Introduction
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:
1. Model selection: Purpose-driven strategy
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.
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.
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, and security.
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.
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.
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.
2. Implementation and deployment: From Proof of Concept to production
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 GenAI 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.
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 that requires to have a clear deployment strategy, a modular system design that allows for future upgrades (once more advanced LLMs are available - remember the speed at which this is evolving? Darwin?) and robust data engineering services pipelines for model training.
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 GenAI 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
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.
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 AI against key metrics like accuracy, drift, bias, and hallucination. As this will be an ongoing process, use of automated monitoring tools can be of help.
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
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.
Conclusion
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.