10 AI Tools For Cost Savings

AI in Cost Savings

The potential of AI in cost savings shows in existing models like OpenAI’s ChatGPT and Microsoft Copilot, with features that allow for faster document analysis and market research among others.

According to a report by KPMG, AI boosts efficiency in supplier negotiations, demand planning, contract reviews, and invoice processing, leading to a 5-8% increase in spend under management.

In cost savings, AI presents an opportunity for procurement leaders to leverage smarter methods of approaching negotiations, supplier evaluations, inventory management, and more functions.

10 AI Tools For Cost Savings

AI-powered tools help organizations cut costs and boost efficiency by automating tasks, optimizing resources, and streamlining operations across multiple business areas.

1. UiPath 

UiPath is an RPA platform that uses AI for document recognition, data extraction, and automating repetitive tasks. Automating invoice processing, data entry, and back-office workflows reduces employee hours spent on routine work. Integration with ML models enables intelligent exception handling and routing, lowering manual review costs. The result is faster processing times and measurable reductions in operational expenses.  

2. Automation Anywhere 

Automation Anywhere combines RPA with cognitive capabilities (IQ Bot) to automate structured and semi-structured processes like document understanding. It extracts key data from invoices, forms, and emails and pushes that data into ERPs and CRMs, cutting manual entry errors and labor costs. Companies deploy it for finance, HR onboarding, and customer service automation to free staff for higher-value work. This leads to faster ROI through reduced FTE dependence and improved efficiency.  

3. OpenAI (ChatGPT and API) 

OpenAI’s models automate text generation tasks across customer support, marketing, documentation, and code assistance. Chatbots built on these models can handle routine inquiries at scale, reducing the need for large frontline support teams. They also speed up internal tasks like summarizing reports or drafting templates, saving staff time. Combined, these efficiencies translate into lower labor costs and faster task completion.  

4. Intercom (Resolution Bot and AI Inbox) 

Intercom leverages AI to automatically answer frequently asked questions and route complex issues to the right agents. This shortens average resolution times and lowers the headcount required for first-line support. Intercom’s AI also enables personalized messaging and automated workflows that improve retention and reduce churn-related costs. The net effect is a lower cost-per-contact and higher support efficiency.  

5. SparkCognition (predictive maintenance) 

SparkCognition applies AI to IoT and sensor data to detect anomalies and predict equipment failures before they occur. This enables scheduled maintenance instead of costly emergency repairs, reducing downtime and repair expenses. Proactive interventions extend equipment life and improve overall uptime. For industries with heavy capital equipment, this delivers substantial operational savings.  

6. Blue Yonder (supply chain & demand forecasting) 

Blue Yonder uses advanced ML to improve demand forecasting, inventory optimization, and supply chain planning. More accurate forecasts reduce excess inventory, storage costs, and losses from obsolescence. The system can also recommend order and replenishment adjustments to minimize stockouts while lowering carrying costs. Improved inventory efficiency increases cash flow and reduces tied-up capital. 

7. CloudHealth

CloudHealth analyzes cloud usage to identify underutilized instances, recommend rightsizing, and suggest reservation purchases. Automated detection of idle resources and policies to shut down nonessential workloads reduces monthly cloud bills. It provides team- and project-level visibility so organizations can enforce cost accountability. These optimizations typically yield significant savings on public cloud spend.  

8. Spot by NetApp (cloud workload optimization)

Spot uses AI to manage cloud infrastructure automatically, leveraging spot instances, autoscaling, and workload placement to balance cost and availability. It reduces overpayment for on-demand instances and dynamically migrates workloads to cheaper options without manual intervention. Spot’s automation of instance lifecycle and scaling decisions minimizes waste and admin overhead. This leads to lower cloud costs for containerized and cloud-native applications.  

9. BrainBox AI (building energy optimization)

BrainBox AI applies machine learning to HVAC and building sensor data to optimize climate control and energy consumption in real time. By adjusting systems proactively based on occupancy and external conditions, it lowers energy usage while maintaining occupant comfort. The system learns building behavior over time and continuously improves efficiency. Owners and operators typically see reduced utility bills and faster payback on the system.  

10. GitHub Copilot (developer productivity)

GitHub Copilot assists developers by suggesting code, filling boilerplate, and accelerating routine programming tasks through AI-driven completions. It reduces time spent searching documentation and writing repetitive code, speeding feature delivery. Faster development cycles and higher productivity mean fewer developer hours per feature and lower development costs. Copilot can also help generate tests and documentation, further improving team throughput.  

5 Practical Examples of How AI Tools Are Utilized for Cost Saving

1. Lithuanian Police — UiPath

The Lithuanian Police used UiPath RPA to automate the processing of traffic violations and related administration. Before automation, officers manually processed millions of violations annually (collecting data, printing notices, and mailing them), which consumed significant work hours. After implementing UiPath bots, the system automatically gathers violation data, generates documents, and sends them, with human intervention required only for exceptions. Processing time per violation dropped significantly (reporting multiple-fold acceleration), freeing officer capacity for other tasks. The result is reduced administrative costs and increased operational efficiency.

2. Bancolombia — Automation Anywhere

Bancolombia implemented Automation Anywhere to automate hundreds of back-office processes, including loan processing, collections tracking, and capital markets operations. The platform handled structured and semi-structured data and automated routine tasks that previously required substantial human labor. According to reports, the bank freed hundreds of thousands of hours annually, reduced booking costs, and quickly achieved a high ROI. Automation improved process accuracy, reduced manual entry errors, and sped up client processing times. Bancolombia subsequently expanded RPA to additional departments due to visible savings and the scalability of the solution.

3. Amazon (Grocery Fulfillment) — BrainBox AI (in partnership with Trane and AWS)

Amazon piloted autonomous HVAC optimization using BrainBox AI in several of its logistics/grocery fulfillment centers, in collaboration with Trane Technologies and AWS. The pilot demonstrated significant energy savings (around ~15%) in these facilities, leading to lower operating costs and reduced CO2 emissions. The solution operates in real time by analyzing sensor data and automatically adjusting HVAC parameters without major hardware changes. Following successful pilots, expansion to more centers is planned, with expected cumulative savings and faster ROI. This project illustrates how AI-driven building management can reduce overhead costs across large facility networks.

4. SanDisk — Blue Yonder (Demand Forecasting and Planning)

SanDisk used Blue Yonder (Luminate Planning) to improve demand forecasting and integrated inventory planning across OEM and retail channels. Implementation enabled daily, and even intraday, planning with better visibility of demand and inventory, reducing overstock and improving order fulfillment. As a result, SanDisk reported multiple-fold improvements in on-time delivery and enhanced safety stock accuracy. The optimization also supported dynamic inventory segmentation and postponement strategies, reducing tied-up capital and storage costs. The solution enabled more agile responses to demand changes and improved overall supply chain efficiency.

5. Accenture — GitHub Copilot (Enterprise Study)

Accenture expanded the use of GitHub Copilot among thousands of its developers and documented productivity impact measurements in collaboration with GitHub. The study reported increases in pull requests and faster feature delivery, with improvements in merged PR metrics and developer satisfaction. Accenture noted that teams were more efficient in writing and reviewing code, and many developers reported a better work experience using Copilot. The results demonstrate that AI assistance in development can reduce repetitive tasks and accelerate development cycles in large organizations. This example serves as a demonstration of how Copilot can deliver measurable enterprise-level productivity benefits.

5 Ways AI Drives Cost Savings

We’ve covered AI’s potential in cost savings, but how does it directly influence it? Below are the five key ways that AI drives cost savings.

1. Automate routine tasks

AI models can take over routine tasks and speed up processes. But how? Generative AI models, like ChatGPT, can automate purchase order creation and generate documents and summaries for reports in procurement.

How does this equal savings? AI reduces manual work, meaning that it frees up human resources, thus enabling more productive teams that focus on core activities.

2. Analyze spend and control

With capabilities in spend analysis, AI finds opportunities for cost savings by studying spend patterns, supplier performance, and improvement points in the overall process.

Because AI can extract insights from reports, it helps organizations leverage opportunities to manage spend more efficiently, leading to better spend management and control.

3. Support negotiations

AI can assist supplier negotiations in many ways. It can simulate negotiation scenarios, which can help negotiators prepare for the negotiation and adjust their approach based on the simulation outcome.

It can predict the likely outcome of a business deal based on variables like performance history, reports, and other data.

Lastly, AI can assess and counter negotiation tactics. By offering data-backed tips and proposed strategies, AI helps procurement professionals prepare and close better and more cost-effective deals in negotiations.

4. Invoice processing

AI boosts the speed and accuracy of most routine processes in procurement, but let’s zoom in on invoice processing. AI can automate invoice processing, leading to fewer inaccuracies and better cash flow management.

This directly contributes to cost savings, with increased accuracy and compliance with invoice management.

5. Gather and analyze market data

Another way AI impacts cost savings is through market intelligence. AI models can gather and analyze market data about supplier pricing, market trends, and geopolitical developments.

This informs procurement leaders on market dynamics, keeping them informed and equipped to make data-supported decisions. 

Market intelligence with AI leads to improvements in negotiated prices and risk management while capitalizing on cost savings

My Advice on AI in Cost Savings

In this article, I will give some advice regarding AI in cost savings:

Conclusion

AI-driven tools have proven to be powerful enablers of cost savings across various industries, from public administration to finance and logistics. By automating routine tasks, improving accuracy, and optimizing resource use, organizations can significantly reduce labor costs and operational inefficiencies. The practical examples of UiPath, Automation Anywhere, BrainBox AI, Blue Yonder, and GitHub Copilot demonstrate measurable improvements in productivity and faster ROI.

Moreover, AI supports smarter decision-making through spend analysis, market intelligence, and negotiation assistance. These capabilities allow companies to manage resources more effectively, respond quickly to changing demands, and mitigate risks. Overall, leveraging AI for cost savings not only reduces expenses but also enhances strategic agility and long-term operational efficiency.

I have created a free-to-download Productive Procurement with ChatGPT Toolkit template. It includes a PDF file that contains prompts that can help you save costs. I even created a video explaining how to use the templates.