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AI-Driven Transformation: Imagining the Future of Business

Whether it be automating tasks, providing insight and foresight for decision-making, enhancing solutions and services with new capabilities, or driving innovation across the entire business, it is no secret that artificial intelligence, specifically machine learning, is rapidly changing the way we live and work. Businesses across many industries are making use of artificial intelligence to improve their competitive advantage and better serve their customers and employees, from healthcare, finance, retail, and more.

While the barrier to entry may be lowered for business use, many businesses continue to struggle in their adoption of AI capabilities for various reasons. One of the most common reasons is a lack of understanding and comfort with artificial intelligence. In addition, organizations struggle to fully understand the capabilities, limitations, and how to calculate their return on investment (ROI) of AI initiatives. As a result, it becomes increasingly difficult to know how to begin implementing and using the technology.

To make this more difficult, businesses are also expected to decipher what is hype versus what is reality. It is no secret that some media outlets tend to over-embellish what current-state AI algorithms can accomplish (e.g., the latest preview of OpenAI’s ChatGPT), whereas others tend to only focus on extremely common applications. In addition, many neglect discussions around how AI is implemented in specific industries, how these solutions are governed and secured, and how to manage risk and responsible use, instead only focusing on the latest hype.

ConvergeOne feels it’s important to teach you how to become an expert in identifying patterns within your business and what it means to adopt an experimental mindset. The real value of AI may lie in a less common application that is specific to your business, not those commonly seen in the news or social media. Below are some examples of impactful but lesser-known AI use cases:


Medical Equipment Predictive Maintenance

Leverage product specifications, system logs, and sensor data to identify anomalies and predict future failures of medical equipment. Additionally, incorporate decision science methods to assign and schedule maintenance personnel while minimizing the associated maintenance cost.

Personalized Treatment Plans

Using Electronic Health Records (EHRs) and treatment procedure data, generate personalized treatment plans based on a patient’s medical history and diagnosis similarity. Include decision science methods to optimize the treatment plans around an objective (ex. minimizing the number of doses).

Identify and Minimize Physician Burnout

Leveraging physician Electronic Medical Record (EMR) notes, and other available conversational information, develop methods to detect physicians at risk of burnout/fatigue before it becomes more severe.


Automate Loan Approval

Predict the probability of a borrower to make on-time loan payments, further providing support or automation in approving or denying a loan application. Use prediction intervals to determine the highest loan amount while maintaining the lowest risk to the business.

Bankruptcy Risk & Forecasting

Using a mixture of past and present internal and external data, and using econometrics measures, develop models to better forecast and identify areas of risk that contribute to a higher probability of bankruptcy. Make use of model simulations to make better decisions to lower business risks.

Stock Portfolio Selection/Optimization

Leverage data on historic portfolio returns, machine learning algorithms, and simulations to improve the selection of new stocks and optimization of existing portfolios.

These use cases above only cover a few relating to healthcare and finance. There are dozens of real-world applications that apply to every industry, with many just waiting to be discovered.  

When identifying a business problem that might be a potential candidate for an AI solution, look at the problem as a prediction problem. Have you seen an increase in customer churn? The problem solution could be to predict when a customer may churn before it occurs. Maybe your business wants to alert the authorities and lock doors before someone enters the building holding a weapon. The solution may involve predicting if someone is holding a weapon and what the weapon is.

With AI, the sky truly is the limit for businesses that look to optimize, grow, and develop their competitive advantage.

To help our customers navigate this exciting domain, ConvergeOne offers an AI Envisioning Workshop to help understand the current state of AI capabilities and limitations and uncover real opportunities that make a positive impact using AI. This workshop uses a mixture of education, demos, and group breakout exercises to help your teams practice identifying and solving real problems using AI. Key topics that we address in our AI Envisioning Workshop include:

  • Introduction to AI (Machine Learning/Deep Learning)
  • Overview of the AI landscape and various applications
  • Current trends, advancements, and navigating the hype
  • Identify, define, and present potential AI-driven solutions (breakout sessions)
  • AI ethics, responsibility, and governance
  • Developing an AI strategy

If you are interested in taking the next step in making AI happen, schedule a workshop here.

Still have questions and want to talk with an expert? Contact us for a no-obligation 30-minute conversation.

Coming next month: Adoption of Responsible AI Practices

Know Your Customers and Business Better Through Data-Driven Insights

ConvergeOne’s AI Envisioning Workshop is your first step to viewing your business as a data power plant – fueling many areas of growth. Schedule your workshop
About the author:
Ben Prescott has spent the last 15+ years working in various technology roles, with 10 years specific to cloud consulting. Serving as ConvergeOne’s Analytics Practice Manager, he is responsible for growing and leading a team of senior data professionals who help organizations uncover value in their data. Ben holds a master’s degree in data science from Northwestern University, multiple cloud certifications, and is a Microsoft Certified Trainer.