The Power of Data in Artificial Intelligence

Welcome to another enlightening installment by the AI experts at, the leading agentic AI orchestration platform. In this article by Brian Charles, PhD, we dive deep into the critical role of data in artificial intelligence (AI) and explore contemporary practices that enhance AI solution development. Join us as we uncover the intricacies of collecting, cleaning, synthesizing, and managing data in the age of AI, and see how these practices are revolutionizing industries.

Secondary, image focusing on all data activities — and the importance of data stitching

Collecting Data: The Bedrock of AI

Data collection is the foundation upon which all AI solutions are built. The quality and quantity of data significantly impact the effectiveness of AI models. Recent advancements in IoT (Internet of Things) have enabled the collection of vast amounts of real-time data from various sources. For example, smart cities are now leveraging IoT devices to gather data on traffic patterns, energy usage, and environmental conditions. This data is then used to optimize urban planning and improve the quality of life for residents.

However, collecting data is not without its challenges. Ensuring data privacy and security is paramount, especially with the increasing frequency of cyber-attacks. Companies must implement robust data governance frameworks to protect sensitive information and comply with regulations such as GDPR (General Data Protection Regulation).

Cleaning Data: Ensuring Quality and Accuracy

Raw data is often messy and requires cleaning before it can be used effectively. Data cleaning involves identifying and correcting errors, filling in missing values, and removing duplicates. This process is crucial for ensuring the accuracy and reliability of AI models.

In 2023, a major financial institution faced a significant data breach that resulted in corrupted data. Indeed, such occurrences are happening every day and may have already impacted your industry.  In this case, the financial company’s AI-driven fraud detection system struggled to identify fraudulent activities due to the compromised data quality. This incident highlighted the importance of rigorous data cleaning processes. By implementing advanced data cleaning techniques, the institution was able to restore data integrity and enhance the performance of its AI systems.

Tertiary image emphasizing synthetic data

Making Synthetic Data: Bridging the Gaps

When real data is scarce, synthetic data can fill the void. Synthetic data is artificially generated and can be used to train AI models when real-world data is limited or unavailable. This approach is particularly useful in fields like healthcare, where patient data is sensitive and difficult to access. Recently, synthetic data has been used to simulate clinical trials, allowing researchers to test new treatments without compromising patient privacy.

One notable example is the use of synthetic data in autonomous vehicle development. Companies like Tesla and Waymo generate vast amounts of synthetic driving data to train their self-driving algorithms. This enables them to test scenarios that might be rare or dangerous in the real world, accelerating the development of safe and reliable autonomous vehicles.

Stitching Data: Creating a Unified View

Data stitching involves combining data from multiple sources to create a comprehensive and unified view. This practice is essential for gaining holistic insights and making informed decisions. For instance, in the retail industry, companies stitch data from online and offline channels to understand customer behavior better and optimize their marketing strategies.

Amazon, for example, uses data stitching to integrate purchase history, browsing behavior, and customer feedback. This unified data view allows Amazon to provide personalized recommendations and improve customer satisfaction. The result is a seamless shopping experience that drives customer loyalty and increases sales.

Storing Data: Scalability and Accessibility

Efficient data storage solutions are crucial for handling the massive volumes of data generated in the digital age. Cloud storage has emerged as a scalable and cost-effective solution, allowing organizations to store and access data from anywhere in the world.

Google Cloud’s BigQuery, as just one example, offers a serverless and highly scalable data warehouse designed for large-scale data analytics. This platform enables businesses to analyze petabytes of data quickly and efficiently, providing valuable insights that drive innovation and growth. Companies across various industries are leveraging cloud storage to streamline their operations and enhance their AI capabilities.

Embracing Contemporary Data Practices

The dynamic nature of data in the context of AI requires continuous adaptation and innovation. Contemporary practices such as real-time data processing, edge computing, and federated learning are pushing the boundaries of what AI can achieve. These advancements enable faster decision-making, reduce latency, and enhance data privacy.

For instance, edge computing allows data to be processed closer to its source, reducing the need for data to travel to centralized servers. This is particularly beneficial for applications requiring low latency, such as autonomous drones and real-time medical monitoring systems.

Conclusion: The Future of Data in AI and on Your Business

As we continue to advance in the field of AI, the importance of data cannot be overstated. Collecting, cleaning, synthesizing, stitching, and storing data are fundamental practices that enable AI to reach its full potential. By adopting these contemporary data practices, organizations can unlock new opportunities, drive innovation, and stay ahead in an increasingly competitive landscape.

At, we are committed to helping businesses harness the power of data to create intelligent, adaptive, and transformative AI solutions. Stay tuned for more insights and thought-provoking discussions as we explore the ever-evolving world of AI and its impact on various industries.

Brian Charles, PhD, is VP of Applied AI Research at (  He is a subject matter expert in AI applications across industries as well as the commercial and academic research around them, a thought leader in the evolving landscape of generative and agentic AI and is an adjunct professor at the Illinois Institute of Technology.  His insights have guided leading firms, governments, and educational organizations around the world in shaping their development and use of AI.

Unlocking Agentic AI: Balancing Corporate Integrity and FATE Principles

TL;DR: Discover how businesses can harness the potential of agentic AI while maintaining ethical standards and addressing FATE concerns.

In today’s rapidly evolving business landscape, artificial intelligence (AI) has emerged as a game-changer, offering unprecedented opportunities for growth and innovation. However, as AI becomes more autonomous and agentic, concerns about its impact on corporate values and ethical decision-making have intensified. This post explores the delicate balance between leveraging agentic AI and addressing the FATE concerns (Fairness, Accountability, Transparency, and Ethics) surrounding its use.

Embracing Agentic AI

Agentic AI refers to AI systems that can operate autonomously, making decisions and taking actions with limited human intervention. While this opens up new possibilities for businesses, it also raises questions about accountability and the alignment of AI decisions with corporate values. By embracing agentic AI, companies can leverage its efficiency, accuracy, and scalability to drive growth and enhance customer experiences (Russell & Norvig, 2020).

Addressing FATE Concerns

As AI systems become more advanced, ensuring fairness, accountability, transparency, and ethics becomes paramount. Businesses must proactively address these concerns by implementing robust governance frameworks, ethical guidelines, and regulatory compliance measures (Floridi et al., 2018). This ensures that AI systems adhere to the company’s core values, promote inclusivity, and avoid bias or discrimination.

Integrating Corporate Values

To maintain corporate values in the age of agentic AI, businesses must integrate their ethical principles into AI development and deployment processes. This includes training AI systems with comprehensive and diverse data sets, emphasizing fairness and transparency, and regularly auditing AI algorithms to identify and rectify any biases or ethical concerns (Binns, 2018).

Collaboration Between Humans and AI

Rather than viewing AI as a replacement for human decision-making, businesses should embrace a collaborative approach. By combining human expertise and AI capabilities (a sort of “Hybrid Intelligence”), companies can achieve the best of both worlds. This approach fosters a culture of shared responsibility, where humans provide oversight, ethical judgment, and contextual understanding, while AI systems offer data-driven insights and operational efficiency (Rahwan et al., 2019).

FATE as a Guiding Principle

The FATE concerns, which encompass Fairness, Accountability, Transparency, and Ethics in agentic AI, are vital for promoting responsible and ethical use. However, it’s important to recognize that these terms can be ambiguous and subject to cultural differences, affecting their interpretation and implementation. Instead of treating FATE concerns as binary concepts, businesses should acknowledge their nuanced and complex nature, allowing for a more holistic and inclusive approach (Mittelstadt et al., 2016).

Reproducibility, a fundamental aspect of scientific research, holds significant importance in FATE concerns. By prioritizing the ability to reproduce and validate AI models and algorithms, businesses can ensure that the decisions made by these systems are consistent, transparent, and less biased (Hutson, 2018). This focus on reproducibility enhances the trustworthiness and accountability of AI systems. Additionally, considering cultural relevance is crucial as FATE concerns may vary across different cultures and societies. Businesses need to adapt their AI practices by engaging diverse perspectives, incorporating local values, and addressing potential biases or discrimination to effectively address FATE concerns (Jobin, Ienca, & Vayena, 2019).


While agentic AI holds immense potential for businesses, it is crucial to navigate the FATE concerns to ensure ethical and responsible use. By embracing agentic AI, addressing FATE concerns, integrating corporate values, and fostering collaboration between humans and AI, companies can strike the right balance and harness the transformative power of AI while upholding their core principles.

Remember, in this fast-paced digital era, organizations that effectively manage the FATE concerns surrounding agentic AI will not only thrive but also inspire trust and loyalty among customers and stakeholders.


Binns, R. (2018). Fairness in Machine Learning: Lessons from Political Philosophy. Proceedings of the 2018 Conference on Fairness, Accountability, and Transparency. 

Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., … & Schafer, B. (2018). AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, 

Principles, and Recommendations. Minds and Machines, 28(4), 689-707.

Hutson, M. (2018). Artificial intelligence faces reproducibility crisis. Science, 359(6377), 725-726.

Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389-399.

Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: 

Mapping the debate. Big Data & Society, 3(2), 2053951716679679.

Rahwan, I., Cebrian, M., Obradovich, N., Bongard, J., Bonnefon, J. F., Breazeal, C., … & Wellman, M. (2019). Machine behaviour. Nature, 568(7753), 477-486.

Frank Betz, DBA, an accomplished professional at (, is a driving force in guiding industry, government, and educational organizations toward unlocking the full potential of generative and agentic AI technology. With his strategic insights and thought leadership, he empowers organizations to leverage AI for unparalleled innovation, enhanced efficiency, and a distinct competitive advantage.

The AI Afterlife: Why Agentic AI Isn’t Just RPA Back from the Dead

Welcome to the first installment of an exciting new blog series by the AI experts at, the leading agentic AI orchestration platform. In this series, we will delve deep into the transformative world of AI, generative AI and agentic AI, exploring AI’s revolutionary impact on various industries and debunking common misconceptions. Our mission is to provide you with cutting-edge insights and thought-provoking discussions that highlight the true potential of agentic AI. Stay tuned as we embark on this journey, unraveling the complexities of this dynamic technology and showcasing its unprecedented capabilities.

The AI Afterlife: Why Agentic AI Isn’t Just RPA Back from the Dead

The rise of agentic AI marks a pivotal shift in the current AI landscape, and it is poised to overtake the popular-but-hackneyed themes of privacy, bias, weaponization which circulate daily in popular media when talk turns to generative AI.  “Gen AI” may be in the limelight at the moment, but it is agentic AI which truly transcends the mechanical repetition and pre-defined tasks of Robotic Process Automation (RPA) that many business users think of when they think about AI and in many ways which mirrors Gen AI in its capabilities.

When the COVID-19 pandemic kicked workers out of the office, when banks still needed to clear checks, businesses still had to cut payroll checks and insurance companies still needed to process claims, good old RPA was there, scanning, paying and processing – without thought, without too many people to program its own labyrinthine systems and certainly without sentience.  As someone deeply entrenched in the advancements of artificial intelligence, I’ve seen the conflation of these two technologies – AI and RPA – and it’s time we set the record straight: agentic AI isn’t RPA back from the dead.  I’s an evolutionary leap forward!

The Ghost of RPA Past

RPA had its heyday as a game-changer for businesses. It automated mundane, repetitive tasks, liberating human workers to focus on more strategic initiatives. However, its limitations were stark. RPA systems could only follow explicit instructions and handle predictable, rule-based processes. Specialists needed to program RPA systems as well.  Any deviation or unexpected scenario would cause these RPA bots to falter, requiring human intervention to set them back on track.

This lack of adaptability and learning capacity rendered RPA a powerful yet fundamentally static tool. It was akin to having a highly efficient but inflexible employee who could only perform a specific set of tasks. When businesses needed more dynamic solutions, RPA’s limitations became glaringly obvious.

Enter Agentic AI

Agentic AI represents a radical departure from the static nature of RPA. Agentic AI systems are multiple AI assistants, collaborating as a team on discrete parts of any problem they are tasked to solve and combining to produce a result – without continual user intervention or involvement.  These AI systems are not merely programmed to perform tasks; they are designed to think, learn, and adapt autonomously. Imagine an AI that doesn’t just follow a script but writes its own, constantly evolving to meet new challenges and optimize its performance.

Unlike RPA, agentic AI systems can understand context, make decisions based on real-time data, and learn from their experiences. They don’t just execute tasks; they innovate and optimize processes beyond human anticipation. This adaptability is crucial in today’s fast-paced, ever-changing business environment, where agility and intelligence are key to staying competitive.

The Living, Breathing Future of Work

Consider the implications of agentic AI in a corporate setting. Instead of merely automating invoice processing like RPA, an agentic AI system could analyze spending patterns, predict budgetary needs, and even negotiate with suppliers in real-time. It’s not just about doing the work faster; it’s about doing it smarter.  It wouldn’t need explicit programming, and it would infer what a user wanted to accomplish rather than presenting that user with a blinking cursor and a search bar like today’s Gen AI assistants.  An agentic AI system would know beyond a human user’s initial contemplation of a problem, rewriting software code without being told, resolving problems and acting as an autonomous agent on behalf of the user.  Very much as their name suggests, these systems would be agents.  Proxies for their human users, interacting with other agents to perform and conclude work.

This evolution brings with it a paradigm shift in how we perceive and interact with technology. Agentic AI doesn’t just enhance efficiency; it empowers innovation. It’s a dynamic partner in business, capable of evolving with the organization and contributing to strategic decision-making processes.

Dispelling the Myths

Critics often fear that agentic AI is simply a reincarnation of RPA, doomed to repeat the same shortcomings. This couldn’t be further from the truth. While RPA was the stepping stone, agentic AI is the giant leap. The autonomy, learning capability, and contextual understanding of agentic AI place it in a league of its own.

To the pundits who hold the notion that agentic AI is RPA in disguise, this is not only misleading but also diminishes the groundbreaking advancements we are witnessing. It’s akin to comparing a typewriter to a modern computer. Both serve the purpose of creating text, but the latter offers capabilities far beyond the imagination of the former.

The Road Ahead

As we forge ahead into this new era, it is crucial for businesses leaders to embrace the transformative potential of agentic AI. This technology is not just a tool; it’s a catalyst for innovation and growth. It promises to redefine industries, create new opportunities, and tackle challenges that were previously insurmountable.  Challenges that we didn’t even know we had or that happen on-the-fly and require an immediate, intelligent response and action.

Agentic AI is not RPA resurrected; it’s a revolutionary force poised to change the world. As we continue to explore and harness its potential, we stand on the brink of a future where AI is not just an assistant but a visionary partner. The possibilities are endless, and the journey has only just begun.

Brian Charles, PhD, is VP of Applied AI Research at (  He is a subject matter expert in AI applications across industries as well as the commercial and academic research around them, a thought leader in the evolving landscape of generative and agentic AI and is an adjunct professor at the Illinois Institute of Technology.  His insights have guided leading firms, governments, and educational organizations around the world in shaping their development and use of AI.