Introduction to AI & Automation
AI technologies continue to expand their capabilities to perform human tasks, gaining interest from companies that seek to invest and embark on a transformational journey to improve the efficiency and effectiveness of their operations, products, and services. Before adopting these new technologies in their organizations, it is critical for leaders to understand what AI and automation can do, the benefits they provide, and the challenges / limitations that can be encountered. Studying the successes and failures of early adopters provides a blueprint for preparing a roadmap to enable a successful transformation.
Key Takeaways:
- General principles of automation (i.e. what it is vs. what it is not)
- The difference between AI and automation
- The current state of AI and automation in the enterprise
- Differentiators for successful AI and automation enabled transformations
- How to prepare a roadmap that will ensure a successful transformation
Technology Platforms
The need for AI and automation is growing due to the common need for companies to innovate and disrupt their way of business to increase market share. There are a variety of AI and automation software, services, and solutions available in the market. While it is important to pick the right technology for the job, the combination of solution providers and skilled resources is central to a successful transformation. Understanding the technology and resource skill set will further support the development of a roadmap that drives the transformational journey.
Key Takeaways:
- The types of automation and AI technologies in the market
- Common industry terminology
- How to identify the correct technology and vendor software
- How to further refine the roadmap to achieve AI and automation goals
Data Analytics
Data is the fuel of AI. Artificial Intelligence projects do not follow the same precepts as traditional programming projects. While they can use similar agile processes, they are not designed to add features and functions as they mature, but rather to use data to improve accuracy. Learn how to identify data readiness for your potential AI project and to optimize the process of using data to solve the problem.
Key Takeaways:
- Understanding the major ways to use AI and how data is used with each one
- How to identify projects whose data makes them conducive to the use of AI
- Determining the difference between data analytics and text analytics
- Exploring the process of model improvement using data
Process Optimization
AI and machine learning are general technologies which will impact every aspect of your organization. It will allow for the reimagining of work by leveraging innovative technology and applying it directly to core processes essential to your organization. Its impact is already being felt in manufacturing, distribution, transportation, professional services and healthcare. This change will only accelerate.
This module provides a framework to help you leverage AI in processes you identify as critical to your organization, create AI roadmaps, establish pilot programs and prepare your organization for change.
Key Takeaways:
- Conduct a “rapid process assessment” to identify key processes most likely to benefit from AI
- Understand the level of change AI will cause and evaluate its impact on the company
- Develop an AI roadmap that helps align the organization around change
AI for Customer Service and Customer Experience
Customer service and customer experience are increasingly important ways for brands to differentiate from the competition. The problem is that truly top-notch customer service is frightfully expensive, which is why most customer service is mediocre—or worse. AI can improve customer service and customer experience at a lower cost, which can prove a game-changer in many situations.
Key Takeaways:
- How data privacy regulations affect customer data collection
- How chatbots and smarter search answers customer questions
- How to use social media listening to predict brand health
- How to improve customer journeys
AI in Marketing and Sales
For the last 25 years, digital technology has quietly revolutionized marketing and sales. But AI might make even bigger changes in the next few years. Marketing and sales have always needed the human touch—and they still do. But AI has the ability to automate some tasks that could be performed only by people in the past.
AI won’t take away the jobs of marketers and salespeople; however, the ones who risk losing their jobs are the ones who won’t use AI themselves.
Key Takeaways:
- How to analyze data to find your best customers
- Improving site search to improve conversions
- Determining the best marketing message for each customer
- Implementing advanced attribution modeling
- How to improve lead scoring
AI for Supply Chain
Artificial Intelligence (AI), along with other advancements in data analytics, has been changing how supply chains are managed nowadays. Many executives have reported revenue increases and cost savings from AI adoption in business units. As many businesses are at the turning point of adopting AI in their supply chains, it is critical to study successful applications and key challenges. It is also helpful for business leaders to understand the people factor and risks associated with the transformation.
Key Takeaways:
- Understanding of supply chain functional areas, and their usage of data
- AI applications in supply chain functions (procurement, planning, warehousing, logistics, etc.)
- Challenges and risks of adopting AI, especially for small- and medium-sized businesses
Ethics in AI
There is no doubt that AI presents an opportunity for radical advancements in many fields such as agriculture, manufacturing, medicine, computer science and cybersecurity, to name a few. However, with each advancement comes a number of questions and concerns, ranging from business to legal to ethical implications. There is no easy answer to these questions, but they cannot be ignored.
This module will center around the advent of AI and how the business community, and any community for that matter, should view AI from an ethical and legal viewpoint.
Key Takeaways:
- Consider the ethical and legal implications of using AI systems
- Be better equipped to evaluate business decisions related to AI
- Gain a deeper understanding of the impact of bias and computational errors in AI
Program Overview
For an overview of our Mini-MBA: Artificial Intelligence program plus program benefits and outcomes, please click here.