AI is now a mainstream tool driving measurable ROI for businesses, making it essential to evaluate AI use cases based on their potential business value.
In our recent webinar, AI That Works: How to Build the Right Data Foundation for Success, industry experts shared practical strategies for scaling AI with confidence and showcased real-world demos of AI agents in action. We also highlighted success stories of AI implementations that have delivered tangible results and measurable benefits.
If you missed the live session, here are the key takeaways:
AI Adoption Is Accelerating
Recent studies show that 74% of enterprises are seeing positive ROI from AI investments, with nearly half of business leaders using AI daily. Generative AI is no longer a novelty; it’s becoming an integrated part of workflows.
However, most organizations are adopting AI but still face challenges in scaling and integration, including AI fatigue, shrinking training budgets, and talent gaps. Addressing these talent gaps and developing new skills across teams is essential for successful AI adoption. Success requires not just technology, but cultural alignment and executive sponsorship.
Why AI Initiatives Fail
Common pitfalls include:
- Proof-of-Concept (POC) Paralysis: Too many pilots without a clear strategy.
- Fragmented Investments: Scattered tools and no unified approach.
- Weak Governance: Shadow IT and lack of oversight, which can jeopardize regulatory compliance and expose organizations to legal and regulatory challenges.
- Talent Gaps: Difficulty finding skilled resources.
Organizations must also be aware of the risks of AI implementation, including technical failures, operational disruptions, and compliance issues. Proactively identifying and addressing these potential risks is crucial for successful AI deployment.
The takeaway? AI success demands a holistic strategy, not random acts of AI.
The Rise of AI Agents
We’re moving from simple chat-based AI to agentic AI: systems that take action, automate workflows, and collaborate across functions, enabled by advanced AI technologies. IDC predicts 1.3 billion AI agents will be deployed globally in the next few years, transforming industries and business processes.
Organizations can realize significant efficiency gains and cost savings by deploying AI agents. However, scaling AI agents across business functions presents both challenges and opportunities, especially as adoption expands in sectors such as technology, media, telecommunications, and healthcare.
A Proven Framework for Success
The webinar introduced a four-step Business Envisioning Framework:
1) Envision Use Cases:
Identify opportunities across productivity, team workflows, and customer-facing services. Align AI initiatives with clear business objectives and business goals, ensuring each use case supports strategic priorities and delivers tangible value. Prioritizing AI use cases should consider strategic fit, ROI, scalability, technical requirements, stakeholder support, and workforce impact.
This step also requires analyzing data, robust data collection, and assessing data readiness and AI readiness to ensure the foundation is in place for successful implementation. Capture valuable insights by considering the complete picture of customer needs, market conditions, and customer experiences.
Integrate support conversations and customer interactions as key data sources, including unstructured data, to uncover hidden patterns and inform AI models.
2) Solution Design:
Define data needs, user experience, and technical complexity. Design for AI-ready data, ensuring data quality and meeting quality standards to support robust AI outcomes. Address the need for technical expertise and leverage advanced technology, including machine learning, large language models, and natural language processing, to enable innovative solutions.
Incorporate diverse perspectives and understand just what drives business decisions for better AI outcomes.
3) Architect & Build:
Choose the right tools—whether no-code, low-code, or pro-code. Build data products and leverage data platforms and data infrastructure to enable scalable AI solutions.
Ensure the architecture supports the integration of AI solutions and the analysis of both structured and unstructured data, including customer interactions, to generate valuable insights.
4) Prototype & Iterate:
Launch quickly, gather feedback, and refine. Use feedback to drive continuous improvement and maximize value from AI projects. Focus on capturing valuable insights and optimizing workflows, including quality control, to drive operational efficiency and business impact.
In the demo, the solutions showcased how AI models, solutions, and projects drive innovation, revenue growth, and impact across areas such as corporate finance. The tools help teams work effectively with AI, transitioning from traditional analytics to advanced, real-time approaches.
By analyzing unstructured data, integrating customer interactions, and including diverse perspectives, organizations can gain valuable insights and understand just what drives business decisions, leading to better AI outcomes and continuous improvement.
Real-World Demo: From No-Code to Pro-Code
Our experts showcased how to:
- Build a no-code Copilot grounded in SharePoint data.
- Scale to low-code solutions using Copilot Studio for workflow automation.
- Optimize with pro-code tools like Azure AI Foundry for advanced capabilities.
The demo illustrated how AI agents can reduce manual effort, streamline processes, and deliver tangible ROI. Start with simple prototypes and evolve them into the enterprise-grade solutions you need.
Ready to Take the Next Step?
AI is no longer bleeding edge: it’s mainstream. The organizations that act now will gain a competitive advantage. Watch the on-demand recording for deeper insights, live demos, and practical guidance on building your AI roadmap.
And don’t forget: Submit your first AI use case via our landing page to schedule an AI strategy briefing with our team.
