The Real Questions Companies Are Asking About AI Agents

The Real Questions Companies Are Asking About AI Agents

The Questions That Reveal the Market Need

The energy in the room was palpable. Not the kind generated by flashy promises, but by focused, urgent inquiry. As our keynote on AI Agents concluded, the wave of hands told a clear story: the market is hungry for AI automation, but stranded without a map.

The questions from leaders of established companies can be summarize in four categories:

• “We hear ‘Agents’ everywhere, but where do we even start in our organization?” This wasn’t about curiosity; it was a plea for a prioritization framework. Companies drowning in potential use cases need a method to identify the low-hanging fruit, those repetitive, low-added-value tasks that drain productivity but offer the highest ROI for automation.
• “How do we ensure this doesn’t become a security or compliance nightmare?” For businesses with legacy systems and sensitive data, this is the non-negotiable barrier. The hype around Agentic AI often glosses over the paramount need for a secured AI infrastructure that integrates with, rather than disrupts, existing controls.
• “Can these Agents actually work with our current platforms and data silos?” This question gets to the heart of practical implementation. It revealed a deep understanding that the real challenge isn’t the AI model itself, but the tailored integration required to make it function within a complex, existing business ecosystem.
• “What does tangible success look like, and how do we measure it?” Beyond the demo, leaders need concrete outcomes. They sought proof that workflow-based AI Agents deliver more than novelty, they deliver measurable efficiency, accuracy, and freed-up human capital for strategic work.

During our keynote at the French Chamber of Commerce in Shanghai, on January 20th, these questions underscore a critical truth: the hunger for AI Agents is real, but it is matched by a profound need for clarity, trust, and a strategic pathway. It’s why generic hype fails. Established companies don’t need another buzzword; they need the keys to a safe, reliable, and integrated implementation.

From Theory to Tangible: The Power of Concrete Examples

You could feel the shift in the room. The moment we moved from abstract concepts to live, working examples, the atmosphere transformed from one of skeptical inquiry to engaged recognition. The questions didn’t stop, but their nature changed from “Can this work?” to “How can we apply this here?”

This pivot happened when we showcased two purpose-built AI Agents designed for immediate business relevance:

• The Data Scientist Agent: We demonstrated how this Agent automates the foundational, yet time-consuming, work of data preparation, cleaning, transforming, and running initial analyses on raw datasets. The audience instantly grasped the value: freeing expert talent from tedious wrangling to focus on high-level interpretation, strategy, and innovation. It wasn’t about replacing data scientists; it was about empowering them with a tireless junior analyst that operates within strict, predefined workflows and security protocols.

• The Research Assistant Agent: Here, we illustrated an Agent synthesizing complex information, from monitoring competitor news to summarizing lengthy regulatory updates. The output was a concise, sourced briefing, delivered on schedule. The takeaway was powerful: automating the synthesis of intelligence to accelerate decision-making and ensure key insights are never buried in an inbox or a report.

These weren’t futuristic fantasies or generic chatbots. They were targeted, workflow-based solutions to universal business pains. By grounding the “Agentic AI” concept in these relatable, functional prototypes, we made the technology approachable and concrete.

The immediate reaction from the audience confirmed it. The follow-up questions became specific and actionable towards each company’s specific needs. The desire evolved from understanding to testing. This is the critical juncture where true transformation begins, not with a lecture, but with a demonstrable, secure proof of concept that speaks directly to a company’s own operational reality.

The Strategic Framework: Our Answer to the “How?”

The concrete examples provided the “what” and the “why.” But the audience’s most pressing need remained the “how.” How do we navigate from this initial spark of understanding to a secure, scalable implementation within the complex fabric of an established company?

This is where we moved from demonstration to delivery, providing every participant with a actionable, strategic framework. This methodology is designed to cut through the hype and replace uncertainty with a clear, phased plan:

1. Identify & Prioritize with Precision: We shared our systematic approach for collaborating with business units to pinpoint the exact processes ripe for automation. The focus is on identifying low-added-value, repetitive tasks, the hidden drains on productivity that offer the highest and safest return for workflow-based AI Agents. This isn’t about chasing the shiniest toy; it’s about targeting the most credible, high-impact opportunities first.

2. Prototype with Safety & Collaboration: The core of our framework emphasizes that you should never deploy in the dark. We advocate for building a secure, controlled “sandbox” environment where specific use cases, like the Data Scientist or Research Assistant, can be prototyped in collaboration with the actual business users. This phase is crucial for validating utility, refining workflows, and, most importantly, ensuring all outputs meet stringent quality and security standards before any live integration.

3. Scale with Confidence & Integration: Once a prototype proves its value and reliability, the framework provides the pathway to scale. This stage focuses on the tailored integration of the proven Agent into your existing tech stack and business processes, ensuring it enhances your legacy systems. This is how a successful pilot transforms into a permanent, trusted component of your operations.

This structured process is how we translate promise into proven success. It embodies our core differentiators: dense, valuable guidance replaces vague hype; specialized, function-specific training ensures relevance; and an unwavering commitment to safe, reliable deployment builds the trust necessary for true transformation. We don’t just give you the keys to the kingdom; we provide the map, the training, and the secure vehicle to explore it.

Conclusion: The Call to Move from Interest to Informed Action

The energy in the room made one thing abundantly clear: curiosity is no longer the bottleneck for AI adoption. The barrier for established companies is the leap from passive interest to informed, secure action.

Our keynote reinforced a principle we hold fundamental: Don’t just trust the hype. Test the technology. The most valuable opinion on AI Agents for your business is not an expert’s, it’s yours, formed through hands-on experience with a tangible prototype that addresses your specific operational reality.

The journey from the buzzword “Agentic AI” to a deployed, value-driving asset doesn’t begin with a massive investment or a risky leap of faith. It begins with a structured, secure exploration. It starts by applying the strategic framework we shared, identifying your priority use case, building a safe prototype, and validating its impact alongside your team.

For the leaders in that room, true empowerment comes from a partner who provides more than promises. It requires mastery through in-depth training, proof through case studies, and confidence through a secured infrastructure.

The interest is universal. The understanding, however, must be earned. Let us help you earn it.

Ready to move from questions to a working prototype?
Contact us to explore a tailored, secure AI Agent pilot for your business function

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At System in Motion, we are on a mission to empower as many knowledge workers as possible. To start or continue your GenAI journey.