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AI use cases:
an in-depth trend overview for 2026
August 4, 2026
AI adoption & use cases across business functions
Artificial intelligence has become a critical enabler of task and workflow automation, helping organizations enhance operational efficiency across business functions. By using AI to support data analytics, document processing, and content creation, companies can transform how their teams operate, implementing the technology across customer service, finance, HR, and procurement.
General trends
Global AI adoption has shifted from experimental pilots to full-scale deployments in production environments, with businesses prioritizing generative AI and agentic AI solutions for diverse business functions.
| The percentage of organizations reporting using AI tools in at least one business function reached 88% in 2025. Generative AI adoption stood at 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%). | |
| 74% of companies plan to implement agentic AI within the next two years. |
Scheme title: Share of organizations that have already moved or plan to move 40% or more of their AI
experiments into production
Data source: Deloitte
| 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
By implementing AI-powered solutions, companies can cut down on customer service costs, improve service quality, and shorten issue resolution times. The most effective use cases for AI in customer relationship management include automated call summarization, instant automated responses, and personalized self-service.
| 91% of service and support leaders face pressure from executive leadership to implement AI, indicating a dramatic rise in the need for AI-enabled transformation. | |
|---|---|
| AI adoption in customer service accounted for 61% in 2025, with the telecom sector having the highest AI in customer service adoption rate of 83%. | |
| Nearly 70% of customer service leaders stated that the most positive impact of AI on customer service metrics was reducing issue resolution time. Customer satisfaction was another area where AI adoption had a positive effect, according to 63% of respondents. | |
| 92% of sales professionals who use AI agents claim that AI helps improve prospecting. | |
| Companies can reduce customer service-related operational costs by 22% by adopting generative and agentic AI. These cost savings are driven by automated call summarization, AI-powered chatbots, instant automated responses, early problem detection, and post-contact analytics. | |
| By 2029, 80% of common customer service issues will be resolved via AI agents. |
Scheme title: Top five functions prioritized for agentic AI usage
Data source: BCG
AI in HR
Companies actively deploy predictive analytics, generative AI, and agentic AI solutions to support HR professionals in finding the right talent, detecting talent development opportunities and needs, and streamlining routine tasks.
| 39% of HR departments have already implemented AI, and 7% plan to do so this year. | |
|---|---|
| 34% of European HR teams piloted GenAI solutions for workforce planning and analytics, followed by recruitment, selection, and applicant management (33%). | |
| According to HR professionals, hiring (27%), talent development (17%), and employee experience (14%) are the areas where AI tools and technologies are most common. | |
| 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. | |
| The most common applications of AI in HR include job description drafting and refinement (20%), automated resume parsing and screening (16%), programmatic optimization of job ads (12%), and passive candidate sourcing (12%). | |
| Predictive analytics and artificial intelligence are expected to have the greatest impact on talent acquisition during the next two years. |
Scheme title: AI applications for people analytics tasks
Data source: HR.com
AI in finance & accounting
Finance and accounting teams adopt AI software to streamline data analysis, financial reporting, and audit compliance tasks, which helps reduce operational costs.
| 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. | |
|---|---|
| 98% of accounting professionals say they use AI in 2026. | |
| Nearly 60% of CFOs plan to increase investment in artificial intelligence in finance functions by 10% or more in 2026, while another 24% expect their finance function AI budgets to grow by 4-9%. | |
| 49% of CFOs see great AI potential in data analysis, 45% in growth forecasting, and 41% in dynamic pricing. |
AI in supply chain management
Supply chain teams embrace AI for diverse tasks, from spend optimization to contract renewal management. By automating shipping documents generation and processing, AI-powered tools help reduce documentation lead time by 60% and logistics coordinators’ workload by 20%.
| 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 transform logistics by auto-generating shipping documents and detecting errors, cutting documentation lead time by 60% and reducing 20% of logistics coordinators’ workload. | |
| 72% of supply chain companies are deploying generative AI. | |
| About 75% of firms plan to incorporate generative AI technology into their supply chains, and 80% believe doing so will redefine how they operate. |
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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: AI agent use by industry & business function
Data source: McKinsey
AI in retail & ecommerce
Scheme title: AI use cases & implementation trends in retail
Data source: Deloitte
| 38% of European customers utilize genAI-powered solutions to research goods and services and make purchasing decisions. | |
|---|---|
| According to C-level executives in retail and consumer products organizations, AI is expected to be implemented for content and campaign creation and management (mentioned by over 60% of respondents), followed by personalized responses and trade promotion creation and management (highlighted by 56% of respondents). |
AI in healthcare
| AI use cases in healthcare cover clinical decision support, medical imaging, and workflow optimization, with 61% of medical technology specialists saying they use AI for medical imaging, and 57% of pharmaceutical and biotechnology leaders using AI for drug discovery. | |
|---|---|
| With a revenue share of more than 13%, the robot-assisted surgery segment leads the AI in healthcare market, while fraud detection is predicted to grow at the highest rate between 2026 and 2033. | |
| In the US, doctors who specialize in neurology have the highest AI adoption rate, with almost 65% reporting its use, followed by gastroenterology and internal medicine with an AI adoption rate of over 60%. |
Scheme title: AI value across diverse applications in healthcare
Data source: Deloitte
AI in financial services
| 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%). |
|---|
Scheme title: Top five AI focus areas for financial services institutions
Data source: World Economic Forum
| According to banking executives, top use cases for agentic AI include enhancing fraud detection (56% of leaders surveyed), strengthening security (51%), cutting costs and increasing efficiency (41%), and improving the customer experience (41%). | |
|---|---|
| AI in financial services is primarily implemented for back-office functions, including process automation (79%), data visualization (75%), software engineering (75%), and data and knowledge management (69%). AI-powered customer support is the leading front-office use case with a 74% adoption rate. |
Front office
Scheme title: Front-office AI use cases in the asset & wealth management industry
Data source: Grant Thornton
Middle office
Scheme title: Middle-office applications of AI in the asset & wealth management industry
Data source: Grant Thornton
Back office
Scheme title: Back-office operations where AI is applied in the asset & wealth management industry
Data source: Grant Thornton
| AI is being used in financial planning, reporting, and commercial analysis by over 75% of businesses. |
|---|
Scheme title: Top ten AI use cases in the financial services industry
Data source: NVIDIA
AI in manufacturing
Scheme title: AI deployment in manufacturing by functional area
Data source: Deloitte
Scheme title: Top AI applications in manufacturing
Data source: Fortune Business Insights
| 50% of German manufacturers have already invested in AI, with 47% considering its full-scale implementation. | |
|---|---|
| 73% of manufacturers focus their efforts on automating repetitive tasks, 65% on improving planning and scheduling, and 64% on implementing AI as the main productivity driver. | |
| 81% of manufacturing leaders plan to increase their AI investments over the next three years, and 93% consider intelligent systems to be crucial to America’s industrial advantage. |
Scheme title: Autonomous factory capabilities companies invest in the most
Data source: PwC
AI in automotive
| Automotive OEM executives expect that the share of total revenue attributable to AI will increase from 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%). | |
|---|---|
| Service operations is the business function where travel and logistics companies most frequently employ artificial intelligence (47% of respondents), followed by knowledge management (36%). |
Process automation
Supply chain / logistics optimization
Energy optimization / sustainability
Automated quality inspection
Predictive maintenance
Worker safety monitoring
Robotics
Scheme title: AI use cases in transportation
Data source: Cisco
Increased productivity
Cost reduction
Enhanced sustainability / lower energy usage
Faster decision-making
Improved worker safety
Competitive advantage / innovation
Reduced downtime
Scheme title: Expected gains from AI implementation
Data source: Cisco
| Customer experience (64%), demand forecasting (63%), warehouse management (61%), and inventory management (60%) are among the top areas where AI shows the highest impact, according to supply chain professionals. |
|---|
AI in IT & cybersecurity
| 96% of security leaders admit defensive AI significantly enhances their security capabilities. | |
|---|---|
| The substantial usage of AI in security helped save $1.9 million when compared to firms that didn’t use these technologies. | |
| Nearly one-third of companies that use AI and automation do so throughout the whole cybersecurity lifecycle, which includes prevention, detection, investigation, and response. |
Scheme title: AI applications in the development workflow
Data source: Stack Overflow
AI in nonprofit
| 33% of nonprofits already use AI tools for content marketing, while 24.6% rely on AI to streamline grant writing. |
|---|
Scheme title: AI applications in nonprofit organizations
Data source: Fundraising.AI
| AI is being used or piloted by 55% of nonprofit organizations, with the US having the highest adoption rate of 89%, compared to 44%-48% in other regions. |
|---|
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.
| According to 46% of managers, AI tools help improve employee productivity, with one of the key benefits being reduced administrative workload. | |
|---|---|
| The most common benefits from AI use include improvements in innovation (cited by 64% of companies), employee satisfaction (45%), and customer satisfaction (45%). | |
| 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 the 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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About Itransition
Delivering software development and IT consulting services since 1998
5+ years in AI development and consulting
In-house AI/ML Center of Excellence consolidating AI expertise
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ISO 9001- and ISO 27001-compliant quality and information security management systems
Deloitte, Gartner, Forrester, and Everest Group awards and recognitions
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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 now adopt customer service chatbots for communication automation, AI assistants to support their workforce, and anomaly detection tools powered by AI to ensure cybersecurity resilience, 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 selected application areas.
FAQs
Solutions powered by artificial intelligence like virtual assistants, analytics software, and automation tools enable proactive interventions and personalized medicine in healthcare, accelerate claims processing in insurance, and facilitate risk assessment and fraud detection in finance. In retail and ecommerce, AI models are used for dynamic pricing and social media analytics, while AI-driven recommendation engines help enhance user experience, providing personalized product or content suggestions that match customer needs.
Manufacturers integrate AI-powered analytics into digital twins to simulate and predict future scenarios for production process and supply chain optimization, as well as implement computer vision solutions for quality control and defect detection. In professional services, AI-powered tools that combine optical character recognition (OCR) and natural language processing (NLP) capabilities help quickly process contracts and invoices, minimizing human error and helping ensure regulatory compliance.
Organizations across industries employ AI for lead scoring, as well as predictive AI to forecast future events like product delivery delays or market shifts and their impact on business performance. Additionally, companies adopt solutions with facial recognition, behavior analysis, and real-time threat detection capabilities for security purposes.
Responsible AI principles help guide the design, development, deployment, and use of AI-powered solutions to mitigate risks, such as algorithmic bias, data breaches, lack of transparency into AI model reasoning, and decision accountability gaps. These principles include fairness, reliability and safety, transparency, privacy and security, inclusiveness, and accountability, which help ensure that developers create ethical, trustworthy, and explainable AI systems, ultimately enhancing user trust in AI tools and the organization’s compliance with privacy laws.
At Itransition, we train modern large language models, such as GPT-4-class, Mistral, Llama, Hermes, Phi, and Gemma, when building conversational AI and generative AI solutions like chatbots and virtual agents. We’re also proficient in Python and JavaScript programming languages, deep learning frameworks and libraries such as TensorFlow and PyTorch, computer vision technologies like KerasCV and Stable Diffusion XL, and data mining technologies such as NumPy, Pandas, and scikit-learn. We also work with cloud platforms like AWS, Microsoft Azure, Google Cloud (GCP), and Hugging Face, as well as generative AI platforms like Azure OpenAI and Amazon Bedrock.
The total cost of implementing AI-based solutions varies depending on AI model accuracy requirements, training data volume, quality, and availability, solution development and integration complexity, and the solution’s hosting infrastructure expenses. Typically, basic pilot solutions amount to $10,000-$20,000, while the price for complex enterprise systems with custom interfaces and advanced capabilities can reach $250,000+.
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