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Quick and Easy AI Solutions

Quick and Easy AI Solutions

Machine Learning is Hard

We have seen a couple of conflicting outlooks in the world of Artificial Intelligence (AI). On one hand, you have the hype of Machine Learning (ML) and AI solutions greatly improving business decisions and outcomes. “Everyone ‘wants’ to leverage ML,” and if you do a quick search on how AI is vigorously being applied to Covid-19 solutions, you’ll find it’s quickly becoming accepted practice.

However, on the other hand, you have the perspective that “machine learning is still years in the future” for some organizations. While AI is being proven in mission critical industries like Healthcare, other organizations shy away from the possibilities of AI because they view it as “too difficult,” or in the proverbial sense, a “run” when many organizations feel they just need to learn how to “walk.” To that, I always ask, “why?!?!”

Late last year, we presented a workshop at the Microsoft MTC and showcased a few methods for implementing a sentiment analysis solution, each performing better than the last. The session lasted just 4 hours, i.e. 0 to ML in 4 hours. We implemented 3 completely different approaches to sentiment analysis in under 4 hours. This even included me presenting for 1.5 of them about the benefits, use cases and approaches to machine learning. So really, it was 0 to ML in 2.5 hours. Still think machine learning and AI is years in the future for your organization or that you need to wait until you’ve started walking?

Fun fact, my daughter could ride her toddler bike before she could walk. Let’s think of these AI solutions as hopping on a bike with training wheels and let’s forget about this walking concept.

What can ML do for me?

OK, so you’re a skeptic. You may say, “fantastic, what can a sentiment analysis solution in 4 (2.5) hours do for me?”

For starters, keep this in mind: According to IDC surveys, 67% of organizations globally have already adopted or plan to adopt AI. Many adopters have seen returns that meet or exceed expectations, leading many to increase spending on AI in the next two years. IDC sees the compound annual growth rate for AI spending near 50% in the U.S. and even higher in Asia/Pacific. Can you afford to ignore AI and wait years to realize your competitive ROI?

Let’s look at some quick and easy ML use cases that might just help you become an optimist--

Sentiment Analysis

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Sentiment analysis has many applications and just a few are discussed here. The application of sentiment analysis provides the ability to identify and categorize opinions or perspectives from within your data. For example, Is your customer happy? Is your student focused on the lecture? Will your customer buy this product?

Additionally, sentiment analysis is one of the most widely available AI solutions with several pre-trained solutions such as Azure Cognitive Services, SQL ML Services and many different custom approaches. As we demonstrated in our workshop, each approach can be implemented very quickly. The real questions when applying sentiment analysis is what sentiment data would drive business value and do you really want to know what your customers are saying about you?

Common use-cases:

  • Call center (Automated complaint tracking, customer perspective, rep management)
  • Company perspective (Social media, product reviews, complaints)
  • Quality of care (Healthcare delivery)
  • Deal closing likelihood (Customer perspective)
  • Internal Communication Monitoring – Score internal solutions like Yammer or Teams to get the pulse of the workforce or use it as a “policing tool”

Quality Score

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Quality scoring, i.e. providing an intelligent score in order to evaluate the ‘quality’ of that content. In the most common scenario, a quality score (or likelihood) can quickly be applied to your sales organizations deal data. Deal or no deal?!?! Helping your sales team identify quick win opportunities or where more effort could be applied to high value targets. Quality scoring can even be implemented with automated model training and low-code no-code solutions such as Azure ML Services and AutoML.

While quality scoring is basic predictive modeling, BEWARE as they can easily introduce bias depending on your feature input. You must understand the application of good vs bad bias.

Common use-cases:

  • Opportunity Scoring (Sales, Brand Location, Recruiting Evaluation)
  • Customer fit evaluation
  • Deal fit evaluation (these 4 products and this customer… or … these 6 and this customer?)
  • Employee performance
  • Employee or Customer retention
  • Care approach evaluations (input expected next step, receive quality of approach score)   


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Forecasting is the ability to predict future outcomes, such as sales revenue or inventory quantity requirements, based on historical patterns. This is a practice found in every industry and organization, although typically driven by employee tendency not factual patterns and is a very time-consuming manual process.

Unfortunately, often AI forecasting is viewed as a risk, primarily because trusting an algorithm to make financial predictions in a chaotic economic climate can be unnerving. And with that, if it was perfect, we’d all be rich from making better investment decisions or at least would have put our money in cash before Covid-19.

For a quick and dirty approach to AI forecasting, leverage the output of AI as your input to human intervention. Allow the forecasting to guide your own forecasting approach and save time in the process. Like Quality scoring, AI forecasting can be implemented quickly with Automated model training and low-code no-code solutions.

Common use-cases:

  • Sales Revenue
  • Inventory Quantity Expectations
  • Staffing Allocation and Alignment
  • Student Drop Out Predictions


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Recommenders are another excellent quick win AI solution. Specifically, because they are very easy to build and are well known, not just in Data Science but in life.  What is a recommender? Think online shopping, adds on your phone, Netflix content recommendations, etc. Recommenders ‘recommend’ something based on community patterns found within your data.

Recommenders are tried and true solutions, well documented and with a bit of creativity, or not, can provide great business value. Like sentiment analysis solutions these could also be demonstrated in hours.

Common use-cases:

  • Product recommendations
  • Customer targeting recommendations
  • Care approach recommendations (recommend the most likely next steps in the care process?)
  • Staffing recommendations (which staff to apply for this case? How many employees do I need in this territory?)
  • Predictive Maintenance (do I need to order parts next? Where’s the best place to look?)
  • Developmental needs for employees
  • Price for services, products, policies


Knowledge Mining

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Knowledge mining is a new term for an old solution; document indexing. Traditionally, document indexing and the ability to scan common indexes and terms in a search engine has been a manual process of creating indexes that are linked to the original documents.

With new AI solutions knowledge mining is just the application of recognition (form, video, image, speech to text, etc.) integrated with the power of search. Thus, automating content storage of any kind with the ability to search and find relevant content that is generated from the content itself and not dependent on the user’s interpretation. New concepts are actively being introduced such as knowledge store projections that enable user validation, alerting, reporting of content and innovative approaches for additional business process automation.  

Like the previous examples, knowledge mining solutions can now be implemented quickly with developer platforms like Azure Cognitive Services, Azure Search, Azure Functions and low to no-code solutions like the Power Platform AI builder and Power Automate. Additionally, knowledge mining solutions are familiar, think organizational Google.

  • Index PDF’s such as contracts, invoices and order summaries and trigger down stream business processes
  • Index Call Center interactions and automate alerting
  • Index Educational content
  • Index, link, mine and automate Healthcare data enrichment


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Recognition is a rapidly growing application in AI. We see this with the facial recognition built into our phones, image recognition on social media or selecting the capture images for multi factor authentication.

Recognition might be considered one of the most advanced of the quick and dirty solutions, but like the others, they are well documented and more and more pre-trained predictive platforms are being introduced into the world. Recognition can be revolutionary in spaces like Healthcare, Insurance claims or even Education. Developer platforms like Azure Cognitive Services and low to no-code solutions like the Power Platform AI builder can really make this a quick and dirty solution.    

Common use-cases:

  • Disease Recognition
  • Document Content Extraction
  • Person Recognition
  • Defect/Quality Recognition (Predictive Maintenance, Quality Assurance)
  • Sentiment Recognition (Student Evaluation against course material, patient deterioration, patient satisfaction)


Anomaly Detection

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The most fun case of Anomaly Detection is clearly the application of finding all Waldo’s on the planet from every book within seconds. Waldo is in fact the anomaly within the image that we’re all searching for. However, how much value does finding Waldo provide your organization……. unless you’re Martin Handford evaluating the quality of the latest installment of “Where’s Waldo”. In that case, please contact Spyglass MTG, we can help with your quality review of “Where’s Waldo 2020”. i.e. Sitting at home watching Netflix and making TikToks or getting criticized on Facebook for being on a crowded beach without a mask.  

Anomaly detention is another tried and true application of AI. Fraud modeling has been around for more than a decade but has traditionally required custom algorithms and a team of statisticians (now called data scientists). With recent introductions in Cognitive Services and open source machine learning packages these use-cases can now readily be implemented as quick and dirty solutions.

Common use-cases:

  • Fraud Detection
  • Risk Analysis
  • Forecasting exclusion – Don’t include in the forecast because it was an anomaly
  • Predictive Maintenance – Vibrations from mechanical parts


Are you an optimist yet?

A recent Gartner survey of global CIOs found that only 4% of respondents had deployed AI. Gartner estimates that by 2020, AI will be a priority for more than 30% of CIOs. 

In a Harvard Business Review report, Subra Bose, CEO of Financial Fabric said, “The return on investment (ROI) for knowledge mining at a small fund with one or two analysts is 30 percent to 58 percent. For much larger funds, with 50 or more analysts, it is over 500 percent.” 

AI does not have to be an afterthought, it can lead your business transformation. AI is driving transformation across industries and empowering companies to think about their business and how they engage with their customers and internally in completely new ways. Adopting a better approach to AI with Quick and Easy AI solutions can give you the kick start needed. 

Interested in learning more about AI Solutions and how we can help you? Contact us today!

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