An AI Primer for the Non-Technical Executive

As AI continues to capture our imagination – creating both excitement and fear about the possibilities – business leaders are now forced to grapple with the question of “How can AI help my organization?”

That’s a challenging question for the non-technical executive. The obvious use cases are the ones that most of us have been exposed to this year – Chat-GPT textual generation and chatbots for customer service.

Since the rest of artificial intelligence at the business level is typically not clearly understood, gaining a high-level understanding of the fields is a good starting point. Let’s start by defining AI:

• Brookings Institute: AI generally is thought to refer to “machines that respond to stimulation consistent with traditional responses from humans, given the human capacity for contemplation, judgment, and intention.”  
• Layperson: AI is a branch of computer science focused on building smart machines and programs capable of performing tasks.
• An even simpler definition: AI are computer programs that can “think.”

Currently, AI cannot think and reason on a variety of different tasks (that it’s not trained for) like a human.

AI Subfields

AI works by combining large amounts of data with fast, iterative processing and intelligent algorithms, allowing the software to learn automatically from patterns or features in the data.

Here's an overview of the key areas (courtesy of ChatGPT):

1. Machine Learning (ML):

• Definition: ML is a core part of AI that involves algorithms learning from data, identifying patterns, and making decisions with minimal human intervention.
• Subcategories:

• ~Supervised Learning: Learning from labeled datasets to make predictions or classifications.
• ~Unsupervised Learning: Identifying patterns in data without any labeled outcomes.
• ~Reinforcement Learning: Algorithms learn to make decisions by trial and error to achieve a certain goal.
• ~Deep Learning: A subset of ML based on artificial neural networks, particularly effective in processing large amounts of unstructured data like images and text.
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2. Natural Language Processing (NLP):

• Focus: Understanding, interpreting, and generating human language.
• Applications: Speech recognition, language translation, sentiment analysis, chatbots, and text summarization.
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3 Computer Vision:

• Focus: Enabling machines to interpret and make decisions based on visual data from the physical world.
• Applications: Image recognition, object detection, facial recognition, and video analysis.

4. Robotics:

• Focus: Designing and building robots often equipped with AI, enabling them to perform tasks autonomously or semi-autonomously.
• Applications: Industrial automation, medical robots, drones, and self-driving vehicles.

5. Expert Systems:

• Focus: Mimicking the decision-making ability of a human expert.
• Applications: Medical diagnosis systems, financial asset management, and certain legal advisement systems.

6. Knowledge Representation and Reasoning:

• Focus: Representing information about the world in a form that a computer system can utilize to solve complex tasks.
• Applications: Semantic web, automated reasoning, and AI planning.

7. Cognitive Computing:

• Focus: Simulating human thought processes in a computerized model.
• Applications: Creating more intuitive human-computer interactions, as seen in some healthcare applications.

8. Affective Computing:

• Focus: Developing systems that can recognize, interpret, and simulate human emotions.
• Applications: Customer service bots and mental health analysis.

9. Ethics and AI Governance:

• Focus: Addressing ethical, legal, and societal implications of AI.
• Applications: Developing guidelines and policies for the responsible use of AI, ensuring privacy, fairness, and transparency.

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Automation to Solve a Problem

After learning the general categories of AI, establish the framework within your organization for exploring digital transformation. To establish the framework, consider:

• Challenging legacy assumptions and systems
• Encouraging creativity in re-thinking processes, roles and functions
• Fostering an agile-thinking culture, and a “think fast and break things” approach for tackling the art of the possible
• Focusing on rapid prototyping instead of waterfall development
• Preparing to update your data / systems into the modern cloud infrastructure that can leverage AI at scale
  

Finally, identify the key areas of the business that can benefit from optimization and automation. The type of AI that can help one department of a company might be very different than another.  

• A creative agency can use generative AI to write short video scripts, articles, images or videos.
• A legal office could train their own LLM (large language model) on cases or research to be able to quickly deliver answers to their researchers.
• A medical facility could use computer vision to detect anomalies in X-rays, MRIs or in-body videos.
• An online pilot training company could use generative AI to create custom training flight videos as part of their curriculum.
• A medical device company could use machine learning to uncover the physicians who are the best fit to use their new medical device.
• Amazon and manufacturing companies use robots to move inventory and build products.
• A transportation company could create simulations of traffic flows and see how they react to different variables.

Start by focusing on the tasks / functions to optimize – not the technology to do it. Innovations are occurring rapidly, and technology that’s not yet commercially available today might be available in six months.

Once this is complete, connect with people who are experts in deploying AI to evaluate how effective AI could be, and the cost/benefit of the automation.

At Rehinged, our expertise is in machine learning and natural language processing to extract intelligence from data at scale. Connect with us if you’d like to learn more about how we’re automating intelligence.