Adopting and deploying Generative AI within your organization is pivotal to driving innovation and outsmarting the competition while at the same time, creating efficiency, productivity, and sustainable growth.
Acknowledging that AI adoption is not a one-size-fits-all process, each organization will have its unique set of use cases, challenges, objectives, and resources. The framework below tries to recognize this diversity and provides foundational pillars and key considerations to take into account when planning for effective adoption and deployment of Generative AI within your organization:
1. Common Language
One common stumbling block in AI adoption is the lack of a unified understanding across departments and stakeholders. By establishing a shared language, we pave the way for smoother communication and collaboration throughout the adoption journey. To do that, initiate a discussion to define AI and explore its diverse benefits for you, your department, and the organization at large. Make sure all stakeholders have a normalized understanding of what it means (and what it doesn’t).
2. Use Cases
Where you start holds paramount importance. You must formulate use cases that align with data accessibility and availability, and to drive competitive innovation, you might also need to venture into uncharted territories, where the benefits outweigh the risks – consider the delicate balance between being “risk-averse” and “reward-driven,” as you evaluate and prioritize your Generative AI use cases.
When weighing the various business use cases, precision becomes non-negotiable. Employing generic Large Language Models (LLMs) models that produce directionally accurate yet non-specific responses is suboptimal. Models such as GPT-4 cater effectively to consumers seeking recommendations or insights derived from an expansive array of data. While these models might satiate a company’s initial curiosity about LLMs or serve as a viable Proof of Concept (POC) in their current state, they fall short when confronted with the demand for accurate responses to deep, domain-specific knowledge queries.
That being said, when contemplating widespread business integration of Generative AI, particularly within an enterprise setting, three frequently requested use cases cited in our recent Fortune 1000 survey demand substantial access to internal documents and organizational data to yield precise and beneficial outcomes. These are:
A) A content recommendation engine catering to the support of internal teams
B) An assistant for strategy, corporate planning, and finance
C) Integration of Gen AI as a product feature
The models tailored for these use cases will likely necessitate deployment on-premise or on a secure private cloud to safeguard company data and intellectual property. This implies that your business will potentially need to make an investment in building in-house teams to support multiple models for diverse use cases.
3. Prototyping for Accelerated Adoption Proof of Value (POV) versus POC
When evaluating solutions, especially when adopting new technologies, prototyping is a critical decision in Gen AI adoption. Our framework advocates for enterprises to prototype with real data, while choosing a simplified approach such as proof of value (POV) to enable them to fail forward faster and evaluate narrow success metrics as opposed to complex, cross-departmental POCs. When companies contemplate wide-scale adoption of large language models, fine-tuning becomes a critical step. Make sure to go through the process of assessing the viability and types (AI-as-a-service, open source, etc.) of LLMs for your specific use cases.
Enterprises often spend a lot of time and money defining a plethora of use cases, but they can speed up the process by prototyping with real business data to quickly assess MVPs and viability while taking into consideration security, compliance, and IP protection. As organizations contemplate widespread integration of LLMs for various use cases across their workforce or within applications, it’s imperative to recognize that foundational models, while equipped with logic and command comprehension, lack the intrinsic knowledge of your unique business.
4. Infrastructure
AI talent and deployment teams assume that the basic infrastructure required, such as AI and MLOPs Frameworks, hardware limitations, Container platforms, Cloud Ops, data storage, data science framework, data engineering and knowledge worker feedback loop and the ability to run multiple programming techniques, is in place. Make sure you audit this carefully and actually have the required infrastructure to support your Generative AI adoption and deployment at scale.
5. Talent & Knowledge
The success of any AI project hinges critically on the capabilities of your AI/ML team. Often, internal talent may lack specific skill sets required for the task at hand, necessitating the hiring of external experts to spearhead Generative AI initiatives at scale. If you choose to hire an external team for the initial phase of your deployment and adoption of Generative AI, your existing team can shadow the external talent and gradually build in-house expertise over time.
A key distinction between an LLM and a conventional Machine Learning (ML) model lies in the critical need for active Quality Assurance (QA). Notably, the data engineers responsible for building and training LLMs often lack subject matter expertise. Involving internal stakeholders and knowledge workers in testing and providing feedback on model accuracy, leveraging reinforcement learning through human feedback (RLHF), becomes exceptionally important due to the generative nature of LLMs. This data should be promptly fed back into the model fine-tuning process to ensure ongoing optimization based on your unique knowledge.
6. Benchmarking, Communication & TCO
Establish your success criteria from the outset; otherwise, you risk lacking a standardized method for evaluating whether the output aligns with the objectives, budget, and Key Performance Indicators (KPIs). For organizations and AI leadership, it’s imperative to craft an effective, strategic approach to calculating, forecasting, and controlling costs tailored to their own organization and its unique business use cases.
One way to do that is to use ClearML’s Gen AI Total Cost of Ownership (TCO) calculator, factoring in diverse cost drivers and variables such as your use case, company size, compute provider, the number of end users for the model, data corpus type and volume, and the chosen Large Language Model (LLM) API. Through this, you can estimate the costs associated with data preparation, required human capital and its associated expenses, as well as the amount of compute or tokens needed for optimal functioning.
7. Decision to Buy (AI-as-a-Service) versus Build
When deciding between purchasing AI-as-a-service or building your own solution, the choice depends on factors such as your use case, scale, and usage patterns. While Generative AI holds the promise of unprecedented business opportunities, it comes with considerable uncertainties and business risks, particularly concerning the potential of escalating running and variable costs, especially at an enterprise scale.
Monitoring and forecasting these costs can be challenging, especially considering that 57% of F1000 C-Level respondents in a recent ClearML/AIIA survey indicated their boards anticipate a double-digit increase in revenue from AI/ML investments in the coming fiscal year, with an additional 37% expecting a single-digit increase. Navigating the uncharted territory of hidden operating costs related to Generative AI may seem unfamiliar and unpredictable. However, to strike a balance between investment and expected outcomes, it is imperative to develop an effective, strategic approach to calculate, forecast, and contain these costs, tailored to your organization and its unique use cases. Consider using tools and platforms that allow your organization to control and create AI models directly from your internal business data, without losing any AI-as-a-Service capabilities.
8. Data, Data, Data
Data is the bedrock on which AI success is built. This is the official, often-underestimated and overlooked critical component in our proposed framework. It involves extensive and honest internal alignment discussions based on your organization’s true data availability, quality, complexity, and policies. It requires early involvement of governance, risk, and compliance stakeholders for approvals. The structured and unstructured data corpuses and data sets must be clean, and your unique data ingestion (and logic) can be a very challenging step. However, there are a few tools in the market that offer low-code, secure, end-to-end LLM platform for the enterprise, featuring data ingress, training, quality control, and deployment, such as ClearGPT, with pre-built wizards that make the process simple and replicable for future updates.
Interested in learning more about how to leverage AI & machine learning in your organization? Request a demo of our Gen AI platform, ClearGPT. If you need to scale your ML pipelines and data abstraction or need unmatched performance and control, please request a demo of ClearML.
