Our 2026 State of Business-Driven Technology Report explores what AI use cases are emerging as the most common as well as where AI adoption is headed. More organizations are branching out from low-hanging, easy-to-implement technology like conversational AI and experimenting with more complex and autonomous AI technologies like agentic AI workflows. Read our comprehensive guide of commonly implemented AI technologies and real world use cases.
1. Conversational AI
Conversational AI technologies leverage data and natural language processing to imitate human interactions. Examples include customer service chatbots and virtual assistants or agents. Conversational AI technology can help streamline customer interactions and save on costs.
Real world use case:
Amazon’s AI-powered customer service chatbot helps resolve order issues
2. AI-Enhanced Software Development
AI-Enhanced Software Development involves tools that promote efficiency and productivity in software development practices, from generating code to automating quality assurance testing. This technology helps deliver development projects and new features faster and more accurately.
Real world use case:
GitHub Copilot uses AI to help developers code faster
3. Business Process Automation
Business Process Automation allows organizations to automate repetitive manual processes and business tasks. Accounting processes, financial reporting, mundane data entry, and similar tasks can be executed by a robot and save valuable time and resources.
Real world use case:
Siemens uses RPA to process large volumes of invoices
4. Predictive Analytics & Decision Making
AI in Predictive Analytics leverages machine learning algorithms to analyze large volumes of data to make business predictions and forecasts. From predicting customer demand to predicting mechanical maintenance needs, this technology can be an impactful tools in making sound business decisions.
Real world use case:
Kaiser Permanente uses predictive models to reduce hospital readmissions
5. Agentic AI Workflows
Agentic AI has the capability to function independently. This AI technology consists of machine learning models that replicate complex, human-like problem solving. Requiring little supervision, AI agents have the capacity to tackle multi-step problems and are well suited for streamlining financial processes, cybersecurity strategies, IT support, and more.
Real world use case:
Ramp uses AI Agents to automate expense management and financial workflows
6. Physical AI & Industrial Automation
Physical AI and Industrial Automation refers to the physical machinery, equipment, or systems that interact with objects. For example, a factory may use AI to automate machinery and sensors working in an assembly line.
Real world use case:
Intuitive Surgical enables precision robotic surgery
About Our 2026 Business-Driven Technology Report: AI Business Reality Check
As organizations shift from experimenting with AI’s capabilities to full operational deployment, our 2026 report taps into the most respected research from the last year to dive into what factors are driving this evolution, what persistent implementation challenges stand in the way, and where businesses are finding success.
Read The Report