When we talk to leaders of government enterprises about their tech operations, they often ask, “How can technology fix our operational challenges?”
Our response is always, “Let’s start with a better question.” While it demonstrates a welcome openness to innovative solutions, framing the challenge solely as a new technological fix assumes that government enterprise leaders know the entire scope of their operational challenges and can easily adopt technologies to solve them. But sometimes neither is the case.
Taking a more granular approach to asking the same question is a better way to begin. If government enterprise leaders start by asking how data can be used to uncover operational improvements, for instance, they can better pinpoint what problems to solve and why before digging into the how.
Insight derived from data analytics delivers the what and the why
Technological solutions can fix operational challenges in many ways – AI, multi-varied analysis, machine learning, wireless monitoring, Internet of Things (IoT) technologies, edge computing, and other tools. Choosing the best option depends on the mission and goals for the deployment.
What, for example, is an office, agency, or institution trying to measure, discover, monitor, or resolve? Why must they do it? Answering these questions helps leaders decide which approaches and technologies would best improve their operations.
They can then harvest and sift through data generated in specific workflows that need attention, pinpoint the issues they want to improve, and understand the operational significance of making those improvements. The resulting digital transformations can exponentially increase quality, efficiency, and productivity.
Digital transformations heighten operational efficacy
Four innovations generally occur when government enterprises take this introspective approach to improving their operations and deploying targeted AI-enabled solutions:
1. Predictive maintenance
By isolating a problem, predictive maintenance technologies can help agencies avoid repeating the issue in the future. Predictive maintenance detects system faults or failures long before they lead to a catastrophic breakdown, saving resources and extending asset lifetimes.
Integrated with an enterprise’s computerized maintenance management system (CMMS), predictive maintenance sensors can detect minor, and often imperceptible, faults in manufacturing operations like defense plants, energy infrastructure, transportation yards, IT networks, and other workflows before they grow into critical malfunctions.
With predictive maintenance, operations can switch from reactive to proactive management and leaders can make better decisions more quickly. The costs of maintenance, emergency repairs, and replacement costs can be eased or eliminated as large-scale failures are averted. Ultimately, these improvements can significantly maximize uptime and minimize waste.
2. Robust workforce management
Several public sector domains are experiencing workforce shortages. Like the private sector, they face difficulties recruiting the talent necessary to tackle an expanding workload. The public sector also faces unique challenges, including competing with the private sector for highly desirable talent, discretionary budget cuts, an aging workforce, and a dependence on outsourcing and contractors.
Take cybersecurity, for example. Both the private and public sectors are competing to hire skilled professionals who can mitigate current risks and prevent future threats because they are in desperate need of those skills.
However, even if enterprises were optimally staffed, human effort alone would still face serious shortfalls in speed, accuracy, and efficiency, particularly when identifying risks or threats that haven’t fully emerged. This is where machine learning technologies coupled with human teams can exceed human capacity alone.
By integrating internal networks and IoT components with AI tools, an enterprise can exponentially improve its capacity to perform historical, real-time, and predictive insights and analytics without requiring teams of data scientists or other specialists. The difference in outcome when comparing a manual versus an automated system can be immense.
3. Leveraging AI, IoT, and machine learning to enhance and evolve systems
Humans are limited when it comes to detecting hidden patterns across multiple data streams in different workflows. ML-based detection systems are designed to identify these unknown unknowns long before they become apparent to the naked eye.
AI/ML-based systems are also more accurate in discerning real risks from false signals than traditional rules-based systems, cutting down the number of red flags that indicate potential faults or failures so humans can determine legitimate problems. In certain domains where false positives tend to dwarf legitimate risk warnings – consider the U.S. Treasury’s Financial Crimes Enforcement Network, where the rate of noise-to-signal can be as high as 95 percent – AI tools can accurately filter out false positives, improving productivity and boosting a team’s analytic performance to levels unattainable on a manual scale.
Lastly, AI-powered solutions learn from experience. One of the biggest benefits of machine learning is its ability to auto-enhance its own efficacy and efficiency. By analyzing high volumes of data across multiple systems and identifying linkages and patterns across multiple data streams, AI has the capacity to improve performance over time.
4. Finding the data for actionable insights
As government enterprises identify and learn more about their challenges and integrate solutions, they will attain more data from these new workflows that will reveal further efficiencies and avenues for improvement.
This iterative process of adapting as more data accumulates is extremely powerful. Government enterprises gain self-optimizing tools that perform at levels exceeding human capacity, compensate for their otherwise limited resources, and allow them to focus on the operational priorities that they handle best.
Ultimately, AI adoption will allow you to heighten and redefine best practices within your enterprise, ensure that staff have the tools to be successful, and give your company better data to train personnel and advance best practices.
The bottom line
By first outlining key challenges you’re trying to solve, deploying AI across your agency will be smoother, faster, and more effective. Once government enterprise leaders can articulate a keen and detailed understanding of their problems and why they need addressing, leaders are in a much better position to find data that will deliver actionable insights. The results are much more likely to comport with their bold ambitions for improving government operations