, potentially yielding three to four times higher returns. This user-centric perspective emphasizes the necessity of not just implementing AI, but ensuring it resonates with and meets the needs and preferences of those engaging with it.

When assessing AI's impact on operational efficiency, businesses should look beyond surface-level metrics to how AI integrates with existing workflows. The ability to create tangible value and contribute to strategic business goals is a crucial indicator of AI's effectiveness in driving operational excellence.

Integrating AI into enterprise workflows—such as predictive analytics in supply chain management and natural language processing in software development—promises to enhance business processes, increase productivity, and drive innovation. However, to maximize the benefits of these AI integrations, it is critical to assess their effectiveness, focusing on factors like Return on Investment (ROI) and real-world applicability.

It is crucial to keep abreast of emerging AI technologies, such as route optimization and Internet of Things (IoT) applications in package tracking. Businesses must follow these developments and ensure they align with their unique business needs and foster innovation. With the right strategy and a vigilant approach to AI advancements, businesses can lay the groundwork for a more efficient and forward-looking future.

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