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You're reading from Unleashing the Power of Data with Trusted AI

Product type Book

Published in Apr 2024

Publisher Packt

ISBN-13 9781835467893

Pages 68 pages

Edition 1st Edition

Languages

Concepts

Data Analysis

Author (1):

Wendy Turner-Williams

Table of Contents (12) Chapters

Executive summary

Target audience

Introduction to AI and generative AI

Modernizing the boardroom to steer data and AI

The offense and defense of data

Navigating the future of AI by bridging strategies

Seven steps to ethical, quality, and trusted generative AI implementation

Real-world AI success cases

The future of generative AI in data and analytics

Impact of generative AI on data-driven decision-making

The road ahead: Embracing the AI revolution

Key takeaways for board members and executives

Conclusion: Embracing the future of data and AI

About the author

Bibliography

Seven steps to ethical, quality, and trusted generative AI implementation

Having emphasized the critical role of business, data, and infrastructure strategies, alongside the concept of Data by Design, it is time to take tactical strides in your AI journey. Assessing organizational readiness is a foundational aspect of defining the explicit steps of your journey and the unique detours needed along the way. As only 35%8 of companies consider themselves adequately prepared for AI implementation, you should think of these steps as your AI trip advisor. The AI trip advisor defines the who, what, what if, when, why, and where of your journey.

Every industry and company has its own unique strategy, organizational structure, people, infrastructure, data, engineering processes, and risks. This report is written to help boards of directors and executives with their generative AI journey—it’s your AI trip advisor companion. The recommendations may seem like a top-down approach...

A personal journey in Azure engineering

I would like to provide a personal example of a Data by Design initiative that had a top-down, bottom-up, and side-to-side approach all at once. Years ago, when Azure was in its initial stages, I was responsible for the Azure Data Platform’s Engineering Fundamentals program. This program was the result of strategy and technology shifts, as Azure was in its infancy and many of the employees had originally worked in a box product environment. The concept of shipping in a day was foreign, as was building products on top of other infrastructure cloud services that were also shipping in a day. We needed to shift engineering fundamentals left to ensure available, extensible, supportable, and trusted services.

To do this, we had to significantly invest in frameworks, training, process automation, and monitoring to assess the maturity of each product against standardized architecture, engineering, support, and risk management processes. This...

Step 1: People and processes – executive R&R model and AI task force

The mainstay of a successful AI transformation lies in a robust, clear executive roles and responsibilities model. As organizations differ in structure, aligning to a guideline that guarantees essential expertise and accountability is critical.

Just as a football team uses offense, defense, special teams, and so on to work harmoniously, organizations operate through interconnected departments and processes. Data, akin to a company’s lifeblood, courses through these systems, profoundly influencing the overall vitality and efficiency. Yet, like any complex organism, internal politics often affect the health and productivity of these data systems. Steering a data and AI strategy demands a single accountable P&L executive whose sole charge is to treat data and AI as a core enterprise asset and to manage its risks. This leader ensures data alignment with strategic business needs, promotes responsible...

Orchestrating the symphony of AI

Continuing with the conductor analogy, imagine data as the notes, tempo, and pauses in music. Each department is represented by an instrument type, and the data strategy is to ensure that each musician and section performs to their finest. When the raw notes on paper are infused with the individual musician’s skill, and the various instrument types intertwine and work in unison to build a score—even the silence between the beats are part of the story, the emotion.

When we think of AI, the musicians are more like wind-up toy versions of ourselves—who only act and react based on the data they have been programed with. This wind-up toy version doesn’t have common sense, it doesn’t understand your company strategy, operations, or tribal knowledge. It can play the instrument, but it doesn’t understand the beat or the song the rest of your team is playing. You must ensure your wind-up musicians are fed the best...

Example: Balanced approach

Here is an example illustrating the delicate balance achieved of offense and defense in my own capacity as a CDAIO. In a previous role, I spearheaded the modernization of our data strategy, data management processes, data platforms and services while concurrently enhancing the quality, reliability, and trust of the data housed within these platforms and mitigating data risks.

One notable project involved the migration of product logs and instrumentation from an expensive and limited on-premises Hadoop instance to a more flexible, scalable, and innovative cloud-based infrastructure. Rather than a simple lift-and-shift approach, we divided the initiative into three parallel tracks:

People and processes: Understanding existing consumers/producers and their data use cases to identify opportunities to improve the data itself. This touched every organization in the company and included use cases like product debugging, customer security incident response...

Step 2: Assessing business readiness

Embarking on the journey toward AI implementation demands an acute comprehension of your organization’s business funnel, the maturity level of the supporting processes, and its data. This phase necessitates a meticulous evaluation of the operational, tactical, and strategic strata of your business operations, aiming to pinpoint areas with mature business and data processes that are ripe for AI integration and improvement. By using the various skill sets and specialty areas of the AI task force, you can combine business, data, technology, and risk frameworks into a new framework that defines, measures, and provides mitigation steps for all.

Understanding the Organizational Business Process Funnel

Begin by grasping the entirety of your business funnel, encompassing every facet from initial customer interaction, lead generation, and sales to service delivery and customer support. Each stage of this funnel holds the potential for AI augmentation...

Step 3: Assessing data readiness

Data rationalization involves the systematic review, cleansing, and organization of your organization’s data assets to bolster efficiency, curtail costs, and amplify data quality, accessibility, and reliability. This critical process involves purging unnecessary data (termed as redundant, obsolete, or trivial (ROT)), identifying valuable data based on relevance, accuracy, and business value, and aligning data initiatives with business strategies, metrics, objectives and key results (OKRs), and initiatives.

To execute a successful data rationalization project, a clear and defined focus is crucial, often established through a specific business use case with a well-defined scope and exit criteria. The involvement of a cross-functional team inclusive of key stakeholders such as C-suite sponsors, business operations, partner ecosystems, IT, and relevant business application and data teams is vital for success. Additionally, your data governance...

Step 4: Understanding the risk landscape

In the ongoing journey to incorporate AI into your organizational structure, it’s vital to grasp the intricate web of risks it entails. Introducing AI without a comprehensive understanding of existing data management maturity and potential risk factors is akin to bringing in a poorly behaved AI entity.

Unique risks introduced by AI and potential mitigations

As with every advancement, AI has risks. As the tech giants battle for AI dominance, policymakers and regulators will try to keep up the pace. The recently released 14 and the 15 are great regulatory starts, but they still ignore that data elephant in the room. Let’s explore some of the new risks that AI introduces and discuss how a Data by Design approach can mitigate these risks while preparing you for the data regulations to come.

Step 5: Data roadmap – using generative AI to uplift readiness

As organizations shift to integrating generative AI into operational frameworks to significantly enhance implementation success, the question arises: how can they safely use generative AI to immediately generate ROI? One solution is to create a dedicated sandbox environment tailored for content generation and the development of custom large language models (LLMs) and language graphs specific to the organization. This approach allows for immediate enhancement of existing data strategies, processes, and AI tactical roadmaps, while consolidating insights gleaned from comprehensive business, data, and risk assessments.

Isolating risks with sandboxes for content generation

The creation of an isolated sandbox environment for generative AI content generation is useful. This sandbox functions as a secure haven where AI algorithms can explore and generate content without interfering with operational data or processes...

Step 6: Embracing the secret sauce – humanism and digital empathy

In the quest to integrate AI into organizational frameworks, it’s imperative to bring employees and customers along on this transformative journey. Organization create an employee development and risk plan specific to the AI journey, outlining the importance of using resources to navigate AI readiness effectively.

Developing employees and retention risk management

As organizations embrace the AI journey, a robust employee development and risk plan becomes a cornerstone for success. This plan aims to upskill and redeploy employees, using existing resources to usher in a culture of AI readiness:

Upskilling for human involvement in AI models: Employees can shift roles to become the humans in the loop, actively involved in training AI models and monitoring their outcomes. This role focuses on guiding AI systems, ensuring ethical and unbiased outputs.
Transitioning to drive business...

Step 7: Implementing an AI Data by Design program

Data by Design represents an approach that integrates data management, data quality, ethics, privacy, and security considerations right from the outset of any data-related architecture, engineering, analytics, process, or system implementation. It goes beyond mere compliance, aiming to infuse data management, quality, linkability, and usage with ethical principles and secure practices into the very fabric of how data is developed, managed, accessed, and utilized. Data by Design ensures that your organization’s data strategy is aligned with business goals, significantly improving the ROI of your data.

A robust Data by Design framework

A Data by Design framework should encompass the following:

Ethics by design: Embedding ethical considerations into the entire data life cycle is fundamental. This includes assessing data collection practices, ensuring ethical sourcing, establishing transparent processes for data use...

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Authors (1)

Wendy Turner-Williams

Wendy is a distinguished leader and innovator at the intersection of business, AI, data, and software. She has been instrumental in spearheading digital, cloud, and AI transformations across globally recognized brands, translating complex concepts into actionable strategies. As the founder and CEO of Culstrata-ai and TheAssociation.ai, Wendy has created platforms that not only provide AI and data professional services but also globally unite professionals across the fields of AI, data, ethics, privacy, robotics, and security in promoting an ethical, quality, and trustworthy AI future. Her leadership role as a CDAIO at Salesforce, Tableau, and other tech giants reflects her ability to realize substantial revenue and operational advancements.

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