When Charles Darwin came up with the theory of evolution, he never would have thought about the speed at which intelligence, once an exclusively human trait, could be replicated, accelerated, and governed by machines. And let’s not forget about Artificial Intelligence, something that became real (yet artificial) only recently, is evolving and adapting!
The world might see another species in existence in the next 10 years, a species made by humans, for the humans, and smarter than the humans themselves, well, at least that is what the targets have been locked at. Since this hot trend of adopting Artificial Intelligence, especially Generative AI, within organizations is going north, what remains south is how to find that right starting point and also ensure a stable ending (if it arrives in the foreseeable future), to make the journey smooth(er). Where do we start, and how do we scale responsible AI initiatives effectively?
At TO THE NEW, we recently conducted a virtual panel discussion to discuss this very Generative AI lifecycle - from selection to governance, with a clear focus on enabling responsible AI at every stage. This article distills the key insights from that session, offering a comprehensive guide for business and technology leaders seeking to implement, scale, and govern Generative AI solutions responsibly.
The three critical stages of this life cycle are:
Generative AI: A Purpose-Driven Strategy for Model Selection
Understanding how generative AI works is essential for effective model selection. Identifying the right large language model (LLM) for your specific business needs has to be anchored to the business needs that you are trying to solve. The urge to chase the latest trends or a default model aligned to your cloud strategy consulting takes a backseat here. A purpose-driven model strategy is foundational to building responsible AI capabilities from day one. The field is evolving rapidly, especially since the generative AI boom, which has significantly increased the availability and adoption of advanced models.
Whether it is defining a primary use case (like customer support, streamlining operations, etc.) or assessing the complexity of it, each use case may demand different outputs like speed, accuracy, cost, domain expertise, etc., from the LLM model that you have selected. Fine tuning is often used to adapt a pre-trained model to specific business needs by providing it with labeled data relevant to the task. When selecting and training a model, it's important to note that foundation models are often trained on large amounts of unlabeled data.
Due diligence: Of course, any organization should undergo due diligence during this selection process, with the same rigor as they do for any other enterprise-grade software. This should include examining the model capabilities, compliance, security, and evaluating the generative AI model based on its architecture and suitability for the business use case.
Sensitivity and deployment: The sensitivity of data involved often determines whether to opt for vendor-hosted models (via API) or self-hosted solutions. While vendor-hosted models offer ease of deployment, self-hosting may be necessary for highly sensitive or regulated data—though this comes with greater technical and operational demands. The use of structured data can further improve model efficiency and accuracy.
Cost considerations: With new advancements dropping in very frequently, the LLMs’ cost is also on a downward trajectory. However, it is the overall cost that organizations must factor in. This includes the licensing fee, infrastructure cost (in case of self-hosted), and maintenance costs such as cloud migration services. Very large models, which have hundreds of billions of parameters, require specialized hardware like GPUs and AI accelerators, often run in datacenter environments, and can significantly impact infrastructure costs. Efficient strategies to train machine learning models are essential to manage these expenses as part of the deployment strategy.
Ultimately, every organization must learn and improve by means of continuous testing, iteration, and re-evaluation to ensure the chosen model delivers on both business objectives and operational constraints, incorporating new data to further enhance model performance.
2. Implementation and deployment: From Proof of Concept to production
Generative AI applications are transforming business processes by enabling organizations to automate tasks, enhance creativity, and streamline workflows across industries.
A model is now chosen with all due considerations, and we must now move to the actual implementation. Implementation strategies vary widely based on organization size, business criticality, and innovation budgets. Hence, it is extremely important to start with a ‘Why’ and then carefully draft a scope. It may be possible that not every challenge will require a gen AI solution. There may be a rule-based system that may suffice in some cases.
Even though the implementation will be larger in itself, considering this is a new shift for most of the organisations, the beginning should still be with a Minimum Viable Product (MVP). Limiting the initial scope to a well-defined use case will not only give a higher return value but also help validate the feasibility faster. An iterative approach with rapid cycles of development, testing, and optimization will act as a confidence booster. Gen AI can generate content across different media, including text, images, software code, music generation, and video generation, making it highly versatile for various use cases.
Integration with existing systems: Many of the organizations will not be building AI solutions from scratch but integrating them into their pre-existing complex architectures. This requires a clear deployment strategy, a modular system design that allows for future upgrades (once more advanced LLMs or many generative AI models are available—remember the speed at which this is evolving? Darwin?), and robust data engineering services pipelines for model training. AI assistants are a prime example of gen AI integration, automating and enhancing workflows. Additionally, advanced strategies like retrieval augmented generation can further improve the accuracy and relevance of deployed solutions.
Responsible AI: While the AI world is evolving, it is very easy to slip off the business value edge. The GenAI Maturity Framework, highlighted by the panelists, serves as a strategic guardrail for responsible AI adoption. Some of the key considerations this framework supports are:
Addressing any known GenAI challenges, such as bias, that may make the output skewed
Strong security and privacy controls, especially if there is sensitive data involved
Ensuring the AI outputs are explainable and enable the user in the decision-making process
Leveraging existing investments: Even though an organization is very forward-looking, it should not ignore its previous investments in traditional NLP and machine learning solutions that are proven and delivering value. This world is still evolving, and in many cases, a hybrid approach to combine gen AI with existing approaches may help reduce cost and optimize performance.
3. Governance: The cornerstone of Responsible GenAI
Dealing with the knowns is easy. Dealing with something like GenAI is far more complex than ever, especially when it is evolving faster than ever. This presents a perfect opportunity to have a robust governance structure in place to reap the maximum benefits and to keep the risks as low as possible. Out of all the reasons that make governance around your GenAI model complex than ever, the key ones highlighted by our panelists were:
Rapidly evolving technology and regulatory affairs
Diverse and inconsistent approach towards AI, meaning risk across departments
The scale and variety of data used for training your model
Generative AI models, such as the generative adversarial network, require careful governance due to their complexity and potential impact.
To give governance a framework, the panelists suggested the following key governance pillars:
Cross-functional governance teams: Stakeholders from various departments bring multiple perspectives. A good strategy could be to involve people from Legal, compliance, IT, data scientists(of course!), etc. For effective oversight of generative AI, teams should also include expertise in neural networks and deep generative models to address technical and ethical challenges.
Standardization and clear policies: Defining internal AI policies sets a level playing field, yet maintains uniformity within the organization. This should cover aspects like privacy, security, and ethical use of AI.
Evaluation and validation: This is not a new concept and applies very well to the realm of GenAI as well. Organizations must evaluate the generative model against key metrics like accuracy, drift, bias, and hallucination. Human feedback is crucial in improving model performance, especially in reinforcement learning with human feedback (RLHF). For models like generative adversarial networks, evaluation should consider the two models—the generator and the discriminator—which are two neural networks trained together, often starting from random noise to generate outputs. Automated monitoring tools can help detect fake data produced by these models. Additionally, governance should extend to advanced models such as Stable Diffusion and other diffusion models, which are widely used for image generation and require ongoing validation.
Transparency: Both parties (internal users and external customers) should know how the AI models are making decisions. This will help build trust and accountability in the relationship. Explaining model decisions can involve concepts like latent space and data points, which are central to understanding how generative models operate. For applications like image generation, it is important to ensure that models create realistic images responsibly and transparently.
Even though one might think that governance is the last part of the chain, bringing it early on in the cycle was strongly advocated by the panelists. This approach will help organizations to avoid mistakes, that can prove to be costly with time as regulations evolve, along with GenAI.
AI Generated Content: Opportunities and Challenges
The rise of AI-generated content, fueled by powerful generative AI models and large language models, is transforming the landscape of digital media and communication. Generative AI systems now enable organizations to automate content creation at scale, from natural language processing tasks like text generation to producing realistic images and videos. This surge in generative AI tools is opening new doors for industries such as marketing, entertainment, and education, where the ability to rapidly generate personalized, engaging content can drive significant value and efficiency.
One of the most exciting aspects of generative modeling is its capacity to augment human creativity. By leveraging deep learning models, generative adversarial networks (GANs), and variational autoencoders (VAEs), businesses can create synthetic data and realistic images that enhance data augmentation strategies and improve the performance of machine learning models. These technologies are not only streamlining content creation but also enabling the generation of high-quality data samples for training and testing, which is especially valuable when labeled data is scarce.
However, the adoption of AI-generated content is not without its challenges. The effectiveness of generative AI models depends heavily on the quality and diversity of their training data. If the input data contains biases or lacks representation, the generated content may inadvertently perpetuate these issues, raising concerns about fairness and discrimination. Additionally, as organizations increasingly rely on large language models and other AI systems, questions around the ownership, copyright, and intellectual property of generated data become more complex. Ensuring the accuracy, reliability, and ethical use of AI-generated content is essential for building trust and maximizing the benefits of these advanced technologies.
Future of Generative AI: Emerging Trends and Opportunities
Looking ahead, the future of generative AI is poised for remarkable growth, with emerging trends set to redefine what AI systems can achieve. One of the most promising developments is the evolution of diffusion models, which are demonstrating unprecedented capabilities in generating high-quality images and realistic outputs. Alongside foundation models and advanced transformers, these sophisticated models are expanding the horizons of generative artificial intelligence, enabling AI to perform multiple tasks across diverse domains with greater coherence and contextual understanding.
Generative AI solutions are rapidly moving beyond the technology sector, finding applications in healthcare, finance, education, and more. In healthcare, for example, generative models are accelerating drug discovery and medical research by analyzing complex data and generating synthetic datasets. In finance, they are being used for risk modeling and scenario analysis, while in education, generative AI tools are personalizing learning experiences and content delivery. The integration of generative AI with reinforcement learning and explainable AI is further enhancing its capabilities, making these systems more transparent, accountable, and accessible to a broader range of users.
As generative AI continues to advance, the development of models capable of handling increasingly complex data and generating contextually relevant, high-quality content will be a key focus for AI research. Moreover, generative AI is expected to play a pivotal role in addressing global challenges such as climate change and sustainability, offering innovative solutions for data analysis, prediction, and decision-making.
Despite these exciting opportunities, it is crucial to address the ethical, privacy, and security challenges associated with generative AI adoption. Ensuring that generative AI systems are developed and deployed responsibly—prioritizing transparency, fairness, and accountability—will be essential for fostering trust and unlocking the full potential of this transformative technology. By embracing these principles, organizations can harness the power of generative AI to drive innovation, solve complex problems, and create lasting positive impact across multiple domains.
Conclusion: The impact of foundation models
Towards the end the panelists addressed several pressing questions around traditional BI vis-a-vis GenAI, estimating GenAI implementation costs, Trusting AI platforms with data privacy, to mention a few. By following the principles discussed during the webinar, organizations can not only implement GenAI solutions effectively but also build resilient, future-proof strategies that drive sustained innovation and trust.
At TO THE NEW, we are actively applying GenAI and digital engineering across diverse business use cases - improving efficiency, reducing costs, and enhancing user experiences. If you're exploring GenAI for your organization, let’s connect. We’d be happy to share our learnings, frameworks, and collaborate on building a responsible AI roadmap for you.