Exploring a Strategic Approach to Activate Purposeful AI Capabilities
As businesses increasingly embrace the power of artificial intelligence (AI), a new wave of technology is making waves: agentic AI. This advanced form of AI combines perception, reasoning, action, and continuous learning, promising significant benefits for productivity and operational improvements. However, its implementation comes with a unique set of challenges.
Key Challenges
Technical Complexity
Agentic AI's sophistication requires advanced AI techniques such as large language model orchestration and integration with external tools. This complexity leads to high infrastructure costs and challenges in scaling beyond pilots, resulting in significant delays and cost overruns [1][3].
Integration with Legacy Systems
Enterprises face difficulties retrofitting agentic AI into existing workflows and infrastructure, especially in sectors with long-standing infrastructure like finance and telecom [1][3].
Talent Shortage
A critical shortage of AI-skilled professionals slows deployment, as successful agentic AI requires hybrid teams fluent in both technology and business contexts [3].
Ethical and Bias Concerns
Bias in AI systems can lead to harmful outcomes, such as discriminatory decision-making or misdiagnoses. Over half of leaders express concern about bias, and many implement bias audits and detection tools continuously [3].
Unclear Business Value and Cost Overruns
Many projects fail due to unclear ROI and underestimated costs linked to technical complexity and infrastructure investments. Gartner estimates over 40% of agentic AI projects will be canceled by 2027 for such reasons [1][2].
Workforce Resistance
Resistance to adopting AI agents owing to fears of job displacement or mistrust of autonomous decisions presents an organizational challenge [2].
Risk Management and Regulatory Compliance
Expanding AI capabilities increases risks such as harmful outputs, operational failures, and systemic bias, necessitating proactive adversarial testing and responsible AI governance to protect business continuity and reputation [4].
Best Practices
To overcome these challenges, best practices emphasize starting with intentional pilot projects, establishing strong governance and risk management frameworks, embedding bias detection and human oversight, and adopting a strategic, transparent, and responsible AI approach.
Start with Focused Pilot Projects
Proven pilots help establish workflows, demonstrate value, and allow for incremental scaling rather than rushing into enterprise-wide rollouts [3].
Implement Strong Governance and Risk Management
Establish formal controls, human-in-the-loop oversight, bias audits, and rigorous testing (including adversarial testing and red-teaming) to proactively manage risks and build trust [3][4].
Continuous Bias Training and Transparency
Bias mitigation must be ongoing and transparent, supported by human review and bias detection tools to ensure fairness and accountability [3].
Cross-Disciplinary Hybrid Teams
Combine technical AI expertise with domain and business knowledge to bridge gaps in understanding and execution [3].
Cut Through Hype with Strategic Deployment
Avoid “agent washing” by critically assessing the true autonomous capability of proposed AI agents and focusing on areas with clear efficiency or revenue impact [2].
Invest Adequately in Infrastructure and Talent
Recognize the high upfront investment needed for agentic AI infrastructure and workforce skills development to avoid unexpected overruns and delays [1][3].
Adopt Responsible AI Frameworks
Embed responsible AI principles from the design phase, including data quality controls, ethical considerations, and regulatory compliance to protect against harm and build sustainable value [4].
By addressing these challenges thoughtfully and following these practices, enterprises can increase their chances of successfully, safely, and responsibly scaling agentic AI deployments.
As the landscape evolves beyond agentic AI, responsibility becomes less of a limitation and more of a competitive advantage. Over the next 12 months, 60% of organizations are expected to make AI a top IT priority, and 53% expect to increase budgets for generative AI by up to 25%. Organizations that prioritize ethical AI practices not only mitigate risk but also build trust, drive innovation, and create lasting business value.
For both Copilot and agentic AI, it's crucial to have a robust data strategy that defines how data is responsibly collected, stored, managed, analyzed, and used. Employee and customer experience is only as good as the data that serves as the foundation for those initiatives. Agentic AI is a top technology trend for 2025, as listed by Gartner. Multiple AI "agents" can be trained and fine-tuned for specific domains to do much more. Agentic AI can help deliver on goals of productivity and operational improvements while accelerating a return on investment. Agentic AI goes beyond responding to prompts and requests, enabling decisions, acting as specialized teams, and addressing specific business challenges.
Technology and artificial intelligence (AI) intersect in the form of agentic AI, a sophisticated AI that combines perception, reasoning, action, and continuous learning. However, the implementation of agentic AI presents unique challenges, such as technical complexity, integration with legacy systems, talent shortage, ethical and bias concerns, unclear business value, workforce resistance, risk management, and regulatory compliance. To overcome these challenges, best practices emphasize starting with focused pilot projects, implementing strong governance and risk management, continuous bias training and transparency, cross-disciplinary hybrid teams, strategic deployment, adequate infrastructure and talent investment, and responsible AI frameworks. The fusion of technology and AI, particularly agentic AI, presents opportunities for increased productivity and operational improvements, but also necessitates a thoughtful and comprehensive approach to ensure successful and responsible scaling.
