Interesting Use Cases for AI-Driven Action Recommendations

Run-through

What are some use cases for Artificial Intelligence (AI) that can help Business Intelligence (BI) professionals? In BI, you are tackling some of the most interesting challenges in finding win-win-win solutions in today’s constantly evolving environment. There’s a path to making companies more profitable, workers happier, and customers more satisfied. It’s often on you – the BI manager or analyst to find it. There are many ways AI can help in expediting your accessing and making sense of Big Data and automating tedious time-consuming tasks.

Almost anyone can get involved in solving win-win-win equations – to improve upon what they’re doing or help their teams perform better. Granted, not every company frames things in a win-win-win context, but there are few reasons why they can’t or shouldn’t with the proliferation of AI systems to help solve the equations.

The Win-Win-Win Logic for BI

The goal is for everyone to benefit. Otherwise, like that “Fast, Good, Cheap: Pick Two” – we’re talking about “Company, Workers, Customers: Pick Two.” There are plenty of examples of profitable companies with happy customers, but unhappy workers. How about the whopping 150% turnover for the rank and file in Amazon’s warehouses and 1-year average tenure for its techs? The logic behind win-win-win when successfully executed looks like this: Workers who love their job are typically more productive, deliver higher quality work, and change jobs less frequently. This tends to make the companies they work for more profitable while customers receive better products. Happy customers tend to share their opinions with others, word of mouth is a powerful thing, helps grow business. Businesses that grow tend to have more promotion opportunities, career growth being a major retention element and helping retain company-specific knowledge. And it’s a cyclical, continuous process. Of course, there’ll be bumps along the way. But a company focused on developing its full potential will respond to those bumps much faster than those settling for the status quo.

Use Cases for AI to Create Win-Win-Win Situations

While we’re only covering three interesting AI-driven action recommendation use cases (including our own), we have a couple of recommendations where you can read scores and scores more. New AI applications are rolling out daily. After watching interviews with Keanu Reeves and Carrie-Anne Moss about the Matrix Awakens (and the Unreal 5 Engine) and Elon Musk’s on Babylon Bee about Neuralink, I had to question whether I took the red or blue pill. In the past few days, every news channel has been talking about a new AI system being rolled out by the Shanghai Pudong People’s Procuratorate. Shanghai has a population of about 26 million people, so one can imagine it also has a fair bit of crime. In effect, this system is able to handle the prosecution of the 8 most common crimes in China’s busiest prosecution office. Claims are that it can ascertain with 97% accuracy if someone is guilty of credit card fraud, theft, gambling crimes, reckless driving, and things like causing dissent. While this is the first instance of AI being used by a government prosecutorial office (that we know of), it’s by no means the first time AI’s been used by lawyers. Even back in 2016, an AI “Chatbot Lawyer” was used to fight parking tickets in New York City, overturning 160,000 of 250,000 cases.

GPT-3 for AI-Driven Content Development

OpenAi’s Generative Pre-trained Transformer 3 or GPT-3 has gotten a lot of buzz in the past few years. It’s basically an AI system using Natural Language Processing (NLP) to generate “human-like text.” You might remember how GPT-2 was so good that it was too dangerous to share and it generated a fair bit of controversy to boot. Not exclusively of interest to BI managers or analysts, but most companies are involved in blogging, writing whitepapers, creating presentations, maybe even writing… poetry. Some argue that GPT-3 can code, too – though any serious developer is likely to see it as an exercise in grins and giggles. And perhaps a few surprises. It may be unfair to write it off entirely though, as GPT-3 has been integrated in Microsoft’s Power Apps providing “citizen developers” with a low code environment. Companies like Bloomberg and Yahoo actively use NLP in content creation for turning out thousands of pages. There are a variety of AI-Text generators available as SaaS products, to be sure. As we’re increasingly engaged in the development of AI for Gitential, we naturally wondered how useful GPT-3 or other NLP programs might be for us. Turns out – not very, but… GPT-3 and other AI-writers can be surprisingly good with fairly basic, non-technical content. Emphasis is on can be, it is hit or miss on the best of occasions. Spell-checking, grammar and punctuation is quite good, but human editing is still required. It can generate “facts” that need to be validated Just a few months ago, ArticleForge announced its 3.0 release, though at the time it was taking about 10 minutes to process each query. And then, only about 20-25% of the total content generated was useable with light editing, but… it did raise my eyebrow with the following example.

Raw output of ArticleForge 3.0 on What is Business Intelligence:

BI is a term that refers to the technologies and strategies used to analyze data and provide a predictive view of a company’s business operations. In its simplest form, BI is the analysis of business data. The goal of BI is to give managers and business leaders a complete picture of the state of a company’s operations. Businesses can use BI technologies to gain these insights. These tools are useful in analyzing current and historical business data.
Some of the most common types of BI are: healthcare, education, and technology. Hospitals, universities, and corporations all use business intelligence to better serve patients. The use of BI is growing because the information it provides can help improve decision-making and increase overall efficiency. A business intelligence system can also help companies manage their costs and optimize production and efficiency. While this may seem a bit overkill, the technology is here to stay and will continue to grow in popularity.
BI is a tool used to transform raw data into actionable information. It involves a collection of technological processes that can turn structured or semi-structured data into useful information. The tools are often used to generate profitable business decisions. It can also be used to create reports for clients. If a client wants to build his or her own business intelligence, they can create custom dashboards. Regardless of the software used to generate the reports, it can be easily customized to suit their needs.

That’s surprisingly good, aye? Give it a few years…

AI for Revenue Management

“Show me the money!” Few arguments are more convincing than company profitability. For the travel and hospitality industry, but increasingly for retail consumer packaged goods (CPG) and even pharmaceuticals, Revenue Management (RM) or Revenue Growth Management (RGM) is getting a lot of attention. RGM is about optimizing product lines (architecture), prices, and promotions to steadily boost sales and market share.

In a traditional “manual” RGM program, it can take several six months or more to determine a 5- year pricing strategy for a given country. Some CPG companies are active in most of the world’s 195 countries, so the slow cycle time hurts. Everyone on an RGM team is likely to move on before they see a single cycle complete.

Our friends over at Revenue.AI are completely changing the RM/RGM game with AI-driven decision-making with their digital assistant (RAI). In a nutshell, it enables BI/RGM specialists to optimize prices and promotions on a “per product or SKU / per store / per country or region” basis, in real-time. It makes the decision-making process at least 10x faster with an exponential increase in granularity. With Next Best Actions, Stop Action, and Real-Time Alerts, it also keeps Sales, Marketing, and other teams aligned to organizational objectives. This knowledge sharing promotes cross-functionality and minimizes the impact of turnover.

Where historical “manual” RM programs have been hit or miss, so far AI for RGM is showing a fairly consistent (and imo, conservative) 3-5% yearly revenue growth. That may sound modest, but for large international CPG companies making billions, this adds tens or hundreds of millions more despite having few to no new markets to expand into. However, it also provides smaller companies the means to seriously compete against their larger counterparts.

Gitential for Software Development Teams

Take Git and combine it with Potential and there you go – Gitential. We’re in the process of upgrading our analytics platform into providing an AI-powered Digital Assistant to help software development teams unleash their full potential… faster. All present and new subscribers are automatically enrolled in our Early Access Program so you can follow along and offer feedback to improve its usefulness for you.

Nearly everyone involved with software development teams stands to benefit – C-levels, BI and HR professionals, engineering managers, and developers. Though still in the early stages, you’ll be able to ask a question and receive a data-driven answer showing a visualization of the data, definitions of the data, a summary of the situation, and Next Best Actions – “Actionable Insights On-Demand.”

The following provides an overview of the major components where our AI can assist you to achieve Win-Win-Win situations:

  • Executive Reporting with real-time summaries to understand project costs and steps to align your organization around critical KPIs to maximize ROI.
  • Delivery Management to optimize teams around project requirements to reduce unexpected costs and delays.
  • Budget Optimization with JIRA integration to understand and improve your budget by project cost and team performance.
  • Risk Management with predictive analytics and real-time alerts to proactively mitigate performance risks.
  • Team Management for a 360° real-time view of team activity and performance to keep all of your increasingly remote teams aligned to organization and project objectives.
  • Team Growth will provide you with “Insights On-Demand” to 10x your developer’s professional growth and structure performance reviews.
  • Team Efficiency to improve teamwork, reduce technical debt and improve cycle time.
  • Vendor Management to set and track third-party team KPIs, cost, and responsiveness to help you optimize around best performers.
  • Benchmarking to compare performance by organization, project, team, or developer, on any metric to set realistic goals and improve knowledge-sharing.

We’ll definitely keep everyone posted as new features are implemented, but we do hope you’ll join us for what is guaranteed to be a very interesting ride!

Want More Use Cases for AI-Driven Recommendations?

Three is only the literal tip of the Ice Borg… If you’re looking for more, you can find AI being applied just about everywhere. Here are two long lists of links that cover nearly every use case and industry imaginable.
  • The CallMiner Team shares 50 AI-driven examples and use cases with links and detailed summaries of the best takeaways from each.
  • Cem Dilmegani of AIMultiple assembled a detailed December 2021 list of over 100 AI use cases spanning 11 categories from HealthTech to Sales and Marketing, Customer Service, FinTech, and more.

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