AI Will Live and Die By Trust

AI will live and die by trust.
Eduardo Kassner
Chief Data and AI Officer, Microsoft

The potential of AI is unmatched, but we have to be able to trust it. Once we trust it, we can address various use cases, ideally one at a time, to avoid confusion or overwhelm IT teams with too much too fast. Hear more of Eduardo Kassner's insights from the Extreme Connect panel on AI:


Answering the Key Questions Around AI Adoption

Navigating trust in AI involves addressing hesitations and slowdowns in its implementation, as Eduardo Kassner of Microsoft explains in the video below. Kassner outlines the five fundamental questions that everyone has around AI, including:

  • Concerns about data privacy
  • Security compliance
  • Scalability management
  • Cost considerations
  • Reliability and accuracy

Kassner's analogy underscores the primary importance of AI fulfilling its intended purpose, highlighting the necessity of trust along the AI journey. Establishing trust involves recognizing AI's usefulness within specific scenarios, thus driving its adoption and integration.


Is My Data Going to be Private?

Given AI's dependence on data, ensuring the privacy of this data is crucial. As AI solutions continuously gather and analyze data, concerns arise regarding its confidentiality and protection from unauthorized access or misuse. Efforts to protect his data may include encryption protocols, stringent access controls, data anonymization techniques, and adherence to strict privacy regulations. Organizations and AI developers must take proactive steps to prioritize data privacy, thereby fostering trust among users and stakeholders in AI systems.

Am I Going to be Secure and Compliant?

The next question concerns security and compliance: "Am I going to be secure and compliant?" As compliance becomes an escalating concern for CIOs and organizations, ensuring AI solutions adhere to regulatory frameworks will be key. This entails implementing stringent security measures throughout the AI solution's lifecycle, encompassing data encryption, access controls, and secure authentication protocols. By prioritizing security and compliance measures, organizations can instill confidence in the integrity and reliability of their AI systems, fostering trust among stakeholders and users alike.

How Do I Scale My AI Deployment?

Addressing the question of scaling AI deployment and usage requires a strategic approach to accommodate evolving demands and workloads. As AI implementations and usage expand, organizations must devise scalable strategies to meet growing requirements effectively. This entails evaluating infrastructure capabilities, leveraging cloud-based solutions for scalability, and adopting future-proof network infrastructures that facilitate seamless expansion.

Investing in automation and orchestration tools can streamline deployment processes and optimize resource utilization. Collaborative efforts between IT and business stakeholders are essential to align scalability initiatives with organizational objectives and ensure scalability remains a priority as AI deployments evolve. By prioritizing scalability considerations from the outset, organizations can effectively manage burgeoning AI workloads and sustain the momentum of their AI efforts over time.

How Do I Manage Cost?

Navigating the costs associated with AI deployments is a critical consideration for organizations seeking to maximize the value of their investments. As AI implementation progresses, managing costs becomes increasingly crucial. Organizations must adopt a strategic approach to cost management to address this question, leveraging techniques such as resource optimization, budget allocation, and cost-benefit analysis.

Will the Information be Accurate?

Ensuring the accuracy of AI-generated information is a big part of its effectiveness and trustworthiness. That said, as Kassner highlights, if AI fails to fulfill its primary purpose, concerns about accuracy become irrelevant. Human oversight will remain crucial in verifying the accuracy of AI outputs, especially in critical decision-making contexts. Striking a balance between leveraging AI to enhance productivity and ensuring accuracy requires continuous refinement and adaptation. At the end of the day, as long as AI can save you time, it doesn’t have to be perfect. It just has to get you there.

If you get into a truck and try to turn it on and it doesn't even start, do you care if it doesn't have a seatbelt? No, so my point is simple. AI dies on being useful in doing the purpose that you wanted it to do first, then it will fail on everything else.
Eduardo Kassner
Chief Data and AI Officer, Microsoft

Implementing AI to Boost Productivity

Implementing AI holds the promise of increasing productivity across all business functions. With its ability to streamline tasks and provide efficient solutions, AI emerges as a powerful ally in navigating the complexities of today's fast-paced world. Whether generating initial drafts or troubleshooting issues, AI stands ready to save valuable time and effort. The key lies in embracing AI, learning its capabilities, and leveraging it to drive positive outcomes.

Our AI panel closed out by stating that there would be two types of people regarding AI: those who embrace its potential and propel their operations forward and those who hesitate, potentially falling behind. In this era, the choice is clear: seize the opportunities presented by AI or risk being left behind.

About the Author
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Cammy Perry
Senior Content Marketing Specialist

Cammy is a Content Marketing Specialist at Extreme Networks, leveraging her expertise to craft thought leadership and engaging content.

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