How Can AI Help Your Business Reduce Costs?

As businesses struggle with increasing expenses, the pressure to find effective cost-cutting solutions has never been greater. Artificial intelligence (AI) stands out as a key resource for tackling these financial challenges. It helps manage repetitive tasks, minimize errors, and enhance the decision-making process, leading to improved efficiency and reduced operational costs.

World Economic Forum estimated in its 2023 Future of Jobs Report that nearly three-quarters of companies that they surveyed plan to adopt AI. To stay competitive in 2024, organizations must embrace this technology to elevate their operational efficiency.

If you’re curious about how implementing AI systems can help reduce business expenses, you’re landing at the right place. Now, just sit back, relax, and enjoy 5 ways that AI drives cost reduction and useful tips to adopt artificial intelligence effectively.

AI Cost Efficiency: The Numbers Speak

AI brings enormous benefits to businesses. According to PwC, its economic impact is expected to reach $15 trillion in the next decade, including $4.4 trillion attributed to Generative AI alone.

The potential for cost savings through AI systems varies greatly across different sectors.

• Thanks to broader AI adoption, the healthcare industry is expected to see annual savings of 5 to 10 percent, amounting to between $200 billion and $360 billion each year.
• Businesses in financial services that effectively integrate machine learning report cost reductions of over 10%.
• In supply chain management, 41% of respondents indicated a 10% to 19% reduction in costs after harnessing AI. Other fields have reported similar results: 20% of marketing and sales teams, 32% of manufacturing departments, and 25% of HR leaders experienced comparable savings.

So, how exactly does artificial intelligence lead to cost reductions? Read on to find out!

How Does AI Drive Cost Reduction In Action?

1. Automating Repetitive Tasks in Businesses

Having tools and machines automate routine tasks is a powerful way to save both time and money.

Essential yet monotonous activities, such as data entry and document processing, can be performed with remarkable precision through natural language processing (NLP), allowing your employees to concentrate on more strategic tasks that drive growth.

What’s more, AI-driven automation not only speeds up these processes but also reduces the likelihood of human error that can result in costly corrections.

This is especially evident in hiring since AI-driven recruitment systems can lower the cost-per-hire by 71%, as Ideal showcased for one of its large retail clients. These tools can support screening candidates’ information from résumes, scheduling interviews, or writing follow-up emails, greatly freeing recruiters from manual tasks.

Also read: The Role of AI in Talent Acquisition to Revolutionize Recruitment

2. Enhancing Predictive Maintenance

Equipment maintenance and repair consume a significant portion of manufacturing companies’ budgets. When a machine breaks down, the financial impact is twofold: the cost of repairs and the lost productivity while the machine is out of service.

Today, many manufacturing businesses are employing AI tools to predict when machinery requires maintenance, facilitating proactive repairs to prevent unexpected breakdowns.

By harnessing predictive capabilities, AI algorithms can identify potential mechanical issues before they occur, effectively saving money and reducing downtime.

According to Deloitte, predictive maintenance can enhance productivity by 25%, decrease breakdowns by 70%, and lower maintenance costs by 25%. This AI technology analyzes extensive data to foresee malfunctions, allowing for wise decision-making to schedule repairs during less expensive off-peak hours. To take a closer look, the advantages of implementing AI technologies for predictive maintenance include:

• 5% to 10% reducing operational costs related to maintenance, repairs, and operations (MRO).
• 10% to 20% growth in equipment uptime.
• 20% to 50% lowering in maintenance planning time.

Learn more: Everything You Need to Know about Predictive Maintenance AI

3. Automated AI-Powered Customer Service Solutions

Automation is a straightforward way to cut costs in customer service. By integrating AI-powered chatbots and virtual assistants, businesses can manage routine inquiries in seconds, giving human agents more time to focus on more complex tasks. Basically, these systems handle frequently asked questions and provide instant, 24/7 support, significantly reducing the need for human representatives and minimizing operational costs, resulting in higher customer satisfaction.

McKinsey’s research found that a company with 5,000 customer service agents that implemented Generative AI improved issue resolution by 14% per hour and reduced handling time by 9%. Additionally, it decreased agent turnover and requests to speak to managers by 25%.

When we mention AI-based customer service tools, chatbots, virtual assistants, and automated call centers will first come into mind. For instance, a company may customize a large language model (LLM) using its historical data related to customer service, such as FAQs, order details, delivery information, and complaints. They can then leverage this model to create a Generative AI chatbot that quickly and efficiently manages a large number of customer inquiries.

Learn more: 7 Mind-Blowing Applications of Generative AI in Customer Service

4. Optimizing Supply Chain Management Processes

With AI in the supply chain sector projected to reach $20.3 billion by 2024, businesses are keen to take advantage of this technology. Indeed, AI-driven solutions can greatly optimize supply chain operations, reducing transportation costs by up to 30% and improving forecast accuracy by 75%.

Effective inventory management helps minimize costs and enhance efficiency. AI streamlines this process by providing accurate demand forecasts based on historical data and market trends, allowing businesses to maintain optimal stock levels and avoid excess inventory.

AI also automates the reordering process, placing orders automatically when stock levels fall below a certain point. Hence, it reduces manual tasks and human error, resulting in a more agile and resilient supply chain.

Walmart proves to be a typical case of using AI for inventory management in its Sam’s Club warehouses, ensuring the availability of roughly 6,000 items. They employ floor scrubbers with inventory intelligence towers that capture real-time images of shelves, amounting to over 20 million photos daily.

Learn more: A Comprehensive Guide to Applying Machine Learning in Supply Chain

5. Lowering Healthcare Costs with AI

Artificial intelligence is becoming increasingly essential for reducing costs in the healthcare sector thanks to its capability to analyze vast datasets and identify patterns.

Misdiagnoses can result in expensive interventions. However, AI-powered diagnostic tools can help by analyzing medical images like X-rays and CT scans with remarkable accuracy, reducing costly medical errors and enabling early diagnosis. AI diagnostic systems have also been shown to outperform human experts in identifying conditions like skin cancer, cutting down the need for extensive tests.

Preventing diseases is often more cost-effective than treating them. AI’s predictive analytics can analyze patient data and identify high-risk patients, allowing for proactive interventions. This can lead to substantial savings – a reduction of hospital admissions by up to 30%.

Furthermore, given that administrative overhead is a major cost driver for each healthcare provider, AI can automate up to 20% of tasks, such as appointment scheduling and billing, minimizing errors and fraud and potentially saving billions annually.

The pharmaceutical industry is also leveraging AI to speed up drug discovery. By analyzing molecular data and predicting potential drug interactions, AI significantly reduces the time and resources needed, ultimately cutting the overall costs of drug development.

Learn more: The Potential and Benefits of Artificial Intelligence in Healthcare

AI for Cost Savings: Tips for Making It Work

While many companies are eager to adopt AI, experts caution that the rush to implement AI may not yield the expected benefits. Gartner predicts that by 2028, over 50% of organizations that develop large AI models from scratch will abandon their efforts due to high costs and complexity.

Despite these challenges, businesses can enhance their success rates and realize AI cost savings by following a structured approach. Gartner outlines three AI use cases that can deliver varying timelines and ROI:

1. Quick wins: Adopting AI productivity tools like Microsoft Copilot, Google Workspace, and ChatGPT will provide immediate returns. The affordable and user-friendly solutions can substantially boost productivity in a short time.
2. Differentiating use cases: More robust projects, such as predictive maintenance, supply chain automation, and AI-driven customer service, require from one to two years of worthwhile investment investment. In contrast, simpler AI applications like data analysis and basic automation can be implemented with minimal investment, allowing businesses to quickly see returns without significant upfront costs.
3. Transformational initiatives: High-cost, high-risk projects, such as creating data lakes and advanced analytics platforms, should be considered only after achieving success with quicker, smaller-scale use cases. These transformational efforts can enable a data-driven organization, but only if foundational capabilities are established first.

To maximize AI cost savings, enterprise leaders should start with quick wins and differentiating use cases to demonstrate value. This approach can help secure buy-in from senior leadership and justify larger investments for transformational initiatives.

Cost Efficiency Strategy

Successful AI adopters often adopt a strategic process to guarantee effectiveness and cost efficiency. Here’s how your business can do it:

1. Identify a use case and solvable problem: Focus on a specific challenge where AI can provide clear benefits.
2. Conduct an AI feasibility analysis: Evaluate whether the proposed solution is practical and achievable within your organization.
3. Collect and prepare data: Gather the necessary data for training AI models, safeguarding their quality and relevance.
4. Deploy the AI platform: Implement the chosen AI solution while taking into account the compatibility with existing systems.
5. Monitor and assess the ROI: Continuously evaluate the performance of the AI initiative to measure its impact on costs and efficiency.

Additionally, McKinsey suggests incorporating technical enablers like feature stores, code assets, standards, and machine learning operations (MLOps) to effectively scale AI initiatives and achieve further cost savings.

Final Thoughts

Now that you understand how AI can bring increased efficiency and reduce operational costs through automation, predictive maintenance, enhanced customer service, streamlined supply chain operations, and healthcare processes, the next step is to integrate AI solutions into your business operations.

In order for your AI cost reduction initiatives to succeed, it’s important to analyze existing business processes and pinpoint areas where AI technology can be effectively deployed.

Neurond provides high-quality AI consulting and development services to help you achieve these goals and stay competitive in an ever-evolving market. Our proven expertise and innovative solutions ensure to empower your business to unleash the full potential of artificial intelligence, offering not only cost savings but also paving the way for sustainable and strategic growth.