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AI use cases:
an in-depth trend overview for 2026
December 23, 2025
AI adoption & use cases across business functions
Owing to abundant business data, recent technological advancements, and companies striving for broader efficiency gains, artificial intelligence is increasingly adopted not just for isolated tasks, but across a wide range of business functions. However, the adoption rate of individual AI-powered solutions, such as AI agents and GenAI chatbots, varies greatly depending on the use case.
General trends
| The percentage of organizations reporting using AI tools in at least one business function increased from 78% in 2024 to 88% in 2025. As for generative AI, the percentage grew from 71% in 2024 to 79% in 2025. | |
|---|---|
| Top business functions where organizations are currently using or planning to use AI agents include customer service and support (57% of organizations), marketing and sales (54%), and IT and cybersecurity (53%). | |
| The business functions where GenAI initiatives are at the most advanced stage include IT (28% of organization), operations (11%), marketing (10%), customer service (8%), and cybersecurity (8%). | |
| In 2025, 65% of organizations reported using genAI in at least one business function. | |
| Enterprises prioritize operations and compliance-heavy areas for their AI agent initiatives, with 46% of use cases focusing on business functions like procurement, HR, and finance where scale, control, and risk management are essential. |
AI in CRM
| Most US B2B marketers (52%) use AI for content-related tasks. | |
|---|---|
| 64% of companies have already developed generative AI use cases in marketing. | |
| 38% of professionals who worked in marketing, PR, sales, or customer service roles identified increased efficiency as the main benefit of using generative AI for social media marketing, while 34% cited easier idea generation. | |
| By 2028, AI agent machine customers will replace 20% of customer interactions at human-readable digital storefronts. | |
| Adopting GenAI and agentic AI in customer operations can educe operational costs by 22%. Use cases driving cost savings include call summarization, chatbots, automated responses, and personalized self-service. | |
| By 2029, 80% of common customer service issues will be resolved via AI agents. |
Scheme title: Top 5 functions prioritized for agentic AI usage
Data source: BCG
AI in HR
| Most organizations adopting artificial intelligence for recruiting use it to generate job descriptions (65%), while companies relying on AI for learning and development typically leverage it to provide employees with personalized training paths (49%). | |
|---|---|
| 25% of HR departments surveyed rely on AI. This technology is most frequently used for talent acquisition (42%), employee training and development (36%), and people analytics (21%). | |
| The adoption of GenAI and agentic AI in people operations can help cut operational and administrative costs by 16%, compliance and legal costs by 17%, and training and development costs by 16%. Key use cases resulting in cost savings include employee self-service, compliance monitoring, and query resolution. | |
| Top-priority GenAI use cases among HR leaders include service delivery via employee-facing chatbots (43%), administrative tasks, policies, and document generation (42%), and job descriptions and skills data (41%). | |
| Content creation for talent acquisition, recruiting, and onboarding is the generative AI use case with the greatest value potential in HR (20%). |
Scheme title: Top AI use cases in human resources
Data source: SHRM
AI in finance & accounting
| Nearly three-quarters of companies surveyed already rely on AI for financial reporting, and this share is projected to rise to 99% by 2027. | |
|---|---|
| Implementing GenAI and agentic AI in finance and accounting workflows, particularly for automated audit compliance and intelligent financial reporting, can result in a 24% reduction in compliance costs. | |
| 57% of accounting professionals think that bookkeeping will be the function most impacted by artificial intelligence. |
AI in supply chain management
| Organizations adopting GenAI and agentic AI for supply chain use cases like spend optimization and contract renewal management can reduce supplier and procurement costs by 27%. | |
|---|---|
| GenAI tools can auto-generate shipping documents and identify potential mistakes, helping cut the lead time for producing documentation by up to 60% and reduce logistics coordinators' workload by up to 20%. | |
| 62% of companies that rely on AI for supply chain management use this technology to monitor and measure sustainability. |
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Use of AI across industries
Industry-specific needs largely shape how companies prioritize AI solutions implementation. For instance, retailers typically focus on improving customer experience with AI capabilities, while financial institutions primarily use AI to mitigate business risk.
Scheme title: Business functions in which organizations regularly use AI, by industry
Data source: McKinsey
AI in retail & ecommerce
| Top AI use cases for ecommerce that businesses are investing in include personalized customer recommendations (47%), conversational AI solutions (36%), and adaptive advertising, promotions, and pricing (28%). As for brick-and-mortar AI use cases, retailers are prioritizing store analytics (53%), adaptive advertising, promotions, and pricing (40%), and stockout and inventory management (39%). . | |
|---|---|
| Top generative AI use cases that retail companies are considering or implementing include personalized product recommendations (66%), branded AI assistant for customers (52%), and customer data analysis and segmentation (50%). Other popular use cases include marketing content generation (59%), predictive analytics (50%), and dynamic code generation (41%). | |
| Nearly 90% of retail marketing managers surveyed believe AI would save them time when setting up a marketing campaign, while another 71% plan to invest in AI to increase customer engagement. |
AI in healthcare
Overall
Medtech, Tools, Diagnostics
Digital Healthcare
Pharma & Biotech
Payers & Providers
Scheme title: Top use cases of AI in healthcare
Data source: NVIDIA
| 81% of consumers surveyed have used an AI-powered bot or voice assistant to get healthcare support in the past year. | |
|---|---|
| 66% of patients surveyed expect their healthcare providers to use generative AI to improve online and phone support. | |
| Top use cases for large language models in healthcare are patient question answering (21%), medical chatbots (20%), and information extraction/data abstraction (19%). |
AI in financial services
| Top AI use cases that financial organizations invested in are risk management (36%), portfolio optimization (29%), fraud detection (28%), and algorithmic trading (27%). | |
|---|---|
| In 2025, banks used AI for data-driven insights and personalization (85%), operational efficiency and automation (79%), security management and fraud prevention (78%), and regulatory compliance and risk prevention (71%). | |
| Top use cases for GenAI and LLMs that financial services firms used or considered implementing include customer experience and engagement via chatbots, virtual assistants, and other AI tools (60% of companies surveyed), report generation, synthesis, and investment research (53%), and document processing (53%). | |
| Top GenAI use cases in terms of ROI include trading and portfolio optimization (mentioned by 25% of respondents) and customer experience and engagement (21%). | |
| The most popular use cases of predictive AI in financial services include risk management (15%), fraud detection (12%), operations (10%), and compliance (10%). | |
| According to banking executives, top use cases for agentic AI include enhancing fraud detection (56% of leaders surveyed), strengthening security (51%), cutting cost and increasing efficiency (41%), and improving the customer experience (41%). |
AI in manufacturing
| Top AI use cases in manufacturing by adoption include production (39%), inventory management (33%), and quality operations (24%). | |
|---|---|
| Nearly 80% of the manufacturing companies surveyed have invested or plan to invest in computer vision solutions, such as AI-based robotic systems for package sorting. | |
| Industry 4.0 front-runners experienced a two to three times increase in productivity and a 30% decrease in energy consumption by using AI for demand forecasting, heavy-transport equipment routing, and other manufacturing use cases. | |
| AI can help manufacturing companies reduce failures in the assembly process by 70%, cut quality control efforts by 50%, and increase visual inspection accuracy by 80%. |
AI in automotive
| Automotive OEM executives expect that the share of total revenue attributable to AI will increase from the current 5% to 9% within three years, with fleet management use cases such as predictive maintenance representing a particularly promising area. | |
|---|---|
| 64% of automotive executives believe autonomous driving will be one of the top customer expectations by 2035, potentially becoming the most prominent use case of an AI-enabled driving experience. | |
| Top AI agent use cases in automotive according to American car owners include mechanical problem alerts (82% of owners surveyed), validating the accuracy of repair/service information (77%), real-time car issue diagnosis (70%), and personalized reminders for insurance registration renewal or other events (68%). |
AI in transportation & logistics
| 70% of transportation and logistics companies surveyed reported adopting AI solutions. The most beneficial use cases include fleet planning optimization (cited by 36% of fleet executives) and route optimization (35%). | |
|---|---|
| Adopting AI systems for daily route optimization can help reduce driver travel times by 15%, ensuring significant productivity gains for logistics companies. | |
| Autonomous trucking technology is expected to reduce the TCO of heavy-duty trucks by 42%. | |
| Autonomous truckload deliveries cost an average of 0.03 cents per ton mile, compared to 0.07 cents per ton mile for deliveries with human drivers. | |
| AI-powered predictive maintenance and AI-driven crew planning and shift planning can help railway companies reduce overall maintenance costs by 20% and optimize shifts by up to 15%, respectively. |
High adoption, high impact
Low adoption, high impact
High adoption, low impact
Low adoption, low impact
Scheme title: Top GenAI use cases in transportation by adoption and business value
Data source: Deloitte
AI in IT & cybersecurity
| The most popular use cases of AI in the development workflow include writing code (reported by 82% of developers surveyed), searching for answers (67.5%), and debugging (56.7%). | |
|---|---|
| The most popular use cases of AI in cybersecurity are anomaly detection (cited by 56.9% of professionals surveyed), malware detection (50.5%), and automated incident response (48.9%). | |
| The cybersecurity areas where defensive AI is expected to impact the most include detecting new or unknown threats (according to 56.9 of professionals surveyed), detecting threats as a whole (55.3%), and autonomously responding to threats (43%). | |
| Top GenAI use cases for cybersecurity include rule creation (cited by 21% of respondents), cyberattack simulation (19%), and network detection (16%). |
AI in nonprofit
Benefits of using AI
The impact of AI on revenue is greatest when adopted for strategic tasks like financial decision-making, while it shows its maximum cost-saving potential once incorporated into administrative processes.
| The most common benefits from AI use include improvements in innovation (cited by 64% of companies), employee satisfaction (45%), and customer satisfaction (45%). | |
|---|---|
| Adopting AI across business use cases could translate into $4.4 trillion in productivity growth. | |
| Organizations reported the greatest revenue increase (>10%) when adopting AI in strategy and corporate finance (12% of companies surveyed), marketing and sales (10%), and product or service development (10%). | |
| Organizations that have conducted AI pilots, achieved limited AI implementation, or scaled AI use cases across various business functions reported an average ROI of 1.7x. | |
| Cost savings from AI vary depending on business functions. Companies can achieve cost reductions of over 30% in functions involving rule-based, repetitive tasks, such as accounting and personnel management, since these activities can be easily automated with AI. On the other hand, in functions focusing on human interactions such as customer operations, cost savings average 27%. |
Scheme title: Cost decrease from AI use by business unit
Data source: McKinsey
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AI adoption best practices
To facilitate AI implementation, companies can apply a variety of best practices, including prioritizing high-impact use cases, ensuring the availability of high-quality data, and selecting a suitable tech stack.
| Companies that follow a range of AI best practices typically see larger returns from their AI initiatives. For instance, 60% of high performers have clearly defined an AI roadmap with specific AI initiatives and use cases in high-priority business areas, compared to only 31% of all other respondents. | |
|---|---|
| 65% of CEOs surveyed say their organization is prioritizing AI use cases based on ROI, with 68% reporting that their company has clear metrics to measure innovation ROI. | |
| 57% of organizations believe their data isn't AI-ready. To address this, companies can adopt data management practices and capabilities ensuring that their datasets are suitable for specific AI use cases. | |
| 46% of executives have begun adopting open-source AI models from non-US/EU providers, including DeepSeek's machine learning models from China and Falcon LLM from UAE. However, their usage is typically limited to use cases that require minimal investment and involve integrating the model into edge devices like smartphones. This allows companies to benefit from the cost-effectiveness of open-source models while mitigating associated risks. |
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Choosing & capitalizing on the right AI use case
With ongoing advances in AI technology, including the rise of deep learning and neural networks, the range of AI capabilities has further expanded, unlocking more and more use cases to address virtually any real-world business scenario. Companies can now leverage AI agents to fully automate time-consuming tasks like data entry, assist their workforce with copilots, serve customers 24/7 with chatbots powered by natural language processing (NLP), and much more.
At the same time, this wide variety of AI use cases can make it difficult to identify one that is worth investing in. Itransition’s team can help you find suitable use cases based on your organization’s goals, pain points, and AI readiness, as well as build powerful AI solutions for the fields of application selected.
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