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Artificial Intelligence 101: The Basics for Deploying AI at Work

Picture of Sam Chappell

 

This guide introduces key terminology, mechanics, capabilities and considerations relevant to business leaders seeking to deploy AI at work.

Demystifying AI for Non-Technical Professionals

Demystifying AI for Non-Technical Professionals
Image credit: DALL-E

The concept of Artificial Intelligence (AI) has long captivated the human imagination. Before it became a field of scientific inquiry in the late 1950s, authors and filmmakers were painting vivid pictures of intelligent machines capable of independent thought and action.

For almost 70 years, people paid little attention to AI. But with the release of ChatGPT, the hype surrounding the technology has exploded. AI now dominates the news, stock markets and political discourse with raging debates about its potential to change the world for the better, render humans obsolete, or simply fail to deliver on the hype.

To non-technical business professionals (i.e., those of us without a PhD in mathematics), these discussions often seem abstract or irrelevant. Even if you believe that AI has the potential to revolutionize the world of work, sifting through the endless barrage of information and actually capitalizing on the opportunity can feel like an overwhelming task.

This guide filters out the noise, distilling information and insights that are critical for understanding AI and successfully integrating the technology within a business.

Key Terminology

Artificial Intelligence (AI) is a branch of computer science. It deals with the creation of machine-based systems that can perform tasks normally requiring humans. Given a set of human-defined objectives, these systems can simulate intelligent behavior.

Data is the most vital ingredient of effective AI systems. Data is any information that can be processed or transferred. This includes numerical, textual, visual, and aural information. The quality, quantity, and diversity of data directly impacts the effectiveness of AI systems.

Algorithms are the sets of instructions followed by AI systems. An algorithm is the programming that tells a computer how to operate on its own.

Machine Learning (ML) is an application of AI in which a system is designed to automatically learn and improve from experience. ML models are trained on existing datasets and can make useful predictions or generate content when new data points are introduced to the system.

Deep Learning is a powerful subset of ML methods inspired by the human brain. It uses artificial neural networks – many layers of interconnected systems – to enable processing of more complex tasks, such as:

  • Computer vision: extracting insights from images and video (e.g., facial recognition)
  • Speech recognition: capturing and analyzing human speech (e.g., digital assistants)
  • Natural language processing: processing and interpreting text data (e.g., chatbots)

Simon J.D. Prince - Understanding Deep Learning
Image credit: Simon J.D. Prince ('Understanding Deep Learning')

Large Language Models (LLMs) are deep learning models that are pre-trained on vast amounts of data. They can understand the relationships between words and phrases and extract meaning from a sequence of text. LLMs are incredibly flexible, with a single model capable of multiple tasks, such as answering questions, summarizing documents, translating languages or completing sentences.

Generative AI (GenAI) models are capable of generating text, images and a variety of other data that has similar characteristics to their input training data. GenAI models are often guided by user prompts, the quality of which greatly affects the quality and accuracy of output content.

Machine Learning Methods

A high-level awareness of AI training methodologies is useful for understanding what AI can and can’t do. The machine learning methods used to train AI systems can be divided into three main areas: supervised, unsupervised and reinforcement learning, each of which has unique applications and mechanisms.

Supervised Learning

Supervised learning relies on labeled datasets to teach models to predict outcomes.

By analyzing examples with known answers, the model trains itself by iteratively making predictions and adjusting to minimize error. In this way, the model learns how to infer the "correct" response from new, unseen data.

This approach is key for applications like fraud detection, where the system discerns patterns by comparing transactions against a historical database of fraudulent and legitimate activities.

Supervised Learning - Google - Introduction to Generative AI
Image credit: Google ('Introduction to Generative AI')

Unsupervised Learning

In contrast, unsupervised learning analyzes unlabeled data to uncover hidden patterns or characteristics, without having been given any prior instructions.

This method excels in tasks that require segmenting data into distinct groups, such as for targeted marketing campaigns where there is a need to identify similarities and differences among customers without prior categorization.

Unsupervised Learning - Google - Introduction to Generative AI
Image credit: Google ('Introduction to Generative AI')

Reinforcement Learning

Reinforcement learning is used to train models to make sequences of decisions in highly complex environments.

Similar to training a pet with treats, algorithms use reward-and-punishment as they process data and work towards a desired objective; actions that move the system closer towards your goal are reinforced, while actions that detract from the goal are ignored. Systems learn from the feedback of each action and self-discover the best processing paths to achieve optimal outcomes, backtracking and incorporating delayed gratification when necessary.

This technique shines in dynamic environments requiring adaptation and optimization over time, such as in autonomous vehicle navigation or optimizing trading strategies in financial markets.

MathWorks - What Is Reinforcement Learning
Image credit: MathWorks ('What Is Reinforcement Learning?')

Potential Business Applications of AI

AI has the capability to fundamentally change how work is done. The University of Pennsylvania predicts that "approximately 19% of workers may see at least 50% of their tasks impacted" by the introduction of LLMs.

With such a dramatic shift taking place, businesses risk getting left behind unless they pivot to reimagine internal processes that will foster innovation, drive efficiency and enhance customer experiences.

Josh Kaufman's '5 Parts of Every Business' is a useful framework for analyzing a company and assessing opportunities for improvement. Although every organization differs in its specific operations, the following examples should help to spark ideas about how AI could be applied within your business.

1. Value Creation

Discovering what people need or want, then creating it.

  • Product Development: AI-driven analytics can identify market gaps and customer preferences, guiding the development of new products tailored to meet emerging needs.
  • Process Optimization: Machine learning algorithms can streamline operational processes, reducing waste and enhancing efficiency in production.
  • Customer Experience: Chatbots and virtual assistants powered by AI can improve engagement and satisfaction by offering 24/7 personalized customer service.

2. Marketing

Attracting attention and building demand for what you've created.

  • Content Creation: AI tools can drive engagement by generating creative content, such as articles and videos, tailored to the interests of a specific audience.
  • Market Analysis: AI systems can sift through vast amounts of data to identify trends and insights, enabling companies to make data-driven decisions about their marketing strategies.
  • Targeted Advertising: AI algorithms analyze consumer behavior to deliver personalized ads, increasing conversion rates through more effective targeting.

3. Sales

Turning prospective customers into paying customers.

  • Predictive Selling: By analyzing past purchase data, AI can predict future buying behaviors, helping sales teams to focus their efforts on the most promising leads.
  • Price Optimization: AI can dynamically adjust prices based on demand, competition, and customer profiles to maximize revenue.
  • Customer Relationship Management (CRM): AI-enhanced CRM systems can provide sales teams with actionable insights into customer needs and preferences, fostering more effective communication and relationship building.

4. Value Delivery

Giving your customers what you've promised and ensuring that they're satisfied.

  • Personalization: AI enables the customization of products and services at scale, meeting individual customer needs and enhancing satisfaction.
  • Quality Control: Through image recognition and machine learning, AI systems can identify defects or anomalies in products, ensuring high quality.
  • Supply Chain Management: AI can optimize supply chain logistics, predicting and mitigating risks, and ensuring timely delivery of products.

5. Finance

Bringing in enough money to keep going and make your effort worthwhile.

  • Financial Forecasting: By analyzing market trends and company data, AI can forecast future financial performance, aiding strategic planning.
  • Cost Reduction: AI-driven automation of finance operations can significantly reduce administrative costs and errors, improving the bottom line.
  • Fraud Detection: AI systems analyze transaction patterns to identify and prevent fraudulent activities, protecting the company's assets.

Business Considerations for Implementing AI

The number of tools vying to address these opportunities is enormous. At the time of writing, There’s an AI for That (an AI tool aggregator) lists "12,236 AIs for 16,604 tasks and 4,847 jobs". These numbers do not include the millions of existing software applications that are currently integrating AI into their feature sets.

Picking tools at random and hoping for results is a recipe for failure. To ensure the successful implementation of AI within an organization, business leaders must consider the following points.

  1. Define Clear Objectives: Begin with the end in mind. Identify specific problems AI can solve or areas where it can add value, ensuring these align with your overall business strategy.
  2. Set Performance Benchmarks: Define metrics for success early on. Tracking performance against specific, quantifiable goals is critical for measuring the ROI of AI projects.
  3. Start Small, Think Big: Pilot projects can demonstrate value and reveal practical challenges. However, scalability is key. Design AI solutions not just for pilot success but with a roadmap for scaling across the business.
  4. Invest in Data Infrastructure: The foundation of AI is data. Data must be accurate, relevant and consistent to produce reliable results. Investing in systems and infrastructure built for quality, accessibility, and scalability is essential for long-term success.
  5. Integrate with Existing Systems: AI doesn't operate in isolation. Plan for integration challenges with existing systems and workflows. Seamless integration will enhance user adoption and maximize the value of AI investments.
  6. Manage Change: AI implementation is as much about people as it is about technology. Prepare your organization for change with clear communication, training, and support. Address fears and build confidence in how AI will enhance, not replace, human capabilities.
  7. Ethical Considerations and Bias: AI systems can unintentionally perpetuate bias. Establish ethical guidelines and use diverse data sets to train models. Regular audits for fairness and transparency are essential to maintain trust and comply with regulations.
  8. Regulatory Compliance and Data Privacy: Stay abreast of regulations governing AI and data privacy in your industry and region. Implement robust data governance and compliance frameworks to protect your business and your customers.
  9. Stay Informed and Flexible: The AI landscape is evolving rapidly. Stay informed about new technologies, methodologies, and best practices. Be prepared to pivot your strategy as the industry evolves, ensuring your AI initiatives remain aligned with the latest advancements and opportunities.

Additional Resources

There are abundant resources available to those who wish to continue learning about AI. These are just a few that I have found valuable – and most are entirely free!