The columnists and interviewees featured here are members of the InformationWeek Insight Circle -- an invitation-only group of trusted readers and expert sources. The Insight Circle helps our editorial team ask the hard questions and make sense of the answers. (Send nominations to [email protected].)
This session provides a high-level overview of generative AI focus areas across business functions.
Money talks, especially when generative AI finds itself facilitating more than just chats. Modern enterprises continue emphasizing the need for a clear AI strategy that is aligned with organizational goals. While real-world use cases are nearly limitless, so are potential risks.
In this archived keynote session, Sidney Madison Prescott, CEO of Moonshot Productions, explores ways to integrate GenAI into enterprise technology to increase business efficiency and productivity. This segment was part of our live webinar titled, “Generative AI: Use Cases and Risks in 2024.” The event was presented by InformationWeek on May 29, 2024.
A transcript of the video follows below. Minor edits have been made for clarity.
Sidney Madison Prescott: So, when we talk about transformation, we have all these different business categories within the traditional enterprise. And it's about looking at how do we do business today? What are the tools at our disposal?
How can we think differently about the way that we approach transformation from a technology perspective in the enterprise by leveraging generative AI?
One of the ways that we do this is really thinking about how do we create a safer future and guardrails around these tools? We use generative AI to enhance different capabilities inside of the business, but we also need to think about some of the guardrails that need to be put in place as we leverage these technologies.
As we transition from the current state of business methodologies toward future methodologies, understanding what that means in terms of also transforming the way that we approach compliance and risk at scale in the enterprise is important.
So, let's really think through how we can apply these concepts from an operational perspective, which is, in essence, that broader enterprise journey. When we talk about enterprise AI, it truly is a transformative journey that begins with the awareness and education of your stakeholders.
It's about really focusing on helping everyone in the organization understand what AI is, as well as the potential benefits and implications of leveraging this technology on a broader scale. Again, a key point of this is education.
During my journey at several different companies, I've really focused on how we can upskill employees to understand not only the technology, but also how it can be applied to their specific area of concentration within the business. This really helps your stakeholders start generating ideas about how they can leverage this technology in unique ways.
One thing I do want to stress in terms of looking at this from a leadership perspective, it really is about developing a clear AI strategy. That strategy needs to be aligned with your organization's overarching goals. So, this is about how we can identify the areas of the business where we can add the most value by utilizing AI.
Also, how do we set realistic measurable goals for how we're not only going to implement this technology, but how we're going to measure its success over time? One of the things that I would suggest is making sure that you establish a clear roadmap and KPIs at the beginning of your AI journey.
This will help you to really understand how you need to flow through the process. Whether that's adding new employees to the team that have this skill set or looking at selection. At each milestone, make sure you have a clear understanding of what you need to deliver on time and where you're going next.
One of the primary drivers for enterprise adoption of AI is its impact on boosting operational efficiency. We talk a lot about AI and automation, and it really comes down to automating routine and manual tasks.
This allows us to really improve the quality of the data that we have in our various systems, as well as helping accelerate business processes. This leads to an overall increase in productivity at that higher organizational level.
We can also look at AI to help cut costs. Now, this is less about removing employees from the enterprise, and more about enabling those employees to do more with their time. Ideally, this allows more value added, knowledge-based tasks, versus those very highly manual, repetitive, and tedious tasks that can be allocated to AI.
Another piece of this puzzle is also looking at how we can use AI to be a little bit more predictive from an analytical perspective to help us in terms of how we're foreseeing downtime and maintenance costs. This means finding ways that we can look at potential failures in the system and being able to remediate those potentially before they even happen.
Not only does this provide a holistic view of productivity enhancement, but it also shows cost optimization, scalability, and agility. All of this combined really helps us to improve the overall operational efficiency of our business.
Watch the archived “Generative AI: Use Cases and Risks in 2024” live webinar on-demand today.
Read more about:
test keyword for featuring ICYou May Also Like
