The A-Z of Generative AI and ChatGPT - Chapter 4

Hardly a day goes by?without some new Generative AI development surfacing in the media. CEOs have a tough but crucial role in understanding the signal from the noise to capture significant value for their enterprises by investing in the right Generative AI technologies.

With Generative AI being one of the fastest-growing technology categories ever, business leaders cannot afford to delay defining and shaping their enterprises Generative AI strategy.

If businesses are not already investing in developing AI super powers, they can be certain that their competitors already are.

Business leaders need to run simply to keep up. This series helps everyone learn what they need to know about Generative AI to stay ahead.

Enjoy.

Generative AI, a subset of AI, has generated more interest than the launch of the iPhone and the World Wide Web combined in a much shorter time. For example, it took ChatGPT less than 5 five days to get a million users and reached 100 million users just two months after launching.

Why is Generative AI causing such excitement?

Because of its transformative potential. And I don't just mean its ability to help save lives, provide real-time personalisation and conversation, predict significant events, and more from data. Traditional AI models could already do that.

Traditional AI is narrow. It performs a single or specific task, such as processing a customer churn model. In comparison, Generative AI is broad and can generate new images, music, speech, code, video, or text.

It is a technology that people are excited to use as it enables them to search, work, learn, and engage in a human-like way that is more productive and creative.

In addition, having API or screen-based access to large 'out-of-the-box' pre-trained foundational Large Language Models (LLMs) has opened affordable access to AI to the masses. Google, Bing, Bard, Meta, OpenAI and others have spent billions of dollars training foundational models so you don't have to.

This is extraordinary as it enables businesses to benefit from a low-margin cost structure that can now leveraged to get more of their work done. And they are doing so at an almighty pace since the releases of OpenAI's ChatGPT and other Large Language Models.

Automated Intelligence 2.0

With LLMs openly available businesses can now focus on developing 'last mile' generative AI technology applications or use cases with the largest upfront cost already borne by others e.g., OpenAI, Google, or Meta. Where prior automation technology focused on physical work, the explosion in generation AI means that the automation of knowledge work is now in 'affordable' sight.

We are entering a new era of software and business - Intelligence 2.0 - where generative AI's capabilities and applications are limited only to our imaginations.? If you can think it and describe then generative AI can (or will) be able to create it.

Professionals in fields such as translation, creative writing, marketing, law, technology, health and the customer service industries are likely to see significant parts of their jobs automated sooner than anyone previously expected.

The practical implications and applications of generative AI are exciting the business world. 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 when AI is combined with Robotic Process Automation (RPA) and Intelligent Automation, it will improve businesses operational efficiency, cost structures, productivity, and business decisions making immensely.

Now that is exciting.

Let's take one example of how generative AI and intelligent automation will useher in a new and better era of organizational decision making. Poor decision-making has a significant financial impact on individuals, organizations, and the global economy. ?Harvard Business Review have found that an organization’s financial performance correlates 95%[1] with the effectiveness of its decisions.?

A McKinsey survey found[2] that ineffective decision-making costs the average S&P 500 company 530,000 days of managers time, the equivalent to some $250 million in wages annually.

Signal found that 85% of executives believe an additional $4.26 trillion in revenue could be brought into the U.S. economy (almost twice the GDP of the U.K.) by applying technology to help decision-making.

As such, any improvements in decision-making capabilities could have significant financial benefits for organizations and the global economy.

What are the causes of ineffective decision-making?

There are two primary reasons for ineffective decision-making.

1. Human nature: biases, emotions, and subjectivity.
2. Environment: amount of data, time, uncertainty, and velocity.

[1] https://hbr.org/2010/06/the-decision-driven-organization

[2] https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/three-keys-to-faster-better-decisions

Bias and its impact on corporate decision-making.

In the world of corporate decision-making, biases are an all-too-common occurrence. They can stem from a variety of sources, including long-standing habits, inadequate training, suboptimal executive selection, a lack or abundance of information, uncertainty, biased algorithms, inferences based on subjective decisions, a lack of resources (time, money, or workforce), overconfidence in our decision-making capabilities, using intuition or gut feeling, and corporate culture[1]. ?

At the core, biases are a product of human nature, ingrained and often impervious to criticism[2]. They can have a profound impact on the quality of the decisions made. ?Even a small amount of bias can skew the results and lead to suboptimal outcomes.?

Many people admit they first make their decisions, and then conduct analysis or worst, cherry-pick data to justify their initial decision (called confirmation bias). ?40% of people admit discounting existing analysis that contradicts their decisions (called the discounting bias). ?

Executives have long resisted using data analytics for higher-level decision-making. They have often preferred to rely on gut-level decision-making as opposed to AI-assisted decisions. A survey by KPMG found that 67% of CEOs prefer to make decisions based on their own intuition and experience over insights generated by data analytics[3].

Our Environment - amount of data, time, uncertainty, and velocity.

It’s the best and worst of times?for decision-makers. The pace of technology development has risen sharply over the last few years[4]. ?The amount of data and the quality and diversity of information we can obtain, combined with inexpensive cloud computing means more information can be processed, faster and more powerfully than ever before.

We are asking our brains, (which evolved 100,000 years ago[5])?to cope with technology that is a few decades old or often even younger. Swelling stockpiles of data, advanced analytics, and intelligent algorithms in particular, are providing organizations with powerful new inputs and methods for making a vast array of decisions. ?According to figures from research firm ITC, the volume of unstructured data, such as images, emails, voice records, is set to grow from 33 zettabytes in 2018 to 175 zettabytes by 2025[6]. ??

Technology has become ubiquitous over the last two decades.? The confluence of our modern technological advancements and our cognitive limitations as human beings has resulted in a daily struggle to contend with the overwhelming volume of information that inundates us. Yet the demand for data driven decisions generated by technology is growing exponentially.? And this demand is being boosted by the increasing amount of data, processing power, technological advancements, and the pace of a modern economy.?

Our efforts to manage this influx of data are compounded by the need to seek out additional information, process it efficiently, and ultimately make well-informed decisions. ?Yet rather than making ‘decision-making’ easier, organizations have adopted a piecemeal mix of tools including spreadsheets, PowerPoint, planning software, data lakes, reporting systems, process automation and machine learning platforms.

Thus, making the decision-making process even more difficult because of the siloed data that is spread across their information repository. In light of these challenges, it is increasingly clear that the cognitive resources of our 100,000-year-old modern brains[7] are ill-equipped to thrive in today's rapidly evolving landscape of advanced technologies.?

Today, the vision of many organizations around the world is to create technologies that will help people (and even themselves) make more informed decisions[4]. ?

There is an urgent need for better decision-making within organizations. What organizations need are methods that combines the data-focused insight and power of artificial intelligence and machine learning with the task-focused capabilities of process automation tools.? They need solutions that connect data, decisions, actions, and outcomes.? Generative AI offers a solution to that problem of too much data.

How can generative AI improve decision making?

Generative Artificial Intelligence, serves as a transformative tool in this landscape. By leveraging AI, organizations can transcend the limitations of human cognitive biases, facilitating more objective and data-driven decision-making. Generative AI algorithms can analyze vast amounts of data, identifying patterns and insights that are often imperceptible to the human mind. This capability is invaluable in an era where data volume and complexity are escalating exponentially.

Moreover, it can simulate various decision outcomes based on different scenarios and variables, providing executives with a comprehensive view of potential results. This predictive modelling empowers leaders to make decisions grounded in evidence rather than intuition or incomplete information.

The integration of generative AI into decision-making processes ensures a more holistic approach, where decisions are not just based on past experiences or limited data sets, but on a wide array of variables and future forecasts.

In addition to enhancing decision accuracy, generative AI can significantly expedite the decision-making process. By automating the analysis of complex data sets, it reduces the time and resources traditionally required for data processing and interpretation. This speed is crucial in today's fast-paced business environment, where timely decisions can be the difference between success and failure.

Generative AI helps democratize decision-making within organizations. By providing access to sophisticated analytical tools, employees at various levels can participate in decision-making processes, fostering a culture of data driven decision making. This democratization helps in mitigating the risks associated with centralized decision-making and the biases that often come with it.

Finally, generative AI continuous learning capability means that it evolves and improves over time. As it processes more data and outcomes, it refines its algorithms, leading to progressively more accurate predictions and insights. This self-improving nature of generative AI ensures that organizational decision-making becomes more robust and reliable over time.

Conclusion.?

The success of any organization hinges on its ability to make informed and effective decisions. ?There is a distinct correlation between the way decisions are made and organization financial performance. ?So, the consequences of poor decision-making have a significant impact on individuals, organizations, and the global economy. ?There is an urgent need for organizations to make better decisions, and with intelligent automation and generative AI they might just do so.

Sources:

[1] https://www.mckinsey.com/capabilities/strategy-and-corporate-finance/our-insights/the-case-for-behavioral-strategy

[2] https://www.mckinsey.com/capabilities/strategy-and-corporate-finance/our-insights/the-case-for-behavioral-strategy

[3] https://assets.kpmg.com/content/dam/kpmg/gh/pdf/gh-2018-CEO-Outlook-report-final-low-res.pdf

[4] The Impact of Technology on the Human Decision-Making Process, Roy Darioshi and Eyal Lahav, Human Behavior and Emerging Technologies Volume 3, Issue 3, Pages 347-459.

[5] https://ecoevocommunity.nature.com/posts/29801-the-evolution-of-modern-human-brain-organization

[6] https://venturebeat.com/ai/why-unstructured-data-is-the-future-of-data-management/

[7] https://ecoevocommunity.nature.com/posts/29801-the-evolution-of-modern-human-brain-organization

Wow, a very comprehensive must read that gets to the heart of generative AI. If this doesn’t kick start an organization to take this seriously, or a career opportunity for those on the fence, nothing will.

You should break-out of this article about what you care about first and “get on it” and then, as you progress, use this content to sanity check when you overlook or where you might want to divest from the norm.

Generative AI is beyond disruptive and whilst caution should be the overarching theme for you, it must NOT stand in your way.

Francis Carden, Automation and AI thought leader, CEO, Automation Den Inc.

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. Generative AI has changed the very nature of how businesses operate and deliver value. It can 'generate' text, speech, images, music, video, and computer code in response to a command.

When that capability is joined with a business's intelligent automation capabilities and its own proprietary information then how businesses make money dramatically changes. Generative AI will drive efficiency and innovation across all industries at scale. What ChatGPT can enable will be greater than any existing software produced to date.

How should CIOs, CEOs and CEOs respond to this 'goose-bump technology?

There are eleven answers:

1. Communicate the company's stance: Quickly establish the company’s stance toward adopting Generative AI and communicate it clearly to employees. This is a transformative technology but care needs to be taken as it is rolled out to every role in the business.
2. Identify business opportunities: Identify ways to use Generative AI to massively improve business productivity, growth, and create new business models or architectures. Generative AI enables the creation of new operations models and architectures including new digital, business, and resource architectures. Its potential is far, far greater than simply adding Generative AI to a conversational AI platform. Big thinking is required and that means the board and c-suite need to be involved at every step of the journey toward a generative enterprise.
3. Have the CFO build the business case for Generative AI after you have experimented with this AI technology. Don't try to do that day 1 as this will kill experimentation. This technology needs to be experimented with, and then a robust financial costs and benefits model can be built when you understand the art to the possible with Generative AI much better.
4. Build Generative AI capabilities e.g., hire data scientists, MLOps engineers, enterprise architects, software developers, ML engineers to fine-tune models, business analysts, intelligent automation engineers, and database business administrators to name but a few roles. Invest quickly and move rapidly as your competitors are already experimenting with this technology. Proficiency does not end at hiring - ensure that career pathing in place to develop and grow talent as everyone will quickly fight for Generative AI talent like they have never done before.
5. Deliver an education program across the enterprise starting at the board and executive team so everyone knows what's possible and expected of them. Invest in upskilling key leaders with the skills and understanding they need first, then train the broader workforce, tailoring training programs by roles and proficiency levels.
6. Build a prompt library that ever member of the business can access to help kick start their ability to generative value faster.
7. Upgrade or build the enterprise technology architecture needed to integrate and manage Generative AI models. Develop a data architecture to enable access to quality data from structured and unstructured sources.
8. Create a centralized team to own and govern approved Generative AI models to product and application teams on demand and have it report to the executive leadership team. Businesses saw, when establishing Robotic Process Automation (RPA) and Intelligent Automation programs, they needed a Centre of Expertise to govern and grow automation. Don't make the mistake of leaving people to work this AI technology out for themselves.
9. Evaluate the new risk landscape and establish ongoing mitigation practices for models, data, and policies. Generative AI presents a fresh set of compliance, business, data privacy, intellectual property, and ethical risks. This does not mean you hold back - invest quickly. But do establish a governance framework to ensure that the responsible use of Generative AI - including transparency, accountability, and fairness - happens under your guidance.
10. Incentivise and reward the use of Generative AI in the business e.g., make its effective use part of everyone's objectives / KPIs, have each executive own a Generative AI value stream and report progress to their peers monthly, bonus its impact, and finally place the best employees in Generative AI expanding roles to show how important this is.
11. Collaborate with industry partners, academia, and government agencies to advance the development and adoption of generative AI.

Keen to learn more? Then keep reading.

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Then book a?FREE 30 minute introductory call?so we can discuss your specific business Intelligent Automation, Data Analytics, Artificial Intelligence, Generative AI needs -?click here.

1. Data Augmentation in Generative AI ??

Data Augmentation in Generative AI involves enhancing the diversity and quantity of training data to improve the AI model's performance. Like a photographer using different angles and lighting to capture stunning images, data augmentation introduces variations to the existing data, enabling the model to learn from a broader range of examples.

For example, an autonomous vehicle company could employ Data Augmentation in Generative AI to train its driving model. By introducing various weather conditions, road types, and traffic scenarios, the AI model becomes more robust and capable of handling diverse real-world driving situations.

Example Prompt "Use Data Augmentation in Generative AI to improve our customer feedback sentiment analysis. Introduce variations in the phrasing and structure of customer reviews to enhance the AI model's understanding and accuracy."

2. Deep Learning in Generative AI ??

Deep Learning forms the backbone of Generative AI, where complex neural networks are used to learn patterns and generate content. Similar to how a musical prodigy learns to play multiple instruments, deep learning models master intricate representations to generate diverse and high-quality content.

For example, a music production company might use Deep Learning in Generative AI to create personalized soundtracks for videos. The AI model analyses video content, extracting relevant emotional cues, and generates matching musical compositions to evoke specific moods.

Example Prompt "Utilize Deep Learning in Generative AI to compose a series of background music tracks for our advertising videos. The AI model should understand the content's context and generate suitable tunes to amplify the video's impact."

3. Differentiable Programming in Generative AI ??

Differentiable Programming allows AI models to incorporate custom functions and constraints directly into the training process. Like an agile dancer adapting movements seamlessly, this approach enables AI models to optimize content generation while adhering to specific rules or guidelines.

For example, a graphic design agency might use Differentiable Programming in Generative AI to create customized logos. The AI model considers design principles and branding guidelines while generating unique logos that align with clients' requirements.

Example Prompt "Apply Differentiable Programming in Generative AI to design a series of logo concepts for our new product line. Incorporate specific colour schemes and design elements to ensure the generated logos adhere to our brand guidelines."

4. Domain Adaptation in Generative AI ??

Domain Adaptation in Generative AI focuses on adapting AI models to perform effectively in different target domains. Like a multilingual translator who effortlessly switches between languages, domain adaptation equips AI models to transfer their knowledge from one domain to another seamlessly.

For example, a healthcare organization could employ Domain Adaptation in Generative AI to diagnose medical conditions from different hospitals' datasets. The AI model learns from diverse patient data and adapts its diagnosis capabilities to new medical facilities.

Example Prompt "Demonstrate Domain Adaptation in Generative AI by generating personalized financial reports for various customer segments. Train the AI model on data from different customer groups and ensure it adapts to each group's specific financial needs."

5. Diverse Sampling in Generative AI ??

Diverse Sampling in Generative AI aims to produce varied and distinctive outputs, avoiding repetitive or similar content. Like a creative chef concocting diverse dishes, diverse sampling encourages AI models to generate unique and innovative content.

For example, a content creation agency could use Diverse Sampling in Generative AI to develop a series of unique social media posts for a marketing campaign. The AI model ensures each post has a distinct tone and message, attracting different segments of the target audience.

Example Prompt "Employ Diverse Sampling in Generative AI to design a set of creative advertising headlines for our product launch. Ensure each headline offers a fresh and captivating perspective on the product's benefits."

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6. Domain-Specific Generative AI Models ??

Domain-Specific Generative AI Models are designed to excel in specific industries or niches. Similar to a specialized expert in a particular field, these models possess domain knowledge that enables them to produce highly relevant and accurate content.

For example, a legal firm might implement Domain-Specific Generative AI Models to draft legal contracts. The AI system is trained on vast legal databases, generating precise and tailored contracts for various legal scenarios.

Example Prompt "Develop a Domain-Specific Generative AI Model for our architecture firm. Train the AI model on architectural design principles and construction regulations to generate accurate and compliant building blueprints."

7. Dynamic Output Modification ??

Dynamic Output Modification in Generative AI allows real-time adjustments to generated content based on user feedback or requirements. Like a flexible actor adapting performances to audience reactions, dynamic output modification ensures that AI-generated content remains relevant and aligned with user preferences.

For example, a language translation service might deploy Dynamic Output Modification in Generative AI to improve translation accuracy. The AI model refines translations based on user feedback, continuously enhancing the quality of translated text.

Example Prompt "Demonstrate dynamic output modification in your responses to customer inquiries. Adapt your answers based on user feedback, ensuring accurate and relevant information for each interaction."

8. Data-Driven Decision Making with Generative AI ??

Data-Driven Decision Making harnesses the power of Generative AI to analyse vast datasets and provide valuable insights for strategic choices. Like a seasoned detective gathering clues to solve a case, Generative AI extracts patterns and trends from data, supporting informed and impactful decision-making.

For example, a retail company could employ Data-Driven Decision Making with Generative AI to optimize inventory management. The AI system analyses historical sales data, predicting demand fluctuations, and guiding inventory restocking to minimize stockouts and overstocking.

Example Prompt "Use Data-Driven Decision Making with Generative AI to analyse customer feedback and recommend improvements to our customer support process. Provide insights on potential service enhancements based on customer sentiments and feedback trends."

9. Disentangled Representations in Generative AI ??

Disentangled Representations in Generative AI enable the separation of different factors within content generation. Similar to organizing a wardrobe with distinct compartments for various clothing items, disentangled representations allow AI models to control and manipulate specific features independently.

For example, a virtual fashion designer could utilize Disentangled Representations in Generative AI to customize clothing designs. The AI model separates garment styles, colours, and patterns, enabling users to mix and match various design elements.

Example Prompt "Demonstrate Disentangled Representations in Generative AI by creating a series of customizable car designs for our automotive company. Separate vehicle body shapes, colours, and accessories, allowing customers to personalize their ideal car."

11. Diagnostics and Error Analysis in Generative AI ??

Diagnostics and Error Analysis in Generative AI involve identifying and understanding the model's mistakes to improve its performance. Like a detective analysing clues to solve a mystery, this process helps AI developers fine-tune their models and reduce errors.

For example, an online language learning platform might employ Diagnostics and Error Analysis in Generative AI to enhance speech recognition. By analysing misinterpreted words and patterns in user speech, the AI model refines its pronunciation accuracy.

Example Prompt "Conduct diagnostics and error analysis to improve your responses to legal queries. Identify common misunderstandings and ambiguities in legal language, ensuring accurate and contextually appropriate answers."

12. Deployment in Generative AI ??

Deployment in Generative AI refers to the process of integrating AI models into real-world applications. Like launching a rocket into space, successful deployment ensures that AI models work effectively and efficiently in practical scenarios.

For example, a virtual reality gaming company might undertake Deployment in Generative AI to create lifelike environments. The AI model generates realistic landscapes and scenarios that immerse players in thrilling gaming experiences.

Example Prompt "Prepare for deployment as a virtual assistant in our customer service department. Integrate with our communication systems to provide real-time support and assistance to customers."

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.

Next in our series is chapter E - it will be released next week.

If you missed any of the prior chapters then here are links to them:

?? A-Z Of Generative AI and ChatGPT - Chapter 1

?? A-Z of Generative AI and ChatGPT - Chapter 2?

?? A-Z of Generative AI and ChatGPT - Chapter 3

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

• McKinsey?What is Generative AI?
• McKinsey What's the future of Generative AI? An early view in 15 charts.
• ERP Today?Generative AI for the enterprise?-?Pascal BORNET?and?Kieran Gilmurray

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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

I am regularly ranked as one of the top global experts in Artificial Intelligence, Intelligent Automation, Data Analytics, Brand Influence, and Business Technology Innovation and have won multiple international awards, including:

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I am an hugely experienced data science leader who has lead teams of PHDs, data analysts, data engineers, and database administrators for many years, creating one of the few genuine Decision Intelligence companies to date along the way.

But don't just take my word for it.

'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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