Custom AI Agents: How Copilots Win Back Consultants
The rise of custom AI agents, also known as copilots, represents a significant shift in the consulting industry. In the recent SAP experiment, AI systems demonstrated remarkable accuracy, raising questions about the role and trust in AI systems within professional settings. This narrative leads to a broader understanding of how custom AI agents can be leveraged to improve efficiency, reduce clerical work, and build trust among consultants, thereby enhancing their productivity and focus on strategic outcomes.
Introduction: Why the 95% Experiment Matters for Consultants
In a revealing internal test by SAP, it was shown that consultants initially undervalue the output from AI tools due to skepticism. This experiment underscored the importance of trust in AI, illustrating that AI agents, once misunderstood, can actually deliver highly accurate results—95% in this case. The experiment's insights provide a basis for integrating custom AI agents into workflows, transforming how consultants operate.
Quick recap of the SAP Joule experiment
SAP conducted an experiment using its Joule AI co-pilot to process over a thousand business requirements typically handled by junior interns. When unaware the results were AI-generated, consultants appraised the work just as favorably as when they believed it was humanly done.
What “95% accurate — until it’s AI” reveals about trust
This reveals a critical finding: awareness of AI can skew perception and reception due to inherent biases against machine capabilities compared to human execution. Establishing reliability through proven accuracy can shift the trust equilibrium, preparing grounds for more AI implementation.
What Custom AI Agents (Copilots) Do for Consultants
Custom AI agents are not designed to replace consultants but to augment their capabilities. By handling routine clerical work, these agents free consultants to focus on providing in-depth insights and creative solutions.
From clerical lift to detailed insights
Custom AI agents handle data analysis and clerical tasks efficiently, allowing consultants to focus on more substantive tasks such as crafting strategic recommendations and engaging deeply with client needs.
Examples of copilots in consulting workflows
Consultants can use AI copilots for tasks like synthesizing data into coherent insights, automating report generation, and even predicting business trends, thus ensuring that human input is spent on higher-value activities.
AI Agent Development: Building Copilots That Consultants Trust
The creation of AI copilots that consultants can trust involves careful engineering and specialization. These agents must be tailored to fit the particular needs of the industry and the individual firm.
Prompt engineering and role specification
Effective AI agents are developed through prompt engineering, which involves clearly defining the tasks and roles of the AI. This ensures they understand customer expectations and deliver precise outputs.
Personalization and domain specialization (e.g., SAP S/4HANA)
Personalized agents can be developed to specialize in domains such as SAP S/4HANA, providing tailored insights and solutions that are industry-specific and immensely valuable.
Integrating Agents into Enterprise Workflows
Successful integration of AI agents requires a careful approach to ensure they fit seamlessly within existing enterprise systems.
Connecting to ERP/CRM/data sources
AI agents must be capable of interfacing with core systems like ERP and CRM to facilitate real-time data analysis and reporting, ensuring they add value by enhancing existing data flows and decision-making processes.
Deployment models: cloud, hybrid, on-prem
Choosing the right deployment model—whether on the cloud, hybrid, or on-prem—is essential for aligning with a company’s digital infrastructure and strategic objectives.
Governance, Oversight, and Measuring Accuracy
As AI plays a larger role, implementing governance frameworks ensures that the deployment supports transparency, accountability, and continuous improvement.
Human-in-the-loop validation best practices
Human oversight remains essential in verifying AI outputs. Implementing a human-in-the-loop system guarantees quality and trust in AI-driven conclusions.
Metrics: accuracy, trust, time-savings, ROI
To ensure success, it is crucial to measure key performance indicators such as accuracy, trust levels, time saved, and ROI.
Adoption and Training: Bringing Seniors and Juniors Together
Training and adoption plans can bridge the gap between senior and junior consultants, fostering an environment of shared learning and adaptation to AI tools.
Upskilling, workshops, and mentoring with copilots
Structured training programs that include workshops and mentoring increase adoption rates and boost consultants' confidence in utilizing AI effectively.
Change management checklist for consulting teams
A comprehensive change management strategy helps navigate the cultural shift toward AI integration.
Looking Ahead: Agentic AI and the Consultant of 2030
As we advance, AI is expected to take on more autonomous roles, paving the way for agentic AI that functions beyond prompts by interpreting entire processes.
From prompts to process interpretation and autonomous agents
Moving from simple prompts to complex process interpretation will allow AI to autonomously enhance workflows and business outcomes.
How Encorp.ai can help pilot agentic solutions
To explore these advancements, learn more about our Custom AI Integration to see how Encorp.ai can support enterprise-level adoption of innovative AI solutions.
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Conclusion
Custom AI agents offer immense potential to revolutionize the consulting landscape by enhancing productivity and allowing consultants to focus on strategic tasks. Trust-building, effective integration, and comprehensive training are key factors for successful AI adoption.
External sources:
- Gartner on AI in Business (entry point: Information Technology section): https://www.gartner.com/en/information-technology
- Forbes coverage related to AI and trust in business and technology (Forbes Technology / AI): https://www.forbes.com/ai
- Harvard Business Review on AI and its management/strategy implications: https://hbr.org/topic/artificial-intelligence
- MIT Sloan Management Review on AI strategies and organizational adoption: https://sloanreview.mit.edu/tag/artificial-intelligence
- McKinsey & Company insights on AI and digital transformation: https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai
Martin Kuvandzhiev
CEO and Founder of Encorp.io with expertise in AI and business transformation