Introducing PxR Blog
Let's weed through the hype and gain common understanding of the challenges and benefits afforded by genAI.
The Importance of Governance in your AI Initiatives
We've lived with the idea of intuitive interaction with computing for the better part of a century. The Artificial Intelligence of the robots in the short stories of Issac Asimov and the Robot from Lost in Space could easily carry on normal conversations with people. But the AI in those robots could not have done that because there wasn't enough data available at the time. AI doesn't learn the way people do. People absorb facts, details, events. They associate those things with similar knowledge they already have and from that can synthesize and extrapolate new understanding. AI learns through the brute force of massive data consumption. So, quite reasonably, people have opened the doors for AI tools to large amounts of data that is diverse, historical and current and not always well filtered.
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This has lead to some embarrassing and potentially damaging events. Smart, well intentioned people have fed confidential corporate data to commercial LLMs thinking that they or their co-workers would then be the only persons who could access that data. Once the data is in a common pool it can be very hard to get it out or restrict access to it, like trying to remove a glass of the same water from a swimming pool. Some Federal agencies have seen similar, early growing pains - mistakes by very well intentioned workers who were simply seeking to understand the potential value proposition of a larger AI initiative or take advantage of genAI in their work.
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Governance is the oversight and body of guiding directives that assures the appropriate deployment of AI. While employees need access to AI tools, the extent of information and capabilities that should be accessible to any given employee varies. While a senior manager in a Finance Department has access to information not appropriately shared with a newly hired bookkeeper or junior accountant, they both benefit from utilizing the genAI that is the new assistant in Finance. Managing access to AI tools and organization data is a component of Governance.
As with any new technology, AI runs the risk and being the new flashy object that some leaders will decide to add to the tool suite of a department or entire agency or company. There is no doubt that even a mis-guided deployment of AI will bring value, though it may bring risk and even damage as well. But even if risk is mitigated and potential damage is obviated, there is still no guarantee that the initiative, the cost and effort, will result in the most effective use of this incredibly powerful capability.
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PixelRain was not the brainchild of a collection of data scientists and/or abstract technologists. PixelRain was formed by a collection of people with decades and decades of experience building enterprise class custom software solutions to support real world, mission critical business processes across a wide spectrum of business activities such as finance, asset management, data management, knowledge management to name a few.
We understand that a new Information Technology deployment must fit correctly both the needs of the users and the overarching mission on the organization. The solution must support workflows and augment them, not simply duplicate them, and must do so with a high level of user acceptance.
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AI is not just an "install it and watch the magic happen" solution. That is an invitation to chaos. And while AI will eventually touch all areas of an agency or company, it can't be delivered everywhere, all at once, instantly. Well understood Governance is vital to selecting the most important places to start and defining a roadmap to move forward. Governance is critical to successfully achieve elegant, robust security that does not also handicap users through a blunt or clumsy access limitation model. The PixelRain team has been doing this for decades within IT broadly and our understanding is deep as well as broad.
The Importance of the PxR Platform in your AI Initiatives
AI, or artificial intelligence, has been a growing capability for decades. Many if not most organizations have some form of AI embedded in current IT systems and applications. AI refers broadly to the capability of machines or systems to perform tasks that would typically require human intelligence. A common example is predictive analytics using Machine Learning, such as credit scoring in finance, disease prediction in healthcare, and demand forecasting in supply chain management. AI activities include things like understanding natural language, recognizing patterns, solving problems, and, in some cases, making decisions based on deterministic rules. AI can be applied to a variety of tasks, such as data analysis, automation, robotic process automation, and decision support systems.
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Generative AI is a subset of AI focused specifically on generating new content, ranging from text and images to software code and synthetic data. This form of AI leverages deep learning techniques, particularly neural networks that are structured to learn from vast amounts of existing data and then produce outputs that mimic the original data in a new form.
AI
Generative AI
Output
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Designed to perform a broad range of tasks that mimic human intelligence applied to deterministic problems
Creates new data instances that resemble the training data, effectively generating new content that did not previously exist
Purpose
Decisions, predictions, or classifications based on input data
New content or data derived from learned patterns applied to non-deterministic tasks
Generative AI is revolutionary due to its ability to understand requests in context and produce original outputs, pushing the boundaries of what machines can create autonomously.
With holistic understanding of enterprise AI deployments, management can turn to optimizing AI applications to enhance the benefits to the enterprise. Understanding how the organization is benefitting today supports activities to define the enterprise’s strategy and plan for how it can expand the operational, efficiency and decision-making benefits provided by AI as its integration into broader systems or workflows expands.
Optimize Your AI Application Deployments Based on Understanding
Optimizing generative AI applications relies heavily on both a thorough understanding of the technology, the ways it is deployed and associated usage patterns to derive trust in the performance and ethical alignment of the AI application ecosystem, essential for driving forward the capabilities and acceptance of AI to deliver benefit to the enterprise
Risk Management
With a solid understanding of AI deployments, mangers can better communicate the safeguards and capabilities of AI systems, thereby promoting trust and more innovative uses and applications, driving further exploration and understanding of potential beneficial uses of AI.
Scalability
Understanding of usage and usage patterns allows for scaling AI solutions more effectively, while ensuring that these scaled solutions are accepted and utilized by a broader audience, maximizing the impact of AI in the enterprise.
Enhanced Transparency
Improved understanding among stakeholders fosters a collaborative environment where feedback loops are efficient and effective. This collaboration can lead to more considered adjustments and refinements in AI applications and targeted risk reduction measures.
Technical Optimization
Understanding of generative AI technologies allows the organization to make better decisions on foundational vs fine-tuned models to optimize cost and performance. This includes choosing the right architecture, training procedures, and datasets to minimize biases and improve the quality and relevance of the outputs.
Application-Specific Customization
Understanding how generative AI functions in different contexts and associated usage patterns enables application owners and stakeholders to tailor AI applications to better meet organization and workgroup needs.
Addressing Limitations
Understanding datasets used and associated limitations of generative AI applications, such as potential biases in data or areas where the AI might underperform, is essential for optimization. A holistic view combined with objective data on application benefits and risks raises awareness of limitations that can guide targeted improvements and the implementation of complementary applications to mitigate weaknesses.
User Adoption
Transparency about the organization’s generative AI ecosystem drives trust that the AI applications consistently produce high-quality and fair outputs. With this trust, managers are more likely to integrate AI applications into their operations, driving innovation and further adoption.
Feedback and Iteration
Enabling users to provide feedback engages users which is essential for iterative improvement of the organization’s generative AI ecosystem. Users are more likely to provide constructive feedback and engage in testing new features, thereby aiding in optimization.
Regulatory and Stakeholder Approval
Transparency can accelerate approvals and acceptance of generative AI, enabling broader deployment and integration across the organization. Compliance with mandates and regulatory standards can reassure management and stakeholders of the safety and reliability of the AI ecosystem provided within the organization, which is often a prerequisite for operational optimization.
Understanding vs Trust in Enterprise GenAI Deployments
Both understanding and trust play pivotal roles in assessing generative AI deployments, although they contribute in different ways.
In an enterprise context, understanding the organization’s generative AI ecosystem involves attaining a holistic view of the multiple AI applications, models, tools and agents in use or under consideration including the significant metadata necessary to effectively manage and control AI application deployments and usage across the organization. A comprehensive understanding of how these applications function, what organization data is accessed, by whom within the organization, for what purpose, the benefits and limitations of their capabilities, and the risks associated with usage. This understanding is crucial for several reasons:
Risk Management
Knowing how generative AI models generate content can help in identifying potential risks, such as biases in the generated outputs or the misuse of AI in creating deceptive content. An in-depth understanding allows developers and users to implement safeguards and ethical guidelines effectively.
Compliance
Recent mandates require various types of reporting and comprehensive risk assessment activities as well as a mandate to move with speed to break down barriers and make generative AI available throughout the enterprise. Organizations need to establish policy and ensure alignment of deployments with policy.
Improvement and Innovation
A thorough understanding of these technologies enables researchers and developers to innovate and improve upon existing models. This might involve enhancing the quality of outputs, expanding the range of capabilities, or reducing errors and biases.
Setting Realistic Expectations
By understanding the capabilities, limitations and risks of AI current and pending deployments, organizations can set realistic expectations for what these technologies can achieve and how it will benefit the organization and its employees. This prevents overestimation of AI capabilities which can lead to disappointments or misallocations of resources.
Trust, on the other hand, involves the belief that generative AI will function as intended without causing unforeseen harm. Building trust is essential for widespread adoption and effective use, promoting:
Ethical Assurance
Trust is built when users feel confident that generative AI operates within ethical boundaries. This includes issues like respecting privacy, avoiding discrimination, and ensuring transparency in how decisions are made by the AI.
Accountability
Trust is enhanced when there is clear accountability for the actions and outputs of generative AI systems. Knowing who is responsible for the AI’s outputs and who to turn to in case of problems helps build confidence among users.
Reliability
Users need to trust that generative AI systems are reliable and that the outputs are accurate and consistent. Ensuring high levels of accuracy and minimal errors in different scenarios is critical to gaining and maintaining this trust.
Regulatory Compliance
Compliance with existing laws and regulations also builds trust. Users need to know that the use of generative AI adheres to all relevant legal frameworks, which provide protections and standards for technology deployment.
While trust and understanding are interdependent, they operate at different levels. Trust focuses on the individual AI application level while understanding focuses at the enterprise level and supports the enterprise AI governance function. While trust encourages more interest and user engagement with AI solutions, understanding of AI application deployments is essential to provide the foundational information necessary for on-going monitoring and control of those trusted AI applications to maintain that level of trust. Trust in AI applications works in conjunction with an understanding that the applications are demonstrated to be reliable, ethical, and compliant with societal and organizational standards and provide net benefit to the organization.