CITY, 2025-06-18 09:30:00 – Imagine this: You’re a business leader, and AI is suppose to be your magic bullet. You’re promised cost savings, increased efficiency, and a competitive edge. But what if the reality is a bit more… nuanced? A recent poll of major businesses reveals some surprising truths about how AI is being used and what’s really delivering the biggest impact. Let’s dive in!
The AI Paradox: Simple Tasks, Big Savings
Businesses are seeing meaningful cost reductions with AI, but the improvements are mostly in straightforward areas.
- AI is cutting costs in customer operations,people operations,finance,and supply chains.
- Businesses favor proprietary AI over open-source options.
- The focus is primarily on automating routine tasks, not transformative changes.
The survey, conducted by the Capgemini Research Institute, found that while AI is helping to automate tasks and cut costs, the improvements are largely concentrated on simpler, more repetitive business functions. The study of 1,607 executives from organizations with at least $1 billion in global revenue highlights a 40% reduction in customer operations costs due to AI and generative AI (GenAI). People operations saw a 26% reduction, while finance and accounting costs decreased by 24%. Moreover, supply chain and procurement costs dropped by 21%.
Consider Yum Brands, the parent company of Taco Bell, which operates 60,000 restaurants worldwide. They implemented an AI-powered restaurant manager to track crew attendance and plan shift patterns, even suggesting adjusted opening hours based on market conditions. This showcases AI’s potential to improve efficiency.
Though, the report suggests that thes savings are frequently enough tied to automating basic tasks. According to Capgemini, this indicates that AI and GenAI are currently delivering early-stage efficiencies rather than long-term, transformative impacts.
Food for Thought: Yum Brands’ AI implementation isn’t just about cost savings.It’s also about improving employee satisfaction by optimizing schedules and reducing workload imbalances. Happy employees, happy customers!
The Cost of Innovation: Balancing Savings and Expenses
It’s not all sunshine and rainbows. The cost of running these AI systems needs to be considered.
The Capgemini Research Institute pointed out that the cost of querying a trained model is falling dramatically. OpenAI’s GPT 3.5 saw a decrease from $20 per million tokens to $0.07 per million tokens, while GPT-4 dropped from $15 to $0.12 within a year.
model pruning,quantization,and distillation can reduce the size and complexity of AI models.Optimized models require fewer computational resources, which lowers inference costs. The report also highlights that efficient hardware utilization, batch processing of inference requests, dynamic scaling, and energy-efficient algorithms can significantly reduce power consumption.
While open-source models like DeepSeek have demonstrated an 11x reduction in compute costs, business executives are less enthusiastic about these alternatives compared to proprietary AI models.
Cost Optimization Tip: Don’t just focus on the initial cost of AI implementation. Consider the long-term operational costs and explore strategies like model optimization and efficient hardware utilization to maximize your ROI.
Proprietary vs.Open Source: Trust and Control
The study reveals a clear preference for proprietary AI implementations.
Despite the performance and cost benefits of open-source AI, a significant majority of executives favor proprietary AI. The report indicates that three-quarters of the surveyed executives prefer proprietary models, with 43% opting for those developed by hyperscalers and another third choosing models from niche providers.
This preference is especially strong among organizations that have increased their AI and GenAI investments. The authors suggest this shows a trend toward trusted, enterprise-grade AI products that offer robust support, security, and integration.
The AI in action report also identifies trade-offs that limit the adoption of open-source models. these include the need for more technical expertise, potential security vulnerabilities, and dependence on community-driven support.
Oliver Pfeil, CEO of business services at Capgemini, said, “GenAI and agentic AI can truly transform business services – enabling the shift from customary cost-focused models towards an AI-enabled, value- and insight-driven business. Those that adopt an integrated approach with data and AI at its core will be set to achieve a truly connected, frictionless enterprise.”
He also noted that organizations face barriers scaling up AI deployments. “Adopting a pragmatic approach, fostering trust in AI and creating a strong data foundation will go a long way in transforming business services into a strategic powerhouse to fuel any enterprise,” he added.
The Future of AI: Beyond Automation
While the current focus remains on automating routine tasks, the potential of Artificial Intelligence extends far beyond simple cost-cutting measures. The real value lies in its ability to revolutionize how businesses operate, innovate, and engage with customers [[1]]. As the technology matures, we can expect to see AI taking on more complex challenges and driving truly transformative changes across industries.
The next frontier for AI involves leveraging its capabilities for creative problem-solving and strategic decision-making. instead of just automating existing processes, businesses will use AI to develop new products, services, and business models. Imagine AI-powered tools that not only optimize supply chains but also predict future market trends,enabling proactive responses to changing consumer demands.
One exciting area of development is the use of AI in research and development. At MIT, for example, researchers are using machine learning to accelerate the finding of new materials [[2]]. This shows the potential for AI to speed up innovation cycles and bring new breakthroughs to market faster.
AI in Action: Driving Transformational Change
The shift from automating simple tasks to driving real transformation is already underway, and it’s being fueled by several key factors:
- Advanced algorithms: Refined machine learning models are becoming capable of handling more complex tasks, enabling AI to tackle challenges previously beyond its reach.
- Data availability: Businesses now have access to vast amounts of data, which can be used to train and refine AI models, leading to more accurate predictions and insights.
- Increased computing power: Advancements in hardware, including specialized processors, make it possible to process large datasets and run complex AI models more efficiently.
Table of Contents
