Feature
Artificial intelligence (AI) is becoming a central part of departmental and team-level operations in various sectors.
The internal uses of AI for operations are expanding as companies demonstrate success in focused AI deployments and experience the benefits of AI.
Here, we look at several examples of why companies are using AI for a range of their key business operations.
Streamlining Processes
Edstellar, an employee development company, is using AI to streamline its processes, according to CEO Arvind Rongala.
Predictive analytics driven by AI was included in the company’s project management system. The AI tool predicts project delays based on past data, team performance and resource availability.
“We could reallocate resources and achieve our target when a recent software deployment project identified a possible delay two weeks in advance,” Rongala said. “For projects, it functions similarly to a GPS, recalculating routes when obstacles appear.”
AI also makes reporting procedures more automated for the company. Monthly performance reports take 80% less with natural language-generating techniques. Previously, reporting procedures took five hours. The work now takes 30 minutes, allowing managers to concentrate on strategy. For example, instead of spending hours creating spreadsheets, sales can now examine market trends.
With the benefits, there are also some issues with AI, Rongola said.
“Team morale is one example of a qualitative component that AI algorithms occasionally ignore,” Rongola said. “The AI overestimated the ideal staffing during a hiring surge, since it didn't fully understand employee weariness.”
For instance, the AI advocated for increased workloads, which backfired when output declined.
“Since then, we've put measures to blend human judgment with AI insights,” Rongola said.
“Overall, AI enhances efficiency but requires ongoing monitoring to mitigate errors and ensure ethical use.”
Enhancing Predictive Analytics
The COO at Cisco integrated AI-driven predictive analytics into its supply chain operations to help with forecasting, according to Robin Patra, head of data, platform, product and engineering at ARCO Construction and previously head of supply chain digitization at Cisco.
“Previously, forecasting was labor-intensive and prone to error, leading to both overstock and shortages,” Patra said. “Now, AI tools analyze real-time demand patterns, supplier reliability and global disruptions, like weather or geopolitical events, to create hyper-accurate inventory plans.”
The results included a 15% reduction in inventory costs and a 20% improvement in on-time deliveries within the first quarter of implementation.
However, Patra said operations leaders are not ignoring potential challenges and balancing unprecedented innovation with ethical considerations.
“The demand for AI isn’t just about technology. It’s about how executives reshape operations to maximize value while mitigating risks,” Patra said.
Related Article: 5 AI Case Studies in Logistics
Managing User Growth
Google's customer engagement team used AI to help manage the company’s growth, according to a report the team published in Management and Business Review.
The team managed the growth of marketing, sales and support teams with AI, according to Tim Frank, a senior project management director at Google and lead author of the report.
The team’s interactions start with customer support from tens of thousands of Google representatives and expand to include custom tools and processes that “bring significant improvement to human interaction at scale,” Frank says.
Frank says the team took their interactions “one step further by delivering AI-enhanced experiences that bring even more value to the customer.”
Being able to deliver personalized, proactive and contextual experiences across surfaces or touch points is “critical in the company’s growth,” Frank says.
AI initially helped with customer understanding. As the company learned more about a customer through product, marketing, sales and support, the company could deliver more value, Frank says.
This creates a positive feedback loop and enables every surface to be customer aware and deliver solutions at a global scale, Frank says.
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AI helped improve several of the team’s key metrics: productivity improved by more than 150 full-time employees; a 44% reduction in manual representative messages in chat; and customers found 23.5% more value with the company.
Improving Customer Support
Customer support was becoming “our biggest scaling bottleneck,” said Dev Nag, CEO of QueryPal, a customer support provider.
“We couldn't hire and train agents fast enough to keep up with the flood of support emails,” Nag said. “Our most experienced agents were getting burnt out handling the same questions over and over instead of focusing on complex customer issues. The situation was unsustainable and threatened to cap our growth.”
Nag said his company implemented an AI system that automatically drafts responses to incoming support tickets, using machine learning (ML) to learn from past tickets.
Response times dropped by half, and agent satisfaction increased, as they were freed to focus on customer conversations rather than repetitive queries, according to Nag.
Agents approved over 85% of drafted responses without edits.
“We've been able to grow support volume by a factor of three without adding headcount, while maintaining higher customer satisfaction scores,” Nag said, seeing ROI over a traditional approach of “just throwing more bodies at the problem.”
Nag recommends that others looking to add AI to their operations start with a focused scope or task where success is measurable.
“Get ahead of data privacy concerns by implementing PII masking from day one,” Nag said.
“Make sure to clarify it internally as augmenting rather than replacing agents. We've found they become the biggest champions once they experience how it eliminates the soul-crushing parts of their job and lets them focus on the human touch. The technology is ready for prime time — the main challenge now is cultural acceptance and smart implementation.”
Related Article: Where’s the Generative AI ROI? Start With the Supply Chain
