Unlocking AIs Potential Key Takeaways from our Virtual Event

AI is no longer just a buzzword. From smarter decision-making to streamlined customer service and product innovation, AI is transforming how organizations operate. In our recent webinar, Making AI Work For Your Business, panelists shared actionable insights and use cases to help companies move beyond the hype and harness AI responsibly and effectively.

Featuring perspectives from Andrew Cron of Blackstone and Citrin Cooperman’s Zach Saltzman, Derek Nachimow, and Steven Caione, below is a breakdown of the most compelling themes from the discussion.

Beyond the Buzz: Understanding the Full Spectrum of AI

Andrew Cron opened the conversation by reframing the landscape: Generative AI (Gen AI) may be grabbing headlines, but it’s just one piece of a much larger puzzle. Traditional AI tools like predictive analytics, machine learning, and automation continue to deliver substantial value.

Cron’s advice:

"Focus on what problems you’re trying to solve. Gen AI might bring new capabilities, but the core mission remains — drive business value.”

Step Zero: Data Readiness Is Non-Negotiable

Derek Nachimow emphasized that no AI initiative succeeds without a strong data foundation. Whether you’re using Gen AI or predictive models, your data must be accessible, trustworthy, and well-organized.

Ask yourself:

Where is our data stored?
Is it structured for AI access?
Do we have governance policies in place?
Can we rely on its consistency and availability?

Andrew Cron added that Gen AI broadens the definition of “data” to include emails, documents, and messages — making governance even more critical.

Quick Wins with AI: Low-Barrier Use Cases to Get Started

The panel shared practical entry points for organizations eager to experiment with AI without overhauling their infrastructure:

The following are a few low-barrier AI use cases that were cited:

Email routing automation (such as shared inboxes for customer service)
Form intake and lead qualification
Summarizing meeting transcripts or physician notes
Using Copilot in Microsoft Power BI for natural language querying

These applications don’t require massive datasets or complex infrastructure and can be implemented with minimal disruption.

Cron’s tip:

“Start with a ‘thin slice' of a real business challenge. Sove for that with AI, then expand.”

Microsoft Fabric and Copilot: Making AI Accessible

One standout moment from the webinar was the introduction of Microsoft Fabric, an all-in-one platform for data ingestion, transformation, modeling, reporting, and machine learning – making AI tools more accessible.

Steven Caione and Derek Nachimow highlighted how Copilot in Power BI now supports lower-capacity environments, allowing business users to ask data questions in plain English—no data science degree required.

One of the most compelling sections of the webinar introduced Microsoft Fabric, which is described as an all-in-one data and analytics platform. It combines ingestion, transformation, modeling, reporting, and even machine learning, democratizing access to AI tools.

Steven Caione and Derek Nachimow explained how Microsoft’s Copilot in Power BI now supports lower-capacity environments, making it more accessible. Business users can now ask data questions in plain English.

Caione’s insight:

“It’s not about needing a data scientist; it’s about empowering every employee to extract value from your data.”

Building Trust: The Human Side of AI Adoption

AI adoption hinges on trust and leadership, and can stall without buy-in. To avoid this, the panel emphasized top-down leadership and incremental trust-building.

Tips for driving internal adoption:

Link AI projects to specific business goals.
Avoid boiling the ocean; start with one or two high-impact use cases.
Focus on transparency and observability in early rollouts.
Give employees hands-on experience to build confidence.

Cron’s perspective:

“Frame AI as an enabler — not a threat. It’s here to make work easier.”

Governance: The Backbone of AI Success

Governance may not be glamorous, but it’s essential. Responsible AI means monitoring performance, preventing bias, and protecting data.

Two key layers:

Model Governance: Track model accuracy and performance over time.
Behavioral Governance: Monitor how automated decisions are made and ensure they meet business and ethical standards.

Cron’s analogy:

“If you wouldn't let a human team operate without oversight, don't let AI do it either.”

Defining Your AI Vision: Prioritize What Matters

To help organizations chart their AI journey, Derek Nachimow shared a simple yet powerful framework:

Use Case Prioritization Quadrant:

X-axis: Complexity (Low à High)
Y-axis: Value (High à Low)

Start with high-value, low-complexity use cases to generate quick ROI and build internal momentum for broader adoption.

Start Your AI Journey with Citrin Cooperman

This recap just scratches the surface of a dynamic conversation about unlocking real, responsible, and sustainable value from AI. Reach out to our AI professionals for a full discussion and actionable advice.

Ready to take the next step in your AI journey? Contact Citrin Cooperman’s Digital Services Practice to get started. Our Cloud Solution Provider (CSP) services are designed to help you identify high-value AI use cases, assess your data readiness, and implement the right solutions to drive fast, measurable results.