Generative AI & Applied AI Solutions cover the design and deployment of AI that creates content, automates decisions, and takes action inside real business workflows — not experimental chatbots on the side, but AI embedded directly into how work gets done. Generative AI has moved from pilot projects to production infrastructure: the large majority of enterprises are now expected to be running generative AI in production, with AI-enabled operations reporting productivity gains as high as 40%.
- Content and document generation — drafting marketing copy, reports, contracts, and product content, connected to approved knowledge sources and reviewed by humans.
- Enterprise knowledge systems (RAG) — retrieval-augmented generation and internal copilots that let employees query policies, manuals, or dashboards in plain language and get answers grounded in actual company data.
- AI-assisted software development — coding assistants that draft, review, and accelerate development, reshaping the software delivery lifecycle.
- Customer experience AI — conversational assistants and support automation embedded at every customer touchpoint, not just a single chatbot channel.
- Agentic AI — systems that reason across data sources, plan multi-step actions, and execute workflows with limited human intervention, moving beyond content generation into autonomous task completion.
- Domain-specific (vertical) AI models — models trained specifically for industries like healthcare, legal, or finance, increasingly outperforming generic, horizontal AI tools.
- Significant productivity gains — organizations embedding generative AI into operations commonly report productivity improvements of up to 40%.
- Faster customer service — companies using generative AI for customer service report resolution time reductions of up to 60%, alongside improved satisfaction scores.
- Faster content and campaign delivery — generative AI in marketing and content production has enabled campaigns to launch roughly 3x faster, with meaningfully lower production costs.
- Real-time decision support — AI systems that analyze situations, predict outcomes, and adjust processes continuously, rather than relying on periodic reporting.
- Scalable automation — a growing share of organizations are already scaling agentic AI in at least one business function, with many more actively experimenting.
- Competitive differentiation — businesses moving from isolated pilots to enterprise-wide deployment are capturing measurably more value than those still experimenting.
- Agentic AI in production — the most significant architectural shift underway is the move from standalone generative models to agentic systems that take real action, with a substantial share of enterprise applications expected to include AI agent capabilities.
- Vertical over horizontal AI — industry-specific models are gaining ground over generic, one-size-fits-all AI, delivering sharper accuracy for specialized use cases.
- Enterprise knowledge management as an AI-first function — internal knowledge systems are being redesigned around AI from the ground up, rather than retrofitting AI onto legacy search and documentation.
- Voice and multimodal interfaces — natural, conversational interaction combining voice, text, and visual input is replacing rigid, command-based interfaces in enterprise applications.
- Data governance and AI ethics as differentiators — organizations are building governance and human oversight into AI systems from the start of deployment, not retrofitting it afterward, and treating this as a competitive advantage.
- The AI skills gap as the key scaling barrier — as adoption accelerates, the ability to find and retain people who can design, govern, and operate these systems is emerging as the primary constraint on scaling further.