Artificial intelligence (AI) is transforming the way businesses operate, offering new opportunities to enhance productivity, improve customer experiences, and drive innovation.
However, with great potential comes great complexity, and organizations often find themselves struggling to balance the promises of AI with the realities of implementation. How can businesses and public sector agencies navigate the challenges of AI adoption while maximizing its benefits?
Joined by Rob Kim, CTO and Presidio, and Dan Lohrmann, Field Chief Information Security Officer (CISO) for Public Sector at Presidio, we recently discussed ways to shed light on the complexities of integrating AI into organizations, from the excitement in the private sector to the cautious approach of public agencies.
Here’s what we learned about AI’s potential, its challenges, and how organizations can embrace its transformative power responsibly. To learn more about AI readiness, download the 2024 AI Readiness Report.
The ROI Dilemma: Moving Beyond the Hype
As businesses rush to capitalize on AI, many are finding themselves caught between expectation and execution. Robert Kim highlighted the difficulty in pinpointing AI’s return on investment (ROI) beyond surface-level gains.
“This stuff is expensive,” Rob Kim says. “Whether you get it as a public-based curated cloud service, or we decide to build our own internally and download frozen models like Meta and run them internally. The reality is, they’re trying to figure out how you do beyond employee productivity, knowledge worker productivity, and gain some better efficiencies.”
Kim acknowledged that while early applications such as customer experience improvements and contact centers show promise, broader industry-specific ROI use cases are still emerging.
“We are starting to see some of these ROI-justified use cases starting to emerge more, and they do definitely tend to be much more industry-specific,” Rob Kim says.
While AI’s potential for boosting customer experience and operational efficiency is undeniable, the real challenge lies in identifying industry-specific applications that justify the investment. Early successes have emerged, particularly in areas like contact centers to quantify ROI in other domains.
Bridging the AI Preparedness Gap
A recurring theme in the discussion was the mismatch between AI’s hype and organizational readiness. This gap often stems from a lack of clear data governance and ethical frameworks. According to Dan Lohrmann, this is particularly evident in the public sector, where slower adoption is tied to regulatory hurdles and a cautious approach to new technology.
“There’s a lot of talk around less regulation and rolling back regulation,” observed Dan Lohrmann. “My gut going into it is you’re gonna see less regulation from the Trump administration than we would have seen possibly by a Biden or Harris administration. But, again, I think time will tell on that. And, that leadership from the federal government that may be looking for some national policies.”
For organizations in both sectors, education is critical. Leaders must demystify AI and ensure their teams understand its capabilities and limitations. Proper training and better data practices can empower organizations to close the preparedness gap and unlock AI’s full potential.
Dan Lohrmann emphasizes the widespread recognition of AI’s potential and its growing adoption across industries. He highlights that 36% of IT leaders consider AI essential for staying competitive, and 63% report it as their company’s largest investment area—a trend expected to grow in the coming years. While finance and healthcare sectors lead in AI adoption, government agencies often lag, creating opportunities to learn from private-sector successes and improve their AI strategies.
The Data Quality Challenge
AI’s effectiveness hinges on the quality of the data it processes, but poor data practices often undermine its impact. This issue is a stumbling block for many organizations, as messy or incomplete datasets can lead to unreliable AI outcomes.
Kim and Lohrmann posed the question of why data quality trips us up, pointing out that data governance isn’t just about compliance, it’s about ensuring that AI solutions are built on a solid foundation. Without this, even the most sophisticated AI models can falter.
The solution lies in prioritizing robust data management practices, including cleaning, organizing, and securing datasets. This also involves addressing biases within data to ensure AI systems produce fair and accurate results.
Ethics and Oversight in the Age of AI
As organizations adopt AI, they must also grapple with its ethical implications. From bias in decision-making to privacy concerns, ethical oversight is crucial to avoid missteps that could damage trust and reputation.
Kim and Lohrmann stressed the importance of aligning AI initiatives with business challenges to ensure they drive real value without compromising ethics.
For public and private sectors alike, this means creating governance structures that monitor AI’s impact and align its use with organizational values.
Practical Steps for AI Adoption
Despite the hurdles, Kim and Lohrmann offered practical advice for organizations ready to embrace AI.
- Start with Education: Equip teams with the knowledge they need to understand AI’s potential and limitations. This includes offering training programs and fostering cross-departmental collaboration.
- Focus on Data Governance: Prioritize high-quality, unbiased data to ensure AI systems are reliable and effective.
- Align AI with Business Goals: Avoid falling for the hype by ensuring that AI projects are tied to specific, measurable business outcomes.
- Build Ethical Oversight: Establish clear guidelines to monitor AI’s use and prevent unintended consequences.
The Road Ahead
The clash between AI innovation and organizational challenges is far from over.
“Every time I see a report, it seems like more and more organizations are getting involved and want to get involved and learn more about what the opportunities are,” Dan Lohmann says.
Kim added that while progress varies across sectors, AI adoption is poised to become ubiquitous.
“As we head into 2025, you’re going to see that number grow even higher, pretty much getting too ubiquitous across the board,” Rob Kim says.
As we move forward, the question isn’t whether AI will reshape industries, but how organizations will adapt to ensure its integration drives meaningful and sustainable outcomes.
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