From Layoffs to Profits: AI Operational Efficiency's Impact

"What the steam engine did for mechanical work, mechanical labor, this technology (AI) is going to do for intellectual labor." – Aidan Gomez, CEO of Cohere.

How does AI affect operational efficiency? Many businesses are racing to unlock AI's value while quickly realizing that AI operational efficiency is expensive. 

A recent study by Mindtree, which surveyed 650 global IT leaders, found that 85% of organizations have implemented a data strategy, and 77% have invested in AI technologies. Similarly, a McKinsey survey reveals that 60% of organizations with reported AI adoption are using generative AI, and this trend is expected to grow as more companies recognize the strategic advantages of AI. However, the costs of cloud services, hiring specialized AI talent, conducting experiments, and developing new models add up. For example, enterprise-grade GenAI can cost up to $30 per user monthly.

Given the high cost of AI, many companies are increasing their focus on AI operational efficiency. It all started when Mark Zuckerberg spoke about Meta’s management theme for 2023. He said it was the ‘Year of Efficiency’ and they would focus on becoming a more robust and agile organization. Following this, Meta conducted three layoffs between November 2022 and May 2023, cutting around 21,000 jobs since 2022. Zuckerberg’s strategic shift towards operational efficiency resulted in Meta’s net income increasing by 201% during the last quarter of 2023, with shares up by 178%.

Following Meta's lead, the tech industry saw widespread workforce reductions. Companies like Google, Amazon, Tesla, Microsoft, Apple, Cisco, SAP, and Sony made significant cuts, aiming to increase operational efficiency. Over 270 tech companies laid off more than 70,000 employees in the first quarter of 2024 alone. Wedbush Analyst Daniel Ives views these layoffs as the “first major step” in stabilizing struggling stocks. Many tech companies had hired aggressively during the Covid-19 pandemic, leading to high costs and inefficiencies. Now, reducing the workforce is seen as necessary to balance those costs.

In April 2023, CEO Drew Houston announced that the company would cut 16% of its workforce to focus more on AI and early-stage product development. Houston stated in his email to employees, "These transitions are never easy, but I’m determined to ensure that Dropbox is at the forefront of the AI era." The resources saved from these job cuts will be redirected toward AI operational efficiency initiatives. This resonates with a broader trend of companies reallocating funds from operational efficiencies to invest in AI.

The main reason for this trend is that companies see the potential of AI operational efficiency to help them maintain competitiveness. Using AI as a copilot, they streamline processes, gain new insights, and boost overall efficiency. Recognizing these benefits, many tech companies are making significant efforts to stay ahead in the AI race. As Gil Luria, an analyst at D.A. Davidson & Co., commented, "No company wants to get left behind by the AI revolution, and they are all making sure they have these capabilities and are prioritizing them, even when it is at the expense of other initiatives."

In this article, we'll explore how companies are reallocating resources for AI operational efficiency investment, enabling workforce reductions, and driving stock prices higher. We'll also discuss how AI operational efficiency works and offer practical advice for business executives on maximizing return on investment (ROI) through AI.

AI Operational Efficiency Boosts Big Tech

The enthusiasm around AI has dramatically impacted the stock market, benefiting a select group of tech giants known as the "Magnificent 7": Alphabet, Amazon, Apple, Meta, Microsoft, Tesla, and Nvidia. These companies, alongside AI, are driving stock market gains. For example, the S&P 500's 24% gain in 2023 was primarily due to the performance of these seven firms.

Goldman Sachs analysts suggest that “increased economy-wide output could translate into increased revenues and earnings for S&P 500 companies, even beyond those firms directly involved in the development of AI.” As AI operational efficiency, the financial benefits are expected to extend to a broader range of companies, not just the tech giants. The widespread economic impact of AI is contributing to the overall rise in stock prices as investors anticipate long-term gains from AI-driven advancements.

These insights show that, while AI requires a significant investment, it dramatically increases operational efficiency and profitability. To understand how AI operational efficiency can be integrated into enterprises, let's walk through practical implementations of AI that have proven to improve business operations and make them more efficient. We'll look at examples from customer service, healthcare, and finance.

Axis Bank's AI-Powered Customer Service

Customer service is a pillar of businesses that can be easily transformed by AI. Adam Devine, CMO at WorkFusion, supports this by saying: “Adding natural language processes and machine learning changes everything, giving virtual customer assistants (VCAs) the ability to determine not just what rules-based action to take based on a word, but to understand the meaning of words in different combinations, ask questions to create context and intent, and actually do something for the customer.” 

Research shows that AI in customer service could save companies up to $1 trillion a year.  They can solve many problems that plague customer service and offer benefits like lower staffing requirements, 24/7 customer service, and faster resolution times.

For example, India's Axis Bank has deployed an AI-powered voice assistant named AXAA to enhance its customer service. Using speech recognition and Natural Language Processing (NLP) technology, AXAA handles queries in Hindi, English, and Hinglish. This voicebot simplifies customer interactions by allowing direct requests, such as generating a debit card PIN, and bypassing complex Interactive Voice Response (IVR) menus.

The benefits include over 90% accuracy in resolving queries across 17 essential services, reducing IVR traversal times, and providing 24/7 support. Furthermore, with 17 services handled by the bot, AXAA manages 12-15% of calls seamlessly. Ratan Kesh, EVP & Head of Retail Operations and Service at Axis Bank, states, “We receive close to a hundred thousand customer calls at our call centres daily. VoiceBot gives us the capability and flexibility to handle customer calls coming on a 24×7 basis. It is a great value for us from an operational efficiency perspective.”

Integrating AI operational efficiency into customer service can increase customer satisfaction by reducing the workload on call centers and improving the overall customer experience.

AI Operational Efficiency Improves Healthcare: Insights from Cleveland Clinic

Healthcare is also undergoing a significant transformation thanks to AI. AI can analyze data and automate tasks to save hospital staff time. Cleveland Clinic has perfectly demonstrated how AI can handle administrative tasks better, leading to AI operational efficiency. "We're very excited about our ability to use artificial intelligence to run our own business smarter, better, in a more efficient way.", said Cleveland Clinic President and CEO Tom Mihaljevic. He also added how AI is a big help due to the shortage of healthcare staff. 

One of their AI-driven solutions is a patient portal known as an advice companion. The portal helps patients with chronic diseases by providing AI-generated responses about their conditions. As a result, patients get timely and detailed answers without waiting for a physician. The system saves caregivers time and ensures patients receive compassionate and thorough information about their health.

They also collaborate with AI-driven companies to predict patient influx and surgeries and optimize resource allocation. In addition to all the above, they have launched the Center for Clinical Artificial Intelligence to translate AI-based concepts into clinical tools that will improve patient care and advance medical research. Despite inflation and a global shortage of caretakers, the clinic saw profits in 2023, with over $14 billion in revenue. Rising costs, including a 20% increase in drug expenses, were offset, resulting in a small budget surplus with an operating margin of 0.4%, improving from a $200 million loss in 2022. Part of this success is attributed to AI-driven operational efficiency.

JPMorgan Reduces Time Spent Reading Agreements

The most productive finance teams use the latest technology to get further and make more accurate decisions. A recent report reveals that 91% of financial services companies either assess AI operational efficiency or use it in production. AI can automate tedious financial analysis, complicated report-making processes, and rigorous compliance checks.

“This is not hype. This is real. When we had the internet bubble the first time around … that was hype. This is not hype. It’s real. People are deploying it at different speeds, but it will handle a tremendous amount of stuff”

Said Jamie Dimon, CEO of JPMorgan about their use of AI. AI has transformed JPMorgan's operations, drastically reducing the time spent on tasks like interpreting business credit agreements from 360,000 hours annually to mere seconds. Using an AI-driven Contract Intelligence platform, COiN, the bank has automated the document review process for a specific category of agreements.

Powered by a private cloud network, the COiN system compares and identifies contract clauses using image recognition. Initially, COiN extracted about 150 relevant attributes from annual business credit agreements within seconds. The algorithm identifies patterns based on contract terms or locations, resulting in significant time and cost savings, improved efficiency, and reduced errors. The bank reports that the software has already helped reduce loan-servicing mistakes, often due to human error, in interpreting 12,000 new contracts per year.

Tips for Achieving AI Operational Efficiency 

The drive for operational efficiency is reshaping business strategies, with leaders aiming to improve stock prices and redirect investments into AI. Companies leverage AI strategies to achieve cost savings, boost productivity, and enhance customer satisfaction. Measuring the success of these AI strategies is vital for achieving these goals.

Direct measurement of AI impact can be challenging. Sunil Dadlani, Senior Vice President and CIO of Atlantic Health System, explains, "When there’s no direct way to measure the business impact of an AI project, companies will mine data from related key performance indicators (KPIs) instead."

Research by Gartner reveals that IT leaders in mature AI organizations identify business metrics early and use clear attribution strategies. Based on well-defined and measurable KPIs, these metrics ensure that AI initiatives align with strategic goals and deliver immediate and long-term value. This method allows businesses to track the effectiveness of their AI operational efficiency. Implementing AI enables companies to enhance efficiency, reduce mistakes, and concentrate on strategic goals, driving long-term success.

By following the strategies employed by leading companies like Swedbank, Cleveland Clinic, and JPMorgan, businesses can unlock the full potential of AI. Whether enhancing customer service, optimizing healthcare operations, or transforming financial services, AI operational efficiency offers the tools to drive achieve sustainable growth. As the adoption of AI continues to rise, those who strategically integrate AI operational efficiency will be well-positioned to lead in their respective industries.