Agentic AI , collaborative and personalized AI agents, is rapidly transitioning from experimental pilots to enterprise-critical systems. Capgemini forecasts that AI agents could generate $450 billion in economic value by 2028, yet only 2% of organizations have reached scaled deployment maturity. The rest risk losing competitive advantage as adoption accelerates.
Selecting the right AI agent platform is a strategic decision that will shape your operating model for years. The five considerations below synthesize insights from leading companies like Capgemini, Accenture, McKinsey, Bain, and more to guide C-suite and technology leaders toward a choice that maximizes value while mitigating risk.
1. Define the Strategic Value and Readiness for AI Agents
“Scaled AI deployments deliver 6–12 percentage points higher EBIT margins than peers.” McKinsey
Before you compare features or vendors, align AI agent adoption with clear business value and organizational readiness.
Step 1: Quantify the Strategic Value
- Identify priority use cases aligned to your business strategy (e.g., customer service automation, internal document search, RFP processing).
- Develop a value hypothesis with measurable KPIs: cost-to-serve reduction, revenue growth, throughput gains.
- Benchmark performance — Capgemini reports that fully scaled AI agents can deliver 5× greater ROI than pilot deployments.
Step 2: Assess Your Readiness to Capture That Value
- Data: Is your information accurate, accessible, and well-governed?
- Infrastructure: Can your systems support AI workloads securely and at scale?
- Talent: Do you have AI-literate staff capabilities?
- Processes: Can workflows easily integrate AI outputs into decision-making?
Accenture’s research shows only 15 % of companies have the foundational capabilities to truly scale AI. It’s not just about piloting but embedding AI into core strategy.
Without clear ROI targets and the foundational capabilities to deliver on them, AI agent projects risk becoming expensive proofs-of-concept rather than enterprise growth engines.
2. User Friendliness
When evaluating an AI agent platform, leaders should probe the following:
- Flexibility: Can the platform support the creation of different AI agent types, from simple task automation to complex multiple AI agent workflows?
“The next step is building a network of AI agents with different purposes, ranks and roles … the true magic [is] when multiple AI agents work together in harmony, autonomously enhancing quality, productivity, and cost-efficiency.” Accenture
- Accessibility: What level of technical expertise is required to start building AI Agents and deploying them?
- Onboarding Experience: How seamless and intuitive is the initial setup process for new users?
- Usability: Are tools and settings easy to locate and configure, or will users face unnecessary complexity?
- Documentation & Support: Does the platform provide comprehensive, well-structured resources to guide and optimizatise AI agents?
3. Governance, Security, and Ethical AI
"Organisations with more mature AI governance frameworks report a 28% increase in staff using AI solutions and experience nearly 5% percent higher revenue growth” Deloitte
Non-negotiable governance elements:
- Explainability & AI agent’s thought trails for all agent decisions.
- Regulatory compliance with GDPR, EU AI Act, and industry-specific mandates.
- Human-in-the-loop control for high-risk decision points. Human intervention allows for a close collaboration between AI and humans, harnessing the strengths of each to achieve superior results
Bain & Company underscores that strong governance not only mitigates risk — it actively accelerates adoption by building trust with employees, regulators, and customers.
4. Integration Capabilities / Interoperability of AI Agents
AI agents will not operate in isolation. Their value depends on how seamlessly they interact with your existing business systems to enable accurate, timely decision-making.
This makes integration capabilities a critical evaluation criterion when selecting a platform.
Modern platforms should support:
- Multi-agent orchestration: agents that share memory, context, and work towards a coordinated goal.
- Interoperability: ability to integrate with existing enterprise systems and third-party AI tools.
- Modularity: flexibility to adapt, upgrade, or extend capabilities without lock-in.
- Provide agents with secure read/write access to your internal databases, ensuring they can both consume and update the data that drives your operations.
5. Scalability
Your immediate priority may be to deploy AI agents for a particular high impact use case. But business evolve and tomorrow, you may require those same agents to operate as full-time virtual assistants, triaging support tickets, escalating critical issues, and even troubleshooting complex problems.
If your platform is rigid, scaling to meet these new demands will be slow, costly, or even impossible. That’s why scalability must be a core selection criterion. Assess:
- Capacity: Can the platform seamlessly support higher usage volumes or additional agents without disruption?
- Cost Dynamics: How will your operating costs change as usage scales?
- Limits: Are there hard caps on the number of agents, transactions, or concurrent processes the platform can handle?
- Performance & Analytics: Ensure the platform provides real-time performance dashboards and historical analytics, allowing you to track KPIs, monitor adoption, and continuously improve agent performance as you scale.
Conclusion
Selecting an AI agent builder platform is not simply an IT procurement exercise — it is a strategic transformation decision. The right choice aligns business value with organizational readiness, governance, orchestration, and adoption design.
The Konverso Decision Framework:
- Define the value hypothesis and assess readiness to start your journey.
- Ensure the platform is user friendly, even for non-technical users.
- Implement data governance and trust.
- Ensure orchestration and integration fit.
- Design for scalability.
As Capgemini’s value projections indicate, the winners in the AI agent era will be those who deploy deliberately, scale confidently, and integrate seamlessly into the human workflows that drive enterprise success. For executives, the decision is no longer if AI agents should be deployed, but which platform can deliver measurable business value without creating technical or operational debt.
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https://www.capgemini.com/insights/research-library/ai-agents/ https://www.accenture.com/us-en/insights/data-ai/front-runners-guide-scaling-ai https://www.mckinsey.com/~/media/mckinsey/featured%20insights/artificial%20intelligence/notes%20from%20the%20frontier%20modeling%20the%20impact%20of%20ai%20on%20the%20world%20economy/mgi-notes-from-the-ai-frontier-modeling-the-impact-of-ai-on-the-world-economy-september-2018.ashx https://www.accenture.com/us-en/insights/data-ai/hive-mind-harnessing-power-ai-agents https://www.deloitte.com/cn/en/about/press-room/apac-trustworthy-ai-report.htm https://www.bain.com/insights/are-you-organized-to-reap-value-from-generative-ai/
