The A-Z of Generative AI and ChatGPT - Chapter H

It’s natural to worry about a technology that mimics human intelligence.? Your job will not be replaced by Artificial Intelligence (#AI) but it will be replaced by someone who knows how to use AI.

The mass distribution of industries and roles cause by AI will trigger massive economic changes on a scale not yet witnessed by individuals, businesses and governments to date. This change will significantly impact nearly every organizations and the people that work within them.

Read this series of articles to help you understand what the impact of Generative AI will be and how to get ahead of the curve so that you and your business survives intact.

It’s hard not to catch AI 'fever' but catching a fever and understanding how you got it, are two very different things. Don't worry - I am here to explain Generative AI to you.

Why is Generative AI causing such excitement?

The practical implications and applications of Generative AI are exciting the business world too. This is the era of enterprize AI. It has immense potential to revolutionize any field where creativity and innovation are key which is a very different business shift than ever before and companies are buy into it.

Take for example, HR. Depending on who you listen to, generative AI has the potential to drive a 30% increase in HR productivity. Add in Intelligent Automation and a change in work practices and this can be doubled.

Are companies excited? Heck yes.

For example, Bain found 40% of companies surveyed are already diving into generative AI. Up to 60% of software CTOs and engineering leaders actively rolling out coding assistants based on foundation models and Generative AI. And most importantly, 75% of Generative AI coding assistant adopters say it has met or exceeded their expectations highlighting the fact that this technology works.

Generative AI is a technology that will see knowledge-based roles achieve the greatest leap in supercharged productivity for decades regardless of industry

or the functional role you consider.

But, and there is a but, generative AI can and will impact knowledge roles in ways that may not please. For example, Amazon CodeWhisper claims to make coders up to 57% more productive. If that is the case, then do I need as many coders if everyone is immediately better than before?

Maybe, maybe not.

Generative AI opens the opportunity for the creative destruction of business architectures and more. For example, if a business employs a person and they use generative AI to get their work outcomes delivered in 1/3rd of their working day what might that mean?

Well it could mean that their manager, and the company, is delighted as they are getting the outcomes they paid that individual to deliver.

Alternatively that same individual could take on 3 roles and work multi-portfolio, and by doing so, they could maximize their individual life-time earnings.

Or the shareholders of a company might decide to retain only 1/3rd of the current staffing numbers as each staff member is capable of delivering 10x or 100x their previous output.

Alternatively, they may adopt an abundance mindset and use the now freed 2/3rds time to enter new markets.

The thing to recognize is that, some, any or all of these are now possible given that Generative AI and Intelligent Automation are at their current level of sophistication.

How should you react? Keep reading and find out.

"The recent advancements in Gen AI are leading to the early narrative being very similar to the one early in the RPA explosion and is being driven primarily by the cost takeout potential. As we absorb the possibilities (and the hype) related to Generative AI, we need to make sure that we account for lessons from previous intelligent automation implementations - especially RPA:

To start, realistic expectations must be set for the pace of implementation and impact. To do it and do it well, you need a bigger plan than just hitting the "low-hanging fruit". Intelligent automation implementation is best viewed as a program and not a project and should be considered as such from the start.

Next, history has shown that the best automation implementations and programs weren't dominated by cost takeout as equalling value, and the best Gen AI programs in the future will be the same. Many early adopters' satisfaction waned when the predetermined cost takeout value-driver ran out. Early efforts needed forethought in sustainable value, and not enough effort was put into reflecting on the long-term value obtained from the technology.

Also, the need for proper control and governance may be even more important when the ease of access is as substantial as it is for Gen AI. The average person was not likely to download the free version of an RPA software and bring what they've done to put into production at work. Millions of people have already started "playing" with things like ChatGPT, and a few have tried to use it in their work life with less than positive outcomes. Acceptable use policies. Data quality, governance approaches, and utilization training will be vital to successful implementation and use.

The other thing to remember is that corporate adoption of technology can take a lot of time and effort to be successful. For newer technologies, the agility needed to work through the bumps and bruises can be difficult in larger organizations – even those who view themselves as "agile". On the developing technology front, and specifically within the healthcare space where growing pains can be impactful to more than just the business outcomes, organizations may want to consider investments through their innovation efforts in one of the many start-ups popping up in the space that have the size and flexibility to "fail fast and learn quickly".

A.J. Hanna , Sr. Director, Customer Development – Healthcare

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A digital revolution was already underway. Now this has been turbocharged with the release of Generative AI. We no longer need to be data or computer scientists to change the world. Now it is not a case of when leaders need to get the ball rolling on Generative AI - that should already have happened. Start fast and start now!

How can leaders get the ball rolling?

AI will be core to every industry in the near future, and those that merely pause to take stock might never unwind the head start they gifted to their rivals. Companies across industries have already started to innovate with this new technology - so you will need to run hard to catch up. It is a key time to learn lessons - a wait-and-see approach is particularly high risk for any business today.

Do not try and build a sophisticated business case out of the blocks - instead begin, experiment, learn and iterate. Businesses should start with highly focused, low-risk generative AI use cases, generating the funds and experience for more transformative future applications of the technology.

Whilst you many find some applications of Generative AI that may not be possible today, it will be in 6 to 12 months. The capabilities of today compared to two years ago are massive. So, imagine what things will be like in 2-years time? A test-and-learn approach today can develop a repeatable process that can be deployed widely tomorrow so your investment is not wasted.

Note: But do make sure you govern and risk manage what you are trying to do.

There are many questions surrounding data privacy, IP protection and more that still need solved for. So, engaging your risk team from day 1, is always a smart move.

Keen to learn more? Then keep reading.

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1. Hurdles in Generative AI Adoption ??

Hurdles in Generative AI adoption refer to the challenges and barriers that businesses may encounter when integrating this technology into their operations. Like overcoming obstacles in a race, addressing these hurdles is essential to fully harness the benefits of Generative AI.

Real-World Example: A healthcare organization faces hurdles in Generative AI adoption due to concerns about data privacy and regulatory compliance. By implementing robust data protection measures and adhering to regulations, they can safely leverage Generative AI for medical image analysis and diagnostics.

Example Prompt: "ChatGPT, address the hurdles in Generative AI adoption for our marketing team. Provide insights on overcoming challenges related to data availability and model reliability."

2. Human-in-the-Loop in Generative AI ??

Human-in-the-Loop in Generative AI is an approach that combines human expertize with AI capabilities. It's like having an AI-powered assistant collaborating with human decision-makers. Human-in-the-Loop ensures that AI-generated content is reviewed, refined, and validated by human experts, increasing the overall quality and accuracy.

Real-World Example: A content creation platform employs Human-in-the-Loop in Generative AI to verify and approve automated content before publishing. Human editors review AI-generated articles to ensure accuracy and compliance with editorial guidelines.

Example Prompt:"ChatGPT, demonstrate Human-in-the-Loop in Generative AI for our content moderation process. Assist human moderators in reviewing user-generated content to maintain platform integrity."

3. High-Dimensional Data in Generative AI ??

High-Dimensional Data in Generative AI refers to datasets with numerous features or dimensions. It's like working with multi-dimensional puzzles, where the complexity increases with the number of pieces. Generative AI models must handle such data efficiently to generate meaningful content.

Real-World Example: A retail company uses Generative AI to generate product recommendations based on customer preferences, historical purchase data, and other relevant factors. The high-dimensional data ensures personalized and accurate product suggestions.

Example Prompt: "ChatGPT, tackle high-dimensional customer data to create personalized shopping experiences. Generate tailored product recommendations by considering multiple customer attributes."

4. Hyperparameter Tuning in Generative AI ??

Hyperparameter Tuning in Generative AI involves finding the optimal settings for a model to achieve the best performance. It's like fine-tuning the strings of a guitar to produce the perfect sound. Hyperparameter tuning improves the generative model's output and ensures it aligns with the desired outcomes.

Real-World Example: A music streaming service fine-tunes hyperparameters in Generative AI to create personalized playlists for users. By adjusting model parameters, the service curates playlists that match individual music preferences.

Example Prompt: "ChatGPT, optimize hyperparameters to generate accurate product descriptions for our e-commerce platform. Fine-tune the model to produce compelling and persuasive content."

5. Human-Like Text Generation in Generative AI ??

Human-Like Text Generation in Generative AI aims to produce written content that closely resembles human-written text. It's like having an AI author that can pen compelling narratives indistinguishable from those written by humans. This capability enhances the overall quality and acceptance of AI-generated content.

Real-World Example: A publishing house uses Generative AI for drafting book summaries. The AI generates concise and engaging summaries that capture the essence of the story, appealing to potential readers.

Example Prompt: "ChatGPT, focus on human-like text generation to produce gripping content for our marketing campaigns. Craft persuasive copies that resonate with our target audience."

6. Handling Unstructured Data with Generative AI ??

Handling Unstructured Data with Generative AI refers to processing data that lacks a predefined format or structure. It's like organizing a pile of scattered puzzle pieces to form a coherent image. Generative AI can process unstructured data, such as images or text, to generate valuable insights and content.

Real-World Example: A social media analytics platform uses Generative AI to extract insights from unstructured data, such as user comments and posts. The AI summarizes trends and sentiments, providing actionable information for businesses.

Example Prompt: "ChatGPT, analyze unstructured customer feedback data to generate actionable insights for our product development team. Extract sentiments and preferences from unstructured text data."

7. Hybrid Approaches in Generative AI ??

Hybrid Approaches in Generative AI involve combining multiple AI techniques or models to achieve more powerful results. It's like assembling a toolkit with diverse tools for different tasks. Hybrid approaches allow businesses to leverage the strengths of different Generative AI models for specific content generation tasks.

Real-World Example: A chatbot service implements a hybrid approach in Generative AI by using rule-based models for structured queries and neural networks for open-ended conversations. This results in a more comprehensive and effective chatbot experience.

Example Prompt: "ChatGPT, employ a hybrid approach to generate responses for our customer service chatbot. Use rule-based models for common queries and neural networks for more complex interactions."

8. Heterogeneous Data in Generative AI ??

Heterogeneous Data in Generative AI refers to datasets with diverse data types or sources. It's like combining different puzzle pieces from various sets to create a unified picture. Generative AI models need to handle heterogeneous data to generate coherent and contextually relevant content.

Real-World Example: A smart city project uses Generative AI to predict traffic patterns, combining data from sensors, social media, and GPS devices. The AI model generates real-time traffic predictions for efficient city planning.

Example Prompt: "ChatGPT, process heterogeneous customer data to create personalized marketing offers. Combine data from multiple sources to generate content that suits each customer's preferences."

9. Hardware Acceleration for Generative AI ??

Hardware Acceleration for Generative AI involves using specialized hardware, like GPUs or TPUs (Tensor Processing Units), to speed up the computation-intensive tasks of generative models. It's like using a high-speed engine to optimize a vehicle's performance. Hardware acceleration improves the efficiency and speed of content generation.

Real-World Example: A video game development studio employs hardware acceleration for Generative AI to create lifelike animations and character movements. This results in more realistic and immersive gaming experiences.

Example Prompt: "ChatGPT, leverage hardware acceleration to enhance your language generation capabilities for faster and more responsive conversational AI."

10. Handling Rare or Novel Content with Generative AI ??

Handling Rare or Novel Content with Generative AI refers to the ability of models to generate content that is not present in the training data. It's like an AI-powered chef inventing a unique recipe that hasn't been tasted before. Generative AI should be able to create original content to accommodate novel situations.

Real-World Example: An anomaly detection system uses Generative AI to identify rare or novel patterns in data. The AI model generates predictions based on unusual occurrences, enabling early detection of anomalies.

Example Prompt: "ChatGPT, demonstrate your ability to handle rare customer queries by generating responses for our customer support team. Address novel situations to provide accurate and helpful responses."

11. Health-Centric Applications of Generative AI ??

Health-Centric Applications of Generative AI involve leveraging AI models to improve healthcare services. It's like having a medical assistant that can analyze patient data and assist with diagnostics. Generative AI contributes to personalized treatment plans, medical image analysis, and drug discovery.

Real-World Example: A healthcare provider uses Generative AI to generate patient-specific treatment plans based on medical records, genetic data, and lifestyle information. The AI model tailors treatment options for better patient outcomes.

Example Prompt: "ChatGPT, focus on health-centric applications by generating medical case reports for our research team. Analyze patient data to assist with clinical research."

12. Human-Centered Generative AI Design ??

Human-Centered Generative AI Design emphasizes the ethical and responsible use of AI, keeping human interests and well-being at the forefront. It's like having an AI system that respects and protects user privacy and rights. Human-centered design ensures that Generative AI is applied responsibly, with a focus on transparency and fairness.

Real-World Example: A financial institution adopts Human-Centered Generative AI Design in developing credit risk models. The AI model ensures fair lending practices, avoiding biases that could discriminate against certain applicants.

Example Prompt: "ChatGPT, showcase human-centered design in Generative AI by ensuring your responses adhere to our ethical guidelines. Focus on providing helpful and unbiased information to users."

Understanding the A-Z of Generative AI opens up a rich world of possibilities for business leaders. These concepts provide valuable insights into how AI can create new content, solve problems, and drive innovation across various industries. Embracing Generative AI can lead to enhanced creativity, improved decision-making and a competitive edge in the rapidly evolving data infused digital landscape.

Every single role you can think of will be impacted by Generative AI. In fact understanding how to interact with ChatGPT will soon be an essential key skill.?

Below are 5 roles that begin with the letter H with an example prompt each massively boosting role productivity.

Human Resources Manager. Human resources managers oversee recruitment, employee relations, benefits administration, and ensure compliance with labour-laws and company policies.

• Prompt: Develop a training program for new managers focusing on effective leadership and employee engagement.
• Prompt: Conduct a thorough review of the current performance evaluation process and propose a more streamlined and efficient system.

Health Care Administrator. Health care administrators manage the day-to-day operations of medical facilities and ensure the delivery of quality health care services.

• Prompt: Implement a new electronic health records system to improve data accuracy and patient care coordination.
• Prompt: Develop a disaster preparedness plan to ensure the facility's readiness for emergency situations.

Hospitality Manager. Hospitality managers oversee the operations of hotels, restaurants, or other hospitality establishments, ensuring smooth guest experiences.

• Prompt: Develop a customer feedback system to gather insights and improve the overall guest satisfaction.
• Prompt: Implement a staff training program to enhance service quality and guest interactions.

Human Rights Advocate. Human rights advocates work to promote and protect human rights through advocacy, education, and policy initiatives.

• Prompt: Create a social media campaign to raize awareness about a specific human rights issue.
• Prompt: Develop a strategy to engage with policymakers and promote human rights legislation.

Hydrologist. Hydrologists study water distribution, movement, and quality in various environments to address water-related challenges.

• Prompt: Conduct a hydrological analysis to assess the impact of land use changes on local water resources.
• Prompt: Implement a monitoring system to track water quality parameters in a specific watershed.

ChatGPT can be used in multiple ways by many, many people. If you still doubt this after all reading all of the above examples, then lets take one thing we all do daily - send and receive emails - and apply Generative AI.

Like it or not, email is a part of modern business. In fact McKinsey and HBR both suggest that the average professional spends 28% of their working day reading and answering emails.

Generative AI can already give your workers excellent email writing skills as ChatGPT can already suggest words, organize, copywrite, grammar check and drafts emails message whilst also setting the tone you want it to convey.

• Google’s Duet AI can now write your emails for you

Prompt: "Write an email announcing a company-wide internet policy change, emphasising its benefits and address any potential concerns in a formal tone. This is the original policy test ['COPY IN TEXT'] and here is the new policy ['COPY IN TEXT']"

You can of course use much more complex email prompts. For example, when you know how, you can use ChatGPT to 10x your email sequences using powerful behavioural psychology techniques to massively boost your sales (e.g., social proof, reciprocity, anchoring).

So, if Generative AI can augment 30% of a typical workers week - what happens when it is clever enough to answer most, if not every, email automatically?

Think this through for a moment. What happens to us when Generative AI learns to comprehensively answer emails, slacks, books meetings, searches and gathers information, as well as helps us communicate and collaborate internally i.e., 61% of the average professional workers tasks?

The transformative power of such advancements could reshape the way we work, challenging traditional roles and prompting us to reconsider the very nature of productivity and collaboration in the workplace.

Watch this space carefully!

Some of the best Generative AI articles from some of the best sources on the internet.

• BCG How Generative AI Will Transform HR
• HBR Boost Your Productivity with Generative AI
• Bain Beyond Hype: Getting the Most Out Of Generative AI in Healthcare Today
• McKinsey Tackling healthcare’s biggest burdens with generative AI | McKinsey
• HBR Generative AI Will Change Your Business. Here’s How to Adapt.
• ERP Today?Generative AI for the enterprise ?-?Pascal BORNET ?and?Kieran Gilmurray
• Generative AI: It’s All A Hallucination! | by Bill Franks | Analytics Matters | Medium

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Who am I?

I am a senior executive with 28+ years of experience leading digital programs?and the author of the book “The A-Z of Organizational Digital Transformation.”?I have been a director, board member, research fellow, and advisor to multiple international companies.

Please find me?on social LinkedIn ?|?Kieran Gilmurray ?|?Twitter ?|?YouTube

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'Kieran is an exceptional technologist, automation expert, and skilled at AI, Data Analytics, and Decision Insight. His business and technical knowledge are second to none. If you or your business want to achieve your goals, then connect with Kieran'

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