What is agentic AI, anyway?

Business value in practice, not just theory

Many companies are already using agentic AI to drive business efficiencies and get the edge on innovation. Bosch, for example, has deployed AI-driven customer service agents capable of dynamically handling inquiries, allowing human representatives to focus on more complex issues. This implementation has significantly reduced response times and improved customer satisfaction.

In the manufacturing sector, companies like Schaeffler use AI agents to monitor production lines, detect defects in real time, and recommend corrective actions. This proactive approach minimizes downtime and enhances overall productivity. Similarly, Uber has integrated agentic AI into its software development and data processing workflows, automating query generation and reducing the time required for data analysis by 140,000 hours per month.

In finance, banks are deploying AI-driven fraud detection systems that autonomously analyze transactions and flag suspicious activity. These systems continuously refine their models based on emerging fraud patterns, reducing false positives and enhancing security.

Healthcare providers are integrating agentic AI into patient care workflows, where it assists in diagnosing conditions, recommending treatments, and optimizing hospital resource allocation. By analyzing patient histories and medical research, these AI systems help doctors make more informed decisions while streamlining administrative processes. Agentic AI is also starting to play a role in personalized medicine by tailoring treatment plans to individual patients based on genetic data and past medical history.

Tech companies that build agentic AI

Agentic AI relies on layers to function effectively because no single system can handle everything on its own. It has three distinct layers, which different tech companies are leading in right now and who you could work with to implement agentic AI:

• The foundational layer: Where AI gets its intelligence — it includes the advanced models (like those from OpenAI and Anthropic) and the computing power (from NVIDIA, AMD and Intel) that enable AI to process and generate information
• Orchestration layer: Ensures these AI agents operate smoothly, integrating them into workflows, managing their interactions and optimizing their performance — companies like UiPath specialize in this.
• Application layer: This is where Peak specializes. This layer is how agentic AI becomes a rapidly value-adding solution, powering real-world use cases like automating business actions, optimizing pricing or improving supply chain decisions.

Co:Driver™: A game-changer, built on agentic AI

Peak has been delivering AI long before people were prompting ChatGPT for pictures and poems. Over the years, we have built predictive AI solutions for retailers, manufacturers and more — giving them their own AI which has helped them maximize margins, hit target service levels and free up capital tied up in inventories. In late 2023, we introduced Co:Driver™, our agentic AI assistant.

Co:Driver™ seamlessly integrates predictive AI and agentic AI. Users can ask questions in natural language, and Co:Driver™ will retrieve relevant insights generated through predictive AI, synthesize insights and present findings in an accessible format. Whether it’s querying historical sales trends, identifying bottlenecks in supply chains, or evaluating customer purchasing behavior, Co:Driver™ eliminates the need for technical expertise, making AI-driven decision making more accessible to all employees.

Co:Driver™ provides users with real-time visibility into how their AI models function, offering clear explanations of data processing, decision making and output generation. This level of transparency helps businesses build trust in AI-driven recommendations and ensures that users can make informed decisions quickly.

Whether it’s analyzing sales performance, forecasting inventory needs or assessing pricing strategies, Co:Driver™ breaks down complex AI operations into understandable insights, reducing the knowledge gap between AI and business users.

Co:Driver™ can break down complex objectives into structured plans. When given a business problem, the AI agent determines the necessary steps and selects the best tools to execute them. For example, if a business wants to evaluate the profitability of launching a new product, Co:Driver™ can gather competitor insights, analyze past sales performance and generate projections. Instead of simply providing raw data, it processes information holistically and delivers actionable steps for decision-making.

Co:Driver™ is embedded into Peak, allowing it to automate key business functions within its inventory and pricing solutions. Instead of relying on employees to manually analyze market conditions and adjust strategies, Co:Driver™ can monitor data in real time and autonomously make adjustments that align with business goals.

How to get started with agentic AI

Getting started with agentic AI doesn’t have to be overwhelming. The key is to start small, focusing on simplicity and measurable impact. Instead of trying to overhaul entire processes, identify high-value, low-risk opportunities where AI agents can immediately drive efficiency.

Look for tasks that are time consuming, repetitive or require frequent decision making — these are the best starting points for automation. Balancing ROI with ease of implementation ensures that your first AI deployments are both effective and scalable.

Collaboration between AI and human teams is just as important as automation itself. Keeping humans in the loop during the early stages helps build confidence in agentic AI, allowing teams to understand how AI operates and where its insights add the most value.

As trust in AI capabilities grows, businesses can gradually shift towards higher levels of automation. The key is to refine workflows iteratively, testing AI models in controlled environments before fully integrating them into decision-making processes.

Speed of iteration is another essential factor. Agentic AI is evolving rapidly, and businesses that adapt quickly will gain the greatest advantage. Piloting solutions, gathering feedback and making continuous refinements are the best ways to ensure AI agents deliver reliable results.

Finally, fostering a culture of learning is crucial. The agentic AI space is developing at an unprecedented pace, and businesses that embrace ongoing education, experimentation and knowledge sharing will be best positioned to leverage these technologies. Encouraging teams to explore AI capabilities, test different approaches and identify new applications will help businesses stay agile in an AI-driven future.

Learn more about agentic AI

If you’re ready to take the next step toward integrating agentic AI into your business, Peak is here to help. Whether you’re exploring your first AI use case or looking to scale it across your business, we can work with you to develop tailored solutions that fit your business needs.

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