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Popular State, Local, and Education Use Cases:

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What is needed is a fundamentally new approach to AI; an AI solution that provides an OPTIMAL AI model tuned to your unique business needs, one that is always optimized, even as business conditions change. 

Which cases are likely to be fraudulent?

Of all potentially fraudulent cases, which ones should be pursued to get the most value for the least cost?  How do we optimally allocate resources across different investigative activities (such as interviews, electronic searches, background checks or contact with employers) to get the most cost-efficient return?

As economic circumstances shift and government services and customer behavior change along with them, how should we adjust to evolving fraud and waste patterns?

Inspections and Maintenance
Optimally allocate operations resources across multiple inspections and maintenance activities to deliver the highest overall ROI

Fraud Management
Determine  which inspection strategies should be used to most cost efficiently reduce fraud and waste

Case Management/Customer Service Optimization
Find out which cases and customers you need to prioritize to deliver the optimal overall results

Student Retention
Find out which students you need to prioritize for your retention efforts

Scenario Planning 
Prepare for best case, worst case, and everything in between automatically with AI

Hypothesis Testing
Drive user engagement by sending the right content to each person at the time they are ready to consume it

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Traditional AI falls short, but there is a solution...

Aible's Unique Approach to Typical State, Local and Education Use Cases

Why Aible For AI Optimization

Optimize Fraud and Waste Management

How likely is it that a part will fail or that a vehicle will run out of fuel?

Of all the maintenance activities we could undertake (such as repairs or performing upgrades), which ones should we pursue given our resources in order to have maximum impact? How do we optimally allocate operations resources across multiple inspection and maintenance activities to deliver the highest overall ROI? When does opting for a more expensive maintenance action save money and lower risk in the long run, and when is that not the case?

As market and economic conditions shift, how do we change our maintenance patterns to accurately reflect new consumption patterns?  If more street protests are taking place, how do we optimally prioritize maintenance for police and fire resources? If COVID-19  cases suddenly flare, how do we quickly prioritize the maintenance of healthcare resources based on evolving needs?

How likely is it that this case or customer service opportunity will escalate or have harmful effects? How likely is a student to leave the program?

Given our resource constraints for student retention, case management and customer service, which students, cases and customers do we need to prioritize to deliver the best overall results? How do we optimally allocate resources across different channels of service (such as in-person, emails, phone calls or letters) to deliver the best and most cost-efficient results?

As market conditions and healthcare patterns shift, how do we quickly adjust our student retention / case management strategies and resourcing to react to evolving needs? How can we identify problems caused by shifting patterns as early as possible?

See Aible in Action

See how Aible helps inform critical maintenance decisions.  This is an actual project at a major US state but based on only a small extract of their historical data.

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Optimize Inspections and Maintenance

Optimize Student Retention 
/ Case Management / Customer Service

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Governments everywhere are being challenged as never before. With the Covid crisis, citizen protests and economic uncertainty straining resources, governments have to find a way to do more with less.

In this volatile market, are you struggling with these issues?

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Which inspections and maintenance actions (such as repairs or performing upgrades) should be executed to minimize overall cost? How do we optimally allocate operations resources across multiple inspections and maintenance activities to deliver the highest overall ROI?

Which inspection strategies should be used to most cost efficiently reduce fraud and waste? How and where should we focus our attention on evolving fraud and waste patterns in light of behavioral changes brought on by the COVID-19 crisis?

Which students, cases and customers do we need to prioritize to deliver the optimal overall results? How many student advisors, case managers and customer service resources do we need to obtain the optimal results?

Fraud and Waste Management

Maintenance &

Student Retention Case Management
Customer Service & Optimization

Why Trust Aible?

Visionary in Gartner Magic Quadrant 

Gartner Cool Vendor
Featured in 23 Gartner and Forrester Reports
From the creators of Salesforce Einstein Discovery / BeyondCore

Small extract of data from a US State

More government entities than ever are turning to artificial intelligence to improve their operations. Government entities are turning to AI in order to better predict how likely a case is to be fraudulent or that a customer service opportunity will escalate. 

But here’s the bad news: so far, AI has been a disappointment for many businesses. According to a recent survey by MIT-Sloan and the Boston Consulting Group, 65% of companies report seeing no value from their AI investments. Even among organizations that have made “significant investments” in AI, 40% report no business gains from AI.

There are many reasons companies have failed to see business gains from traditional AI, including:

Aible is the only AI solution that goes beyond predictions to enable you to optimize and anticipate. Aible lets you consider hundreds of shifting market factors and gives you the optimal recommendations to maximize business impact and ROI.

Users have access to a single AI predictive model that is tied to a specific set of business conditions. When conditions change, the model cannot dynamically respond.

Users are not able to define their business assumptions or cost-benefit tradeoffs to determine how to best allocate their limited resources.

End users cannot easily incorporate their feedback resulting in a less optimized model.



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