It鈥檚 not just about productivity. AI agents can help you deliver tech value.
Pressure is mounting for AI to deliver more results. Seventy-five percent of attendees polled at a recent Wall Street Journal CIO Summit said they believe AI is currently driving a small amount of value, but not enough for their investment.1 CIOs are hearing from their boards and CEOs, 鈥�We need transformative value, not just incremental efficiency gains.鈥�
AI agents could be the answer.
Agentic AI systems are evolving faster than any technology in recent memory. They promise more than just productivity boosts鈥攖hey offer genuine business gains. But to achieve this, organizations require a robust data infrastructure, clear ROI metrics, security, and trust, along with a forward-looking strategy.
AI agents evolve from simple, passive information retrieval systems to proactive entities that execute processes without being prompted.
More advanced agents can possess high levels of autonomy, enabling real-time adaptation and optimization in response to environmental changes.虏
Moving beyond single-function task automation, AI agents can collaborate independently to handle complex, multi-step tasks at scale.
AI agents can operate seamlessly across different domains or functions, demonstrating their broad and versatile applicability.
For the past two decades, IT organizations have optimized operating models鈥攖echnologies, processes, people, performance metrics, and security frameworks鈥攁round SaaS models. However, AI agents are now positioning themselves to redefine these fundamentals completely.
For many, transitioning to AI agentic systems will indeed be an evolutionary process. This article outlines actionable strategies to help prepare your tech environment to harness the full potential of AI agents.
Where to begin?
The disruptive potential of AI agents demands an immediate focus on the fundamentals. Preparation is crucial to prepare your tech environment to leverage the value AI agents offer.
Phase 1 | Measure your AI readiness
Phase 2 | Conduct a holistic opportunity assessment
Phase 3 | Implement an AI operating model
To integrate AI agents and maximize their transformative potential, you must establish a robust operating model:
1
Issue Statement:聽How can retail businesses optimize their operations and enhance customer satisfaction through AI agents?
2
Issue Statement: How can healthcare providers improve patient care and operational efficiency with AI agents?聽聽
3
Issue Statement: How can financial institutions strengthen security, improve decision-making, and drive compliance with AI agents?
In the next 12 to 18 months, AI agentic systems will expand their capabilities even further. Organizations that prepare now鈥攁ligning IT strategy, data governance, security, and talent鈥攚ill be positioned to capitalize on these upcoming breakthroughs.
To ensure your business objectives are driving the right tech strategy, and to prepare your environment and infrastructure for AI agents, the time to act is now. By embracing these strategies, CIOs can manage capability demands holistically, maximize tech investment ROI, and realize operational efficiency, cost predictability, and enhanced customer experiences.
Align your strategy now to stay ahead. The transformative wave of AI agents is happening now鈥攄on鈥檛 be left behind.
Build data products聽that rely on agile management systems, elevated data quality, and solid operational foundations. We鈥檒l help you establish federated data ownership practices and data models optimized for specific domains and lines of business.
Anticipate and adapt聽to the wide-ranging impacts AI can have on your data and organization, including budgets and data controls, secure data practices, and cloud-native architectures.
Harness the power of data聽ethically and responsibly with trusted data principles and governance models for managing risk.
Create a consumer lifecycle approach聽that incorporates self-service models, AI assistants and agents, and builds a foundation for enterprise insights.
Operate and manage your data infrastructure聽with integrated frameworks that support access to a broad range of data sources and make analytics faster with less friction.
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