Accelerate growth with strategic AI deployment

Key AI trends and insights

Deploying AI for scalable, strategic impact is quickly shifting from an advantage to a business necessity. Several trends contribute to successful AI adoption, including:

• Companies are increasingly monetizing AI capabilities, shifting from free or low-cost features to consumption-based or platform pricing to recoup research and development costs.
• Experienced AI developers are achieving significantly better outcomes, while nontechnical staff often see productivity decline when building models or agents without the proper expertise.
• Organizations are now investing in dedicated AI leadership roles, such as chief AI officer or AI strategy lead, to guide enterprise-wide AI adoption.
• Organizations are realizing that true success requires data quality and readiness, domain expertise, and effective change management.

AI adoption is a transformational process and often fails when organizational readiness and user adoption are overlooked. In addition, data readiness remains a significant concern, especially in areas such as data cleanliness, structure and centralization. Therefore, breaking AI-driven outcomes into categories helps identify meaningful use cases and return on investment (ROI) opportunities.

In addition, understanding the different AI models can help align solutions to needs.

• Perception AI focuses on sensory inputs such as computer vision and speech-to-text, such as detecting product defects and reading shipping labels.
• Language AI interprets, summarizes and translates language. It is used daily through tools like ChatGPT and Microsoft Copilot.
• Predictive AI represents traditional machine learning (ML) used for forecasting, demand planning and trend prediction.
• Generative AI creates new content, such as text, images, audio and video for automated marketing content, based on natural-language prompts.
• Agentic AI combines multiple AI types to enable autonomous decision making and action taking, often with human-in-the-loop oversight for accuracy, compliance and governance.

Many AI solutions are built using dedicated, layered architecture, though the exact structure may vary depending on the complexity and scope of the implementation. It is crucial to use trusted enterprise knowledge and facts when designing these AI solutions for correct, desired outcomes.

The design process involves the following steps:

• Enterprise data foundation: The foundation shapes model instructions, defines templates and provides critical context. Output quality depends on clean, accurate and governed data; this follows the junk in, junk out principle.
• Design layer: This process uses prompt engineering and contextual setup to teach your business language, jargon, rules and definitions to the AI solution, enabling accurate, tailored outputs.
• Agentic layer: Autonomous agents take actions and make decisions using the design layer’s context, functioning like a subject matter expert within the organization.
• Orchestration layer: This function coordinates multiple agents and workflows, enabling end-to-end process automation across departments or entire business functions.

These layers constantly interact through feedback loops—if an agent lacks context or requirements are not met, it requests additional information to refine the workflow and improve performance.

“There is a full spectrum of AI agents, from simple to highly advanced. Basic agents include generative tools that create text, audio or visuals and retrieval agents that pull information from SharePoint or internal knowledge bases, such as ChatGPT and Copilot,” says Niemoeller. “More advanced action agents can take autonomous steps and automate entire workflows or business processes. Building these interconnected agent ecosystems requires deeper technical expertise for reliability and scalability.”