Essential Pillars for Generative AI Success Webinar Recap
Key Takeaways: Insights from the Expert Panel on Driving AI Success
During the Blue Mantis webinar, an expert panel discussed themes that are critical for AI adoption:
- Business Alignment: Any AI initiative at your organization should align with your specific business outcomes and be driven by executive sponsorship.
- Incremental Adoption: Start with quick wins that deliver a measurable return on investment (ROI) and build momentum for larger-scale projects.
- Data Integrity: Ensure clean, curated datasets are the basis for AI applications to maximize trust and efficacy.
- Hybrid Solutions: Leverage technologies that enable integration of data across diverse environments and platforms (e.g., cloud and on-prem) to make AI more effective.
- Diversification: Experiment with multiple use cases using a portfolio approach to mitigate risks and uncover opportunities.
There’s no denying that generative AI is a transformative force today, reshaping how business and IT leaders innovate, manage data, and deliver value for their organizations and the customers they serve. Blue Mantis recently brought together a distinguished panel of AI experts for a live webinar titled “Essential Pillars for Generative AI Success: AI Agents Powered by Data and Security.” Attendees heard a discussion on the winning strategies, real-world challenges, and golden opportunities surrounding AI adoption, with perspectives from these industry leaders:
- Tim Walton, Head of Capability and Capacity for AI at Microsoft
- John Treadway, CEO and co-founder of AI Technology Partners
- Chris Moyer, VP of the AI & Data Enablement practice at Blue Mantis
Starting Small: Microsoft on AI Momentum
Tim Walton kicked off our conversation with compelling insights into adopting AI incrementally. He emphasized the importance of aligning AI initiatives with business outcomes from the outset. “Starting off with very quick wins, and it has to have a business outcome,” he noted. According to Walton, successful projects often focus on high-yield use cases that deliver specific ROI, ensuring a lower risk of failure.
Walton also stressed the importance of executive sponsorship: “You have to have that strong leadership at the customer with an executive sponsorship to drive innovation through the organization.” From governance and compliance to leveraging Azure AI for custom solutions, Walton’s advice underscored the need for a secure foundation coupled with business alignment.
Low-Hanging Fruit: The AI Portfolio Approach
John Treadway from AI Technology Partners, expanded on Walton’s ideas by advocating a portfolio approach to AI adoption. “Find that low-hanging fruit. We walk customers through that process using a two-by-two grid that shows the cost or difficulty of implementation versus business value or ROI,” Treadway said AITP’s grid-oriented format allows for IT leaders to visualize their needs and “focus on high-impact, low-cost AI solutions.” Treadway explained that this method allows businesses to experiment with multiple use cases while mitigating the risk of investing heavily in a single initiative.
Treadway also addressed the challenge of balancing experimentation with risk, stating, “The risk is that if you choose only one thing and go all in, and it doesn’t succeed, it can sour the business on AI altogether.” This emphasizes the importance of diversification in AI adoption strategies, while implying that outright ignoring AI is possibly a bigger risk.
Data Governance for AI Success
Chris Moyer, Blue Mantis VP and expert on strategically preparing data for generative AI success, brought the conversation back to one of the core concerns for all business and IT leaders—data governance. “At the end of the day, we’re all trying to make data-driven decisions,” Moyer said. “But while artificial intelligence is artificial, we want to make sure the data is official.” This was a great illustration of how companies must prioritize data integrity and trust as the backbone of any AI initiative.
Having built data governance systems for years, Moyer emphasized that for AI deployments to succeed, proper testing, quality assurance, and quality control practices are essential. “Incorporating proper testing practices is where we’ve seen the most success,” he explained. Moyer’s focus on data quality highlighted the foundational role that clean, curated datasets play in driving meaningful AI outcomes.
Real-World AI and Webinar Attendee Q&A
A question from a webinar attendee steered the discussion to practical applications of AI. The attendee, noting that their organization had an IT estate with both Microsoft and non-Microsoft technologies, asked the panel what can be done to integrate generative AI across multiple platforms without compromising security or data governance. Tim Walton dryly asked with perfect comedic timing: “Do you want the Microsoft guy to respond?”
Chris Moyer jumped in, noting that his Data Enablement team at Blue Mantis works with a lot of different platforms and has partnerships with Microsoft along with Snowflake, Qlik, and others. “So, it really does not matter the products you have,” Moyer explained. “At the end of the day, it’s data governance that matters, whether it’s Copilot or ChatGPT or anything else. Our focus is ensuring data quality and trust across platforms.”
Tim Walton complemented Moyer’s perspective by discussing hybrid solutions made possible through Microsoft Azure Arc technology. He conceded that: “All your data isn’t going to be in Microsoft. It’s dispersed—on the shop floor, in manufacturing or retail. That’s why we’ve focused on technologies like Arc to enable machines and push AI models down to those environments.” Walton’s insights demonstrated how AI can be tailored to diverse operational contexts.
Another attendee asked if the security and data governance issues related to generative AI are the same when integrating Azure Cognitive Services (part of a broad suite of pre-trained AI tools and models from Microsoft used primarily by software developers) into a corporate IT estate. Both Tim Walton and John Treadway discussed how Azure Cognitive Services is used to process visual content, analyze speech, and more—but that is a different use case than generative AI. Everyone on the panel agreed that IT and business leaders should take a holistic approach to AI adoption based on their desired business outcome with associated metrics.
Conclusion: Everyone Should Have AI Access
If there’s one truth discussed in this webinar that I believe everyone should remember, it’s that while your business is piloting AI for just a few employees today, it will be an indispensable tool for all employees in a few short years. Just like word processing, spreadsheet, and email software became transformative for office work twenty years ago, AI will become the must-have tool for every employee for businesses that deploy it strategically and collaboratively.
As businesses plan their AI journeys, we recommend a phased “crawl-walk-run” approach where you start by looking for quick wins in deploying AI. Think about your current pain points and how generative AI might solve them. Most importantly, your executive leadership in IT, finance, and business strategy should all agree on a holistic AI deployment strategy that balances innovation, security, and ROI. Optimizing IT spending is extremely important given current levels of global economic uncertainty, and I encourage every IT executive to read the Gartner® report Quick Answer: 4 Actions CIOs and IT Leaders Are Taking in Response to U.S. Federal Policies and Tariffs.
Blue Mantis helps organizations across healthcare, finance, manufacturing, retail, and public sector industry verticals to successfully deploy generative AI technologies on time and within budget. Connect with us to assess your AI readiness today.
