How to Make Enterprise AI Work: Strategy & ROI

Feb 15, 2026

The integration of Artificial Intelligence (AI) into enterprises has become a pressing priority for businesses wanting to stay relevant and competitive. Yet, as many companies are discovering, simply deploying AI is not enough. Without clear strategies, AI initiatives can falter, leading to failed projects, wasted resources, and significant risks. In this transformative article, we’ll explore actionable strategies for mid-to-large enterprises looking to establish AI as a driving force for business transformation while ensuring measurable ROI.

This article draws insights from a discussion featuring Tim Piamanti, president and co-founder of Tribeca Softe, alongside AI expert Anker Patel. Together, they break down the critical elements required to make AI a success. Whether you're a business leader, CTO, or operational decision-maker, this guide will help you unlock AI's full potential.

The Key to AI Success Lies in Strategy, Not Chaos

One of the core themes of the discussion was that many enterprises rush to adopt AI without putting the proper processes in place. This chaotic approach often results in fragmented efforts, shadow IT, and low-impact initiatives. Tim Piamanti emphasizes the importance of aligning AI with an overarching business strategy that incorporates people, processes, and platforms.

Here’s the underlying principle: AI success isn’t just about deploying cutting-edge tools; it’s about ensuring that the organization is prepared to integrate and scale AI effectively across its operations.

Understanding the Three Pillars of Enterprise AI

Tim Piamanti's approach to AI in the enterprise revolves around three critical pillars: People, Processes, and Platforms. Let’s break down why each is essential.

1. People: Building AI-Ready Teams

AI initiatives begin and end with people. Enterprises need to assess whether their teams have the necessary skills and understanding to implement and manage AI effectively.

Skill Gaps: Many organizations face a knowledge gap in AI fluency. Educating and equipping teams with the right skills is a foundational step.
Executive Alignment: Cross-departmental buy-in is essential. AI impacts the entire organization, from the Chief Information Security Officer (CISO) to department heads. Without alignment, enterprises risk internal friction.
Employee Empowerment: AI should augment, not replace, employees. For mid-tier organizations, particularly, AI can enable smaller teams to achieve outsized results without increasing headcount, allowing employees to focus on higher-value tasks.

"If you miss addressing your team’s readiness for AI, you’re setting yourself up for failure", says Piamanti.

2. Processes: Laying the Groundwork with Strategy

Rushing into AI deployment without a roadmap is a recipe for disaster. Enterprises need to develop a structured strategy that begins with identifying pain points and prioritizing use cases.

Start with Quick Wins: Small, low-risk projects that demonstrate ROI can build momentum and trust across the organization.
Map Interdependencies: For more complex use cases, such as international wire transfers or end-to-end automation, enterprises must carefully model interdependencies between systems and processes.
Validation Frameworks: Tim recommends a "Validate the Value" (V2V) approach, where enterprises track ROI, efficiency gains, and customer value on a quarterly basis.

"Quick wins build momentum, but they also give you the data to prove ROI, which is key to scaling AI initiatives", Piamanti explains.

3. Platforms: Choosing the Right AI Infrastructure

Technology is the enabler of enterprise AI, but selecting the right platform can mean the difference between scalable success and an expensive failure.

Off-the-Shelf vs. Custom Build: For most enterprises, building AI solutions from scratch is inefficient and unsustainable. By the time custom tools are built, they’re often outdated. Instead, partnering with proven platforms that offer security, scalability, and adaptability - like multimodal AI solutions - is a smarter choice.
Security and Compliance: Platforms must integrate robust security measures to address internal and external threats. Features like role-based access control (RBAC) and audit trails are critical, especially in regulated industries like finance and healthcare.
Flexibility for Customization: While foundational platforms are essential, enterprises can build on top of these frameworks to tailor solutions for specific use cases.

Overcoming the Biggest AI Blockers

Tim identifies two primary blockers that prevent enterprises from realizing AI’s full potential:

Internal Friction: Resistance to change - whether from leadership or employees - can derail AI projects. Building trust through education and quick wins is essential to overcoming this resistance.
Shadow IT: When employees adopt AI tools without proper oversight, it creates security vulnerabilities and operational chaos. Enterprises must move quickly to implement AI solutions while maintaining governance and control.

"Shadow IT is chaos disguised as progress", warns Piamanti. "Enterprises must establish guardrails to prevent fragmentation and security risks."

The ROI Playbook for AI

For enterprises looking to justify their AI investment, Piamanti recommends starting with a strong ROI framework. This involves:

Prioritizing Use Cases: Focus on high-impact, low-effort projects that can demonstrate immediate value, such as operational automation or client onboarding.
Measuring Impact: Define clear KPIs - be it cost savings, efficiency, or customer satisfaction - and track them rigorously.
Scaling Strategically: Use initial wins as a springboard to tackle more complex, transformative AI projects.

Practical Advice for Enterprise Leaders

If you’re an enterprise leader starting your AI journey, here are three essential steps:

Educate Your Team: Begin with a knowledge and skills assessment to understand your team’s readiness for AI. Fill any gaps with training and education.
Develop a Roadmap: Create a clear strategy with defined use cases, quick wins, and measurable goals. This will help overcome internal resistance and ensure a smooth rollout.
Prioritize Security and Governance: Involve security teams from day one to ensure compliance, build trust, and avoid costly mistakes.

Key Takeaways

AI Success Requires Strategy: Effective AI adoption hinges on aligning people, processes, and platforms with business objectives.
Start with Quick Wins: Demonstrating ROI through small, low-risk projects builds trust and momentum for larger initiatives.
Educate Employees: Address skill gaps and ensure cross-departmental buy-in for smoother adoption.
Focus on Security: Integrate governance and security measures upfront to ensure compliance and prevent shadow IT issues.
Leverage Proven Platforms: Avoid custom builds when possible; partner with software vendors who offer scalable, secure, and adaptable solutions.
Measure Impact Quarterly: Use frameworks like "Validate the Value" (V2V) to track ROI, identify issues, and refine your approach.

Conclusion

AI has the potential to revolutionize enterprises, but only if it is adopted strategically and securely. By focusing on people, processes, and platforms, leaders can avoid the common pitfalls of AI chaos and deliver measurable, long-term value. Remember, success with AI isn’t just about technology - it's about alignment, education, and execution.

Enterprise AI is no longer optional for businesses looking to stay competitive. By following this playbook, companies can ensure that their AI initiatives drive real impact, scalability, and ROI - turning AI from an abstract concept into a transformative business reality.

Source: "How to Make Enterprise AI Work in 2026 ft. Tim Piemonte" - Ankur Patel @ Multimodal, YouTube, Dec 17, 2025 - https://www.youtube.com/watch?v=y2n_LCsY5Y8