Artificial Intelligence (AI) has continuously pushed the boundaries of what’s possible, outperforming humans on a handful of benchmarks like image classification (2015), reading comprehension (2017), visual reasoning (2020), and natural language inference (2021).
In 2023, AI’s capabilities have far exceeded human performance in numerous tasks, marking a pivotal shift in technology’s role in business. Digital transformation is no longer just a trend; it become essential for staying competitive, with AI leading this revolution.
Today, the scope of AI’s impact is expanding, encompassing everything from advanced large language models to innovative robotics. AI is set to redefine the business landscape, enhancing productivity, customer service, software development, and operational efficiency, all while driving cost savings and boosting overall output from 2024 and beyond.
Key AI Statistics
AI statistics and trends underscore its significant impact across industries. A report by the Capgemini Research Institute highlights the rapid acceleration of AI adoption. This year’s report also examines shifts in generative AI (GenAI) adoption, focusing on investment levels and the benefits organizations have gained.
Key highlights from the report include
Figure 1: % of Organizations Who Agree with the Statement on Generative AI Maturity
Source: Capgemini Research Institute
Generative AI executive survey, April 2023, N = 800 organizations; Generative AI executive survey, May–June 2024, N = 940 organizations that are at least exploring generative AI capabilities.
Figure 1 shows that generative AI adoption has surged over the past year in the 1,100 organizations surveyed, with only 6% yet to explore the technology. Of those who have started (n=1,031), 24% are now integrating it into some or most of their operations, up from 6% last year.
Figure 2: Organizations Generative AI Maturity, by Sector
Source: Capgemini Research Institute
Generative AI executive survey, April 2023, N = 800 organizations; Generative AI executive survey, May–June 2024, N = 1,031 organizations that are at least exploring generative AI capabilities; N varies per functional use case, ranging from 499 to 716.
Figure 2 illustrates that since 2023, GenAI adoption has grown across all sectors. Many industries that initially launched pilot GenAI initiatives in 2023 have implemented AI capabilities across several or most functions and locations. For instance, 40% of retail organizations are currently utilizing GenAI in various functions and locations, up from 17% in 2023.
Figure 3: Organizations Implementing Generative AI, by Functions
Source: Capgemini Research Institute
Generative AI executive survey, April 2023, N = 800 organizations; Generative AI executive survey, May–June 2024, N = 1,031 organizations that are at least exploring generative AI capabilities; N varies per functional use case, ranging from 499 to 716.
GenAI is transforming organizations, driving a shift in operational models. Over the past year, AI adoption has rapidly increased across various domains, including sales, marketing, IT, operations, R&D, finance, and logistics. For instance, IT, risk management, and sales adoption rates have risen to 27%, 26%, and 25%, respectively, up from just 4%, as illustrated in Figure 3.
Figure 4: Average Benefits Realized from Generative AI within the past
Source: Capgemini Research Institute
Generative AI executive survey, May–June 2024, N = 940 organizations that are at least exploring generative AI capabilities.
Figure 4 highlights the organizational benefits of GenAI over the past year in areas where it has been piloted or deployed. On average, organizations have experienced a 7.8% improvement in productivity, a 6.7% increase in customer engagement and satisfaction, a 5.4% boost in operational efficiency, a 4.4% rise in sales, and a 3.6% reduction in costs.
Research indicates that these gains primarily stem from pilot projects or partial implementations of GenAI. The benefits are expected to grow as more organizations fully scale AI across all operational areas.
Figure 5: Average Generative AI Investment in 2024, by Annual Revenue
Source: Capgemini Research Institute
AI executive survey, May–June 2024, N = 981 organizations who are at least exploring generative AI capabilities, excluding the public sector.
Figure 5 illustrates the financial commitment to GenAI, with organizations, on average, allocating around $110 million in 2024. While 34% invested $50 million or less, 11% committed $250 million or more. Investment generally increases with company size, with firms earning $1–5 billion spending $85 million, and those earning over $20 billion allocating $158 million.
80% of these surveyed organizations have increased their generative AI investment since last year. None reduced their investment, while 20% maintained the same level. This trend is consistent across all sectors and company sizes in the study.
For instance:
- In aerospace and defense, 88% of organizations increased their generative AI investment.
- In retail, the lowest among all sectors, 66% have invested in generative AI.
- Among smaller companies ($1–5 billion revenue), 73% have increased their investment, compared to 89% of those with revenues above $20 billion.
Additional AI statistics shaping business in 2024, as highlighted by Vena Solutions:
- Market research predicts the AI market will reach nearly $1.8 trillion by 2030, with an expected CAGR of 37.3%.
- By 2025, global investments in AI-related technologies are projected to reach nearly $200 billion.
- AI is expected to boost global GDP by up to 26% by 2030.
- In 2023, AI tools had over 250 million users globally, more than doubled in 2020, with projections to exceed 700 million by 2030.
- A Harvard study found that management consultants using AI tools were 25.1% more productive, completed 12.2% more tasks, and produced over 40% higher quality work compared to a control group.
- Goldman Sachs estimates that GenAI could increase productivity growth by 1.5% and raise global GDP by 7%, the equivalent of $7 trillion, over the next 10 years.
- The AI SaaS market is expected to reach $1.5 trillion by 2030, with a compound annual growth rate (CAGR) of 37.66% from 2022 to 2030.
- Business intelligence (BI) platforms can simplify complex, unstructured data, which accounts for 80% of all available data, to generate actionable insights that enhance workflow efficiency.
- Employers surveyed by Amazon indicated a willingness to pay an average of 43% higher salaries for employees with AI skills in sales and marketing.
- A significant majority (85%) of the general public believes that a collaborative effort among government, industry, and academia is essential to ensure AI safety and security.
o the seventh edition of the AI Index report. The 2024 Index is our most comprehensive to date and arrives at an important moment when AI’s influence on society has never been more pronounced.
Key highlights from the 2024 Artificial Intelligence Index Report:
- AI beats humans on some tasks, but not on all. AI outperforms humans in tasks like image classification and visual reasoning but lags in complex areas like advanced mathematics and commonsense reasoning.
- Industry continues to dominate frontier AI research. Industry led AI research in 2023, producing 51 models, while academia contributed 15. A record 21 models came from industry-academia collaborations.
- Frontier models get way more expensive. Training costs for advanced AI models are soaring. For example, OpenAI’s GPT-4 used an estimated $78 million, while Google’s Gemini Ultra cost $191 million for compute.
- The US leads China, the EU, and the U.K. as the leading source of top AI models. The U.S. led in AI development with 61 models in 2023, ahead of the EU (21) and China (15).
- Robust and standardized evaluations for LLM responsibility are seriously lacking. Responsible AI reporting lacks standardization, complicating risk efforts to systematically compare the risks and limitations of top AI models.
- Generative AI investment skyrockets. Despite an overall dip in AI private investments, GenAI funding surged to $25.2 billion in 2023, an eightfold increase from 2022.
- The data is in: AI makes workers more productive and leads to higher quality work. AI improves productivity and work quality, narrowing skill gaps but requires oversight to prevent performance declines.
- Scientific progress accelerates even further, thanks to AI. AI is accelerating scientific discovery, with notable applications in efficient sorting and materials discovery.
- The number of AI regulations in the United States has sharply increased. AI-related regulations in the U.S. have risen sharply, with 25 in 2023, up from just one in 2016.
- People across the globe are more cognizant of AI’s potential impact—and more nervous. Awareness and anxiety about AI are growing; 66% believe AI will significantly impact their lives soon, and 52% feel more concerned than excited.
Top AI Use Cases and Success Stories
AI has rapidly transformed various industries by providing innovative solutions and creating unprecedented opportunities for growth. Its powerful applications have catalyzed change across fields like healthcare, finance, and manufacturing. Here, we explore the most prominent AI use cases and highlight notable success stories demonstrating its transformative impact.
- Personalized Customer Experience – By examining data from purchase history, browsing behavior, and social media activity, AI creates a highly customized experience for each customer. This approach improves interactions and recommendations by aligning them with individual preferences and behaviors, ultimately increasing satisfaction and loyalty.
Success Stories:
- Sephora: Its Virtual Artist tool enables customers to try on makeup virtually and receive customized beauty advice, leading to higher engagement and sales.
- Amazon: AI-driven product recommendations based on browsing and purchase history have enhanced cross-selling and upselling, contributing to significant sales growth.
- Safety Control – AI-driven safety systems leverage sensors, cameras, and data analytics to oversee environments, efficiently detecting anomalies that could pose risks. By processing large volumes of data from multiple sources simultaneously, these systems can swiftly pinpoint issues that might be overlooked by human oversight.
Success Stories:
- Tesla: The Autopilot system utilizes AI to monitor road conditions and driver behavior, reducing accidents and enhancing overall driving safety.
- Nest (Google): AI integrated into home safety products, such as smoke detectors and intrusion alarms, helps distinguish between genuine emergencies and false alarms.
- Workforce Optimization – AI streamlines work schedule management by predicting staffing needs from historical data and current trends. It helps businesses avoid overstaffing and understaffing, while also considering employee preferences, availability, and skills to create more accommodating shifts.
Success Stories:
- Hilton Hotels: By integrating AI into employee scheduling, Hilton improved staff satisfaction and operational efficiency.
- DHL: AI is used to forecast workload and optimize staff deployment in warehouses, leading to increased efficiency and lower operational costs.
- Content Creation – AI enhances content creation by analyzing trends, crafting narratives, and automating repetitive tasks, ensuring a consistent flow of high-quality content tailored to audience preferences.
Success Stories:
- Associated Press (AP): AI is employed to automate routine reports like earnings reports, allowing journalists to concentrate on more complex investigative work.
- BuzzFeed: Leverages AI to analyze reader data and create viral headlines, optimizing content for maximum engagement.
- User Authentication – AI-driven security enhances protection through biometric verification (e.g., fingerprint or facial recognition) and multi-factor authentication (MFA), which requires additional steps like code entry or biometric scans. AI also monitors for suspicious activities and behavioral anomalies, prompting further verification when needed.
Success Stories:
- Apple: Utilizes AI in its Face ID technology to offer secure and seamless authentication for iPhone users.
- HSBC: Implemented AI-based voice recognition for secure telephone authentication, reducing fraud and enhancing customer experience.
- Predictive Maintenance – AI predicts machine issues by analyzing sensor data (like temperature and vibrations) and comparing it to historical trends, alerting when repairs are needed. In IT, AI detects signs of server slowdowns or unusual network activity, enabling early interventions to prevent crashes or breaches.
Success Stories:
- General Electric (GE): Employs AI to monitor jet engines, predicting maintenance needs before problems occur, which improves reliability and safety.
- Shell: Implements AI for predictive maintenance in oil and gas operations, cutting downtime and enhancing operational efficiency.
- Customer Support – AI improves customer support with 24/7 chatbots, automated responses, and smart inquiry routing. Chatbots handle common queries instantly, reducing the need for human intervention and speeding up the process. AI also directs inquiries to the right agents, ensuring faster and more efficient resolution.
Success Stories:
- Bank of America: Launched Erica, an AI-driven virtual assistant, to handle customer inquiries and offer financial advice, enhancing engagement and support efficiency.
- H&M: Utilizes AI chatbots to assist with shopping queries and provide product recommendations, enhancing the customer experience.
- Employee Onboarding – AI streamlines onboarding by automating paperwork and offering virtual assistants, improving new hire integration, and reducing administrative work. It customizes training for specific roles and experience levels and provides instant answers to new hires’ questions, reducing the need for manager intervention.
Success Stories:
- Unilever: Utilized AI to improve hiring and onboarding with tools that screen candidates and tailor onboarding experiences, resulting in better employee integration and satisfaction.
- Walmart: Uses AI to automate initial training modules for new hires, ensuring consistency and quality in training while allowing managers to focus on other responsibilities.
- Process Automation – AI boosts process automation by handling repetitive tasks such as data entry, routine emails, and inventory management quickly and accurately. This increases efficiency, saves time, and allows employees to focus on strategic and creative work.
Success Stories:
- Coca-Cola: Utilizes AI to optimize supply chain management, improving logistics and inventory processes while reducing operational costs.
- IBM: Employs Robotic Process Automation (RPA) to automate repetitive tasks such as data entry and customer support, enhancing efficiency and accuracy, and allowing employees to concentrate on higher-value activities.
- Financial Reporting – AI enhances financial reporting by automating data collection, analysis, and report generation, ensuring accuracy, compliance, and timely delivery. It quickly gathers and analyzes data, identifies patterns and anomalies, and produces reports efficiently, providing businesses with reliable insights for better decision-making.
Success Stories:
- KPMG: Uses AI to automate financial auditing and reporting, enhancing accuracy and efficiency while reducing audit completion time.
- Deloitte: Implements AI tools to streamline financial advisory services, automating complex data analysis and reporting for quicker, more accurate insights.
Additional notable examples highlight recent generative AI use cases across different sectors and functions:
| Sector | Company | Function | Example |
|---|---|---|---|
|
Aerospace |
Airbus |
Manufacturing |
Airbus revolutionized its operations and innovation processes with GenAI. AI assistants provide aircraft manufacturing instructions, improving access to technical data and facilitating precise task guidance. |
|
Automotive |
Toyota |
Product design/R&D |
Toyota leverages GenAI to integrate engineering constraints into vehicle design, optimizing metrics like aerodynamic drag and improving the efficiency of electric vehicle (EV) development. |
|
Automotive |
Mercedes-Benz |
Customer experience/ service |
Mercedes-Benz uses GenAI in its “Hey Mercedes” feature, currently in beta with 900,000 users, to provide personalized, screen-free interactions that enhance the driving experience with dynamic adjustments and real-time safety support. |
|
Consumer products |
General Mills |
Customer experience/ service |
General Mills launched MillsChat, a GenAI tool that streamlines customer service, enhances efficiency, and boosts engagement with personalized assistance. |
|
Consumer products |
PepsiCo |
Marketing and branding |
PepsiCo uses generative AI to analyze customer feedback, which it uses to refine Cheetos’ shape and flavor. This approach has increased market penetration by 15%, shortened product launch cycles, and boosted profitability. |
|
Consumer product |
Unilever |
Legal |
Unilever uses GenAI to streamline legal processes, such as research and contract reviews, allowing legal teams to focus on strategic tasks and improving operational effectiveness. |
|
Energy |
British Petroleum (BP) |
Human resources – employee productivity |
British Petroleum uses GenAI to assist with tasks like email management, boosting employee productivity, and transforming workflows. |
|
Financial services |
Morgan Stanley |
Customer experience/service |
Morgan Stanley used GPT-4 to develop an AI tool for financial advisors, providing quick access to internal research and enhancing efficiency and client service. |
|
High tech |
BrainBox AI |
ESG/sustainability |
BrainBox AI uses GenAI to lower the carbon footprint of commercial buildings, predicting interior temperatures with historical data. This cuts HVAC energy costs by up to 25% and reduces greenhouse gas emissions by 40%. |
|
Industrial manufacturing |
Rockwell Automation |
IT – coding/software development |
Rockwell Automation integrated GenAI into FactoryTalk Design Studio, enabling engineers to generate code with natural language prompts, automating routine tasks, and enhancing design efficiency. It also helps experienced engineers speed up development and mentor newcomers. |
|
Industrial manufacturing |
Schneider Electric |
Finance and accounting |
Schneider Electric integrated GenAI into Finance Advisor, a tool for financial analysts, ensuring precise, compliant, and timely decision-making across accounting functions. |
|
Logistics |
UPS |
Marketing |
UPS developed the Message Response Automation (MeRA) system using large language models (LLMs) to automate routine customer interactions, cutting email handling times by 50% and allowing agents to focus on complex issues. |
|
Pharmaceutical and biotech |
Insilico Medicine |
Product design/R&D |
Insilico Medicine discovered the new drug candidate MYT1 using its GenAI platform throughout the preclinical drug-discovery process, providing more effective and safer treatments for breast and gynecological cancers. |
|
Pharmaceutical and biotech |
Moderna |
Research and development |
Moderna uses GenAI tools, including Dose ID GPT, which leverages ChatGPT Enterprise’s Advanced Data Analytics to optimize vaccine dosing. |
|
Public sector |
California Department of Transportation and Department of Tax and Fee Administration |
Citizen services |
CDTFA aims to use GenAI to analyze traffic data, enhance road safety, reduce call-center wait times, assist non-English speakers, and streamline healthcare facility inspections. |
|
Public sector |
State of Pennsylvania |
Employee operations |
The State of Pennsylvania’s GenAI pilot program aids government employees by supporting copywriting, policy updates, job description drafting, and code generation. |
|
Retail |
ASOS |
Sales |
ASOS uses GenAI for fashion recommendations, customer service, and trend analysis, boosting engagement, personalizing experiences, and optimizing retail strategies. |
|
Retail |
Walmart |
ESG/sustainability |
Walmart uses GenAI to reduce food waste by guiding employees on product decisions through a digital dashboard that suggests actions like price changes, sales, returns, or donations based on product characteristics. |
|
Retail |
Baskin Robbins |
Product design/R&D |
Baskin Robbins uses GenAI at its Seoul, Korea store to create new ice cream flavors, support a monthly exclusive flavor, and personalize experiences through an ice cream docent program. |
|
Telecom |
AT&T |
Employee productivity |
AT&T uses GenAI in its Ask AT&T tool to boost productivity and creativity, translate documents, optimize networks, and summarize meetings, resulting in increased efficiency and innovation. |
|
Telecom |
Vodafone |
Customer experience/service |
Voxi by Vodafone introduced a GenAI self-service experience that enhances interactions, offers personalized assistance, and optimizes customer support, boosting satisfaction and engagement. |
2024 AI Trends to Look Out for
Technological advancements, rising investments, and talent competition are set to drive a fundamental shift in business operations in 2024, with AI leading the change. GenAI’s versatility enhances various business functions, making AI more accessible and scalable.
Here are some of the anticipated trends in 2024 that will significantly impact how businesses operate and create lasting value.
- The right AI decisions will give companies a substantial competitive advantage. Companies that strategically leverage GenAI will gain a significant competitive advantage. Success requires more than simply adopting GenAI; it involves customizing and scaling its capabilities while managing potential risks.
- GenAI will reshape the roles of leaders as much as those of employees. AI will start transforming job functions across all levels. Managers will need to adapt to overseeing teams where AI handles many tasks, while C-suite executives must spearhead AI-driven operations and business models. Bridging the gap between organizational and AI expertise will be essential for leaders.
- Responsible AI practices are essential to build and maintain this trust. AI will become integral to interactions with data and stakeholders. Trust in AI will be crucial, requiring more than compliance and security—it involves deploying the right solutions with proper data, policies, and oversight.
- GenAI will be a crucial tool for unlocking value from data. GenAI will unlock the value in data by quickly transforming large volumes of unstructured information—such as presentations and customer logs—into actionable insights. It improves cost/benefit ratios for data initiatives by scanning, summarizing, translating, and analyzing complex data.
- GenAI will accelerate and broaden transformation efforts. By processing unstructured data and integrating it with cloud technology, GenAI can fast-track data-related transformation initiatives and enable the leapfrogging of traditional stages.
- GenAI will revolutionize how businesses create new products and services. GenAI will enable “no code” development of new processes and offerings, allowing domain experts and creatives to work directly with AI and data, provided there’s strong governance.
Keeping Up with the AI Tech Evolution
The rapid evolution of AI has brought about a transformative shift across industries, surpassing human performance in various benchmarks and redefining technology’s role in business. The statistics and trends for 2024 underscore the growing significance of AI, particularly generative AI (GenAI), which is reshaping business operations and creating substantial value.
From financial services and SaaS to manufacturing and healthcare, AI is enhancing decision-making, providing deeper customer insights, and streamlining processes. Looking ahead, strategic investments in AI, along with a focus on responsible practices and ongoing learning, will be essential for fully leveraging its potential and staying competitive in an ever-evolving landscape.