AI in Action, Part 2: Reliable and Responsible Systems for Mission Solutions
The ever-expanding market for artificial intelligence (AI) continues to introduce newly theorized use cases and prospective applications for today’s most emerging technology. But with so much focus on the hypothetical and testing the limits of AI’s potential, many federal agencies may be left wondering how to determine what is truly actionable and how to quantify the return on investment in AI.
On May 27, I joined government and industry thought leaders for a virtual panel discussion on how to utilize these new technologies to solve mission problems in a way that is effective and appropriate, and how different strategies can improve an organization’s data analytics and computing infrastructure.
The webcast served as the second installment in a three-part series from WWT and NVIDIA on the topic of AI in Action, focusing on the future development of AI through collaboration and transparency and developing reliable and responsible systems to achieve mission solutions.
AI is not confined to merely one singular market, nor does it exist or fit within one categorization. And this key distinction of AI presents itself as a major advantage as well as possible roadblock when it comes to putting AI into action and implementing this emerging tech into the infrastructure.
AI areas of investment
Consider a selection of the priority areas that are fueling DoD’s continued pursuit of and investment in AI:
- Warfighter Awareness – AI an extract useful information from raw data and help commanders select courses of action that improve mission outcomes, minimizing risks to deployed forces and civilians.
- Predictive Maintenance – AI can predict the failure of critical parts, automate diagnostics, and plan maintenance based on data and equipment condition.
- Humanitarian Assistance – AI can support U.S. civil authorities by shrinking timelines for situational awareness and response using computer vision of satellite and aerial imagery.
Due to its versatility and adaptability, AI has the potential to have a tremendous impact across many key areas of interest that would otherwise be disconnected altogether. Consider AI as a living software, constantly being updated, retuned and recalibrated for maximum efficiency; to operate it, agency CIOs/CTOs/CISOs must remain fully trained up on how to do so. The challenge of AI adoption is going to remain a priority for agency consideration and will continue to impact projected business models.
A four-phased approach
At WWT, we are constantly working with government agencies to realize efficiencies and improve data analytics and computing infrastructure. In fact, we have a four-phased approach to how we do this with our customers:
- Develop a Data Strategy. It might seem all too obvious as the first step, but to get our customers started, we have them start with a data strategy that is directly tied to achieving their business goals. Once a plan is developed, it is time to get started on putting it into action.
- Review of Data Governance. Next comes a series of priority questions presented like a Choose Your Own Adventure for AI: How are you going to deal with the data in question? How are you going to contextualize it? What operational policies do you need to enact or update to oversee these efforts? What are your data sharing requirements? How an organization responds is based on their unique anticipated business outcomes.
- Build Out the Technical Platform. At the end of the day, all of this still needs to run on something, somewhere; whether it’s on-prem or off-prem, in the cloud or in a hybrid architecture, determining the technical platform is a key component to data and AI success. A fully operational platform is the underlying enabler to any data analytics and AI project. If you want to experience how WWT leverages technologies from across the industry, I invite you to continue exploring the WWT Digital Platform to gain access to our state-of-the-art Advanced Technology Center (ATC). Via the ATC, we provide our customers an integrated collaborative environment to develop AI models, build them out and conduct proof-of-concept testing and take them into development and production.
- Key Learnings and Data Analytics. Once the technical platform has been developed and tested and the infrastructure is in place, we are able to we help our customers connect data sources, iterate and test AI models, build applicable use cases and develop production applications. Once the ML is actively learning patterns from the data and the AI is producing the desired outcomes, customers are in position to maximize AI and ML—but as you can see, there are many steps that must be completed before the connection between data and value is clear.
Every organization is going to find itself at a different stage over time. Once you get to the point where the ML is learning patterns from the data and the AI is producing the desired outcomes, customers are in position to see a clear connection between data and value. As is demonstrated through these four foundational concepts, there is a lot that has to happen before you get to this stage.
When I look at the potential return on investment in new technology, I like to look at it through three lenses: security, efficiency and effectiveness. If the tech can deliver on all three of these, then the ROI it will yield is likely to be quite positive.
With AI, you can really show the fiscal ROI; with that information, you can in turn improve an organization’s workforce efficiency to be more operationally effective.
Register for the third installment of this series: AI in Action: Enhancing Access and Efficiency Alongside Security, Tuesday, June 30 at 3:00 PM, featuring WWT’s Chief Technology Advisor Vimesh Patel.