AI Readiness

With the advent of generative AI, using the power of artificial intelligence to create new products and services has become easier than ever. However, transforming themselves in the face of such disruption has not been easy for enterprises. AI transformation is not just about hiring data scientists or deploying advanced models; it’s about integrating AI holistically across business operations. 

AI readiness refers to an organization’s ability to embrace AI opportunities while managing the risks effectively. It is a holistic picture of an organization’s strategy, infrastructure, governance policies, culture, and depth of talent necessary to deploy AI applications.

This article explores the key factors contributing to AI readiness and the best practices for accelerating the AI journey. 

Summary of key AI readiness concepts 

To adopt and scale AI, an organization must focus on the following key areas:

The rest of the article explores these AI readiness concepts in detail.

AI strategy

An AI strategy is a structured plan for leveraging AI to achieve business objectives and improve efficiency. It focuses on two key areas.

1. Driving business outcomes like increasing sales, reducing costs, and enhancing customer experiences.
2. Boosting employee productivity through automation and intelligent insights.

An organization’s journey towards AI readiness starts with defining an AI strategy. A well-defined AI strategy ensures AI investments align with organizational goals for maximum impact. It identifies clear AI objectives that are aligned with business priorities. On a high level, forming an AI strategy involves the following steps.

Develop business goals

Decide what you want AI to accomplish, such as increasing revenue, reducing costs, mitigating risk, or enhancing customer experience.

Identify use cases

Prioritize AI applications according to feasibility, impact, and alignment with core business processes. AI use cases include automated customer segmentation, predictive analytics, and anomaly detection.

Assess AI readiness

Before committing to AI initiatives, assess data availability, infrastructure capabilities, and workforce expertise. Establishing your current status is the first step towards achieving AI readiness. 

Once the business goal definitions, use case documentations, and readiness AI assessment are complete, organizations can move to the next step: preparing a detailed AI roadmap. 

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AI roadmap

AI roadmaps provide a structured approach for implementing artificial intelligence throughout an organization.  Your roadmap should integrate AI incrementally, beginning with small-scale projects and gradually scaling them up. A typical AI road map contains a set of projects arranged in the order of execution, explicitly mentioning the development and production phases. Below is an AI road map example for a bank that has identified several AI use cases. 

Example of an AI roadmap

Each project mentioned in the road map undergoes an iterative process of data collection, preparation, proof of concept model development, evaluation, deployment, and monitoring. Most organizations go through several iterations of individual phases during the implementation. 

Once the use cases are shortlisted and prioritized into a backlog, the next step is establishing the data requirements for the use cases and getting the right data points ready for consumption. 

AI data readiness

Data readiness signifies whether the data in your organization is in a state where it can be used to derive value using AI. Data readiness is the foundation of AI readiness since everything else, like AI strategy, roadmap, and AI governance, relies on data readiness.

On a high level, your organization’s data readiness is represented by its data quality, metadata, lineage, data products strategy, and the level of data integration.

Data quality

During inference, LLMs depend on high-quality, unbiased, consistently updated datasets as context to make decisions accurately. Data must be complete and accurate without any missing fields. It must be consistent, free of drift, and adhere to the same relationships across all the sources. The availability of up-to-date data is another aspect of data quality. Defining validation rules about these factors and building automatic validation within the ecosystem can help continuously monitor data quality. 

Most organizations use pretrained LLMs for their Gen AI integration. The pre-trained LLMs usually have a knowledge cut-off based on the timeline to which training data is included. Changes in language usage, like new slang, evolving meanings, etc, may be unknown to outdated LLMs, leading to erroneous output. Emerging trends, newer product information, etc, that are present in input data can also lead to incorrect output. 

Metadata and lineage

Metadata is the descriptive information about data that provides the context, structure, and meaning to data. Metadata is critical information for Gen AI architectural patterns like RAG since it helps shortlist the relevant information before sending it to the LLM for final response formation. A key metadata component is data lineage, which traces the data’s origin and current state. 

Data product lineage represents its origin, transformations, final form, and location. Lineage information is key for ensuring trust in AI model output and helps in faster root cause analysis when unexpected outcomes occur. 

Since lineage tracks the data versions, it also helps reproduce model results. The ability to reproduce model results enables organizations to revert to any model state while troubleshooting. 

Data traceability is also required for data privacy standards like GDPR, HIPAA, and AI-specific compliance standards like the EU AI Act. Integration platforms like Nexla automatically track the data set’s lineage by assigning a tracker.

Nexla data lineage view

Data products

A data product is a reusable data asset with clear ownership, documentation, quality guarantees, and, more importantly, user focus. Data integration platforms designed with data products as the base premise help organizations become AI-ready without much additional effort. 

For example, Nexla uses the concept of Nexsets. This human-readable, virtual data product turns any source with any unstructured or structured data into a logical data model independent of formats, protocols, and data speeds. It encapsulates schema, sample data, data validation, error management, audit logs, and access control behind a common interface to make data ready-to-use and easily consumable.

Nexsets are AI-ready, and one can use LLMs to query them directly using Nexla-orchestrated versatile agents (NOVA). Since metadata is already part of the Nexset concept, one can quickly build RAG architectures that rely on metadata for hybrid search. Nexla also lets one chat with the data to analyze it and build transforms. 

Integrated data

Fragmented data is the biggest bottleneck in realizing value through AI integration. For example, an AI that recommends products on a retail website must have information about previous purchases, returns, exchanges, and even the support tickets raised by the user. This will involve fetching data from the inventory, shipping, and CRM platforms in a typical organization. 

Data integration helps to combine data from disparate sources across the organization and create a unified view with a consistent format. It is essential to build AI features that require cross-domain information and behavioral and temporal patterns. A data integration platform reduces the time to market for your AI integration. It provides reusable components that can be stitched together to achieve unified data. 

Typical features that one must look for while choosing a data integration platform are:

• Comprehensive connector support for the data source and destination
• Low-code interface for accelerated development.
• Designed with data products as the foundation.
• Built-in continuous metadata intelligence

AI infrastructure

Once data readiness is established, building a robust AI infrastructure is the next critical step toward AI readiness. This involves setting up scalable cloud platforms, deploying high-performance computing resources, and integrating MLOps pipelines to support model training, deployment, and monitoring at scale. The infrastructure includes databases, data processing engines, and GPU virtual machines for deploying and training models. The infrastructure choices impact the scalability, security, cost, and flexibility of AI solutions. 

Gen AI usage patterns

Before defining infrastructure requirements, one must have a high-level understanding of typical enterprise Gen AI usage patterns. Typical enterprises deploy generative AI using the following patterns. 

RAG

Retrieval-augmented generation (RAG) enhances model performance by incorporating external domain-specific knowledge, metadata, and relevant data. The key moving parts of a RAG pattern are LLMs, vector databases, and databases or data APIs that retrieve information specific to the organization. 

Agentic workflows

LLMs can be used to build workflows that perform tasks based on goal-oriented, dynamic decision making. This is different from traditional workflows that relied on predefined programmatic logic implementation. 

Fine-tuning

Fine-tuning involves adapting pre-trained models to industry-specific needs by leveraging custom datasets. A good data integration platform streamlines the whole process by automatically tracking the training and deployed versions. You can learn more about fine-tuning an LLM here. 

Building foundational models from scratch

Organizations can develop large-scale models for specialized use cases requiring high-performance computing (GPUs/TPUs), scalable storage, and AIOps pipelines for training and deployment. This is very rare in the current era, with generic LLMs being suitable for almost all problems. 

From the above architectural patterns, it is evident that deploying Gen AI requires a couple of key components that are not commonly found in traditional AI pipelines. They are large language models and vector databases. Let us explore the infrastructure options for deploying them.

Deploying LLMs

For deploying LLMs, organizations have two primary deployment options: 

1. On-premises managed by the business
2. Cloud infrastructure managed by a third-party provider. 

For on-premises AI deployment, only open-source models are typically available. Most closed-source models are available for consumption only through cloud APIs. For example, models like Open AI, Gemini, Claude, etc, are available only as APIs. Models like Llama, Gemma, etc, can be deployed on-premises in your GPUs or a cloud provider. Models like Mistral offer both. They can be used as cloud-based APIs or deployed on your infrastructure.

In the modern scattered data environment, choosing between on-prem and cloud and sticking to it long-term may not be feasible. Organizations must be flexible enough to use the above based on constraints like cost, business requirements, model availability, etc. 

A flexible data integration framework with comprehensive AI support reduces the risks associated with deployment decisions. They allow engineers to integrate any deployment mode easily without refactoring much code.

Deploying vector databases

A vector database helps store, index, and query high-dimensional vector embeddings. Embeddings are mathematical representations that capture the meaning of data, such as images, text, video, and audio. Vector databases are used in AI architectural patterns like RAG, meaning-based matches are more relevant than exact word matches. Like LLMs, vector databases can also be deployed on-premise or via cloud APIs. Open source databases like FAISS, Milvus, Weaviate, etc, can be deployed on-premise. Databases like AWS Kendra and Pinecone can only be accessed by cloud APIs. 

AI governance

As AI becomes more integrated into business operations, it is critical to implement safeguards that ensure model reliability, fairness, and safety. AI governance ensures responsible AI usage by establishing policies, processes, and controls for AI model development and deployment. This involves automated validation of AI outputs, managing privacy concerns, building explainability into the inference workflows, and ensuring that the model remains reliable throughout its life cycle. 

AI output validation

Generative AI models can sometimes produce unintended, biased, or harmful outputs. The first step towards controlling the AI output is to establish if there is a problem. This can be done by adversarial testing that challenges the models with edge-case data. 

A naive way of blocking harmful output is to use a rule-based filter to block specific harmful outputs. Another method is to use a small language model explicitly trained to detect harmful responses before feeding them to the user. Compared to rule-based approaches, this mechanism introduces more latency.

Another key safeguard for controlling AI outputs is using a human-in-the-loop (HITL) system. Human experts review and validate the outputs before they are deployed or acted upon. This is particularly important when AI models make high-stakes decisions impacting people’s lives, such as in healthcare, finance, or criminal justice.

Bias in AI models is a critical problem in achieving responsible AI. One can use libraries like fairlearn to assess the fairness aspect of model output. Such assessments are not a one-time process. They need to be part of an automated continuous monitoring effort. 

Managing privacy concerns

Being AI-ready involves adhering to AI-specific guidelines like the EU AI Act and sector-specific guidelines, such as FCRA, which deals with the privacy of bank customers, or FERPA, which deals with the privacy of student records. These privacy regulations dictate that identifiable data must be anonymized, and only the most necessary data is collected and processed. Data used for AI training and inference must adhere to privacy guidelines regarding individual rights. 

Using a data integration platform like Nexla with built-in features for PII detection, dynamic masking, and adherence to the zero data copy principle can help improve the privacy and compliance posture. 

Explainability & interpretability

Explainability and interpretability are crucial for making AI models more understandable to stakeholders. With the complexity of AI models, it can be challenging to understand their decision-making process. 

A key element of understanding model output is having complete confidence in the data used to train the model and infer from it. A data integration platform with active metadata intelligence and automated lineage capture can solve this problem. 

Understanding what goes inside a model during inference is another challenge. Traditionally, this was accomplished by using frameworks like Shap and Lime. These frameworks train a parallel model specifically to explain the outputs of the original model. This approach does not work well with modern transformer-based architectures and decoder-only LLMs. 

For LLMs, frameworks like Bertviz can help visualize the attention layer. But they are difficult to understand and interpret. Instead, prompts containing instructions to explain the reasoning and the final output can help bring some explainability. One can request the model to explain the reason behind its response by issuing a second prompt with both the original query and generated output.

This can work well in situations where the monitoring program does not have control over the application prompts. One can use a data integration platform like Nexla with LLM output monitoring support to configure the second prompt to run automatically. 

Ensuring model reliability

AI models tend to gradually lose effectiveness because of external factors like data drift, change in consumer preferences, etc. They require continuous monitoring to ensure that any drop in performance is quickly identified and fixed. Version control for models and data also plays a critical role in systematically identifying issues and model upgradation. 

AI talent

The technical team itself is the last but not least important component of being AI-ready. An organization that aspires to be AI-ready needs a strong team that is cross-functionally aligned, embraces collaboration, and continuously evolves. To build a successful AI team, one needs expertise in the following areas:

• Gen AI, Machine learning & deep learning – Using LLMs, understanding neural networks, supervised and unsupervised learning, and reinforcement learning.
• Data engineering – Implementing ETL processes and data pipelines through distributed computing.
• AIOps – CI/CD implementation, monitoring, and versioning for AI models.
• Domain knowledge – Expertise in the application of AI in specific industries.

By developing in-house AI expertise, businesses reduce reliance on outside vendors and enhance their decision-making abilities. Training programs, upskilling initiatives, and university partnerships can build a sustainable AI talent pipeline. Low-code platforms like Nexla enable non-experts to contribute to AI projects, democratizing AI adoption and reducing the skills gap.

Recommendations

Getting large enterprises AI-ready is no small feat. It requires careful planning, budgeting, and focused effort towards choosing the right tools, infrastructure, risk management, and continuous monitoring. The following section provides essential best practices to guide your AI readiness journey. 

Align AI strategy with business goals

The key here is to start with business priorities and not technology. With the ongoing hype around AI and newer models, frameworks, and architectural patterns popping up frequently, it is easy to fall into the trap of implementing cool technology. Instead, one must start with high-impact problems, evaluate their feasibility, and establish the possible return on investment before considering technology. Organizations must also develop success metrics based on KPIs related to business goals early in their AI readiness lifecycle. 

Estimate the effort in scaling POCs to production

With several no-code platforms and countless quick demo guides available all over the internet, it is very easy for engineering teams to develop ‘wow’ inducing proof of concept applications. Taking such POCs to production is a different ball game. It requires effort to ensure consistent LLM output, proper metadata-based context retrieval, and explainability. 

The effort spent getting the organization AI-ready starts reflecting during the transition phase. Hence, it is essential to consider the efforts required to scale POCs to production while making plans to be AI-ready. 

Focus on data readiness and choose the right tools

The most critical part of being AI-ready is to ensure data readiness. It serves as the foundation for AI strategy, roadmaps, and infrastructure. Organizations need to ensure the data used for training and inference is of good quality, with adequate metadata, lineage information, and proper access control. Building such a data environment with custom development can take years to provide results. Low-code or no-code data integration tools with built-in support for metadata intelligence, automatic validations, and role-based access control can help build data platforms quickly. 

Explainability is key

With several legal guidelines and domain-specific compliance regulations in most geographies, it is now impossible to roll out AI applications that lack explainability to production. Despite this, explainability is often overlooked during the POC building process, leading to problems while transitioning to production. Retrofitting explainability in later stages of AI development is nearly impossible and needs to be thought through in the POC stage itself. In deep learning or statistical machine learning projects, one must focus on selecting interpretable models. In Gen AI projects, engineers should implement prompt segments that ask the LLMs to explain the reasoning behind their outputs. 

Define a risk management framework

Security breaches and compliance violations are billion-dollar affairs in the modern era. Hence, defining a risk management framework at the start of your journey towards AI readiness is crucial. It helps organizations to proactively identify, assess, mitigate, and monitor risks associated with AI systems. Implementing a risk management framework involves identifying and forming mitigation plans for the following risks.

• Model risk: Risks associated with wrong outputs
• Data risk: Problems because of poor data quality and drift
• Operational risk: Failures in integration, scaling, etc
• Regulatory risk: Compliance violations related to regional laws and regulations
• Security and privacy risk: Vulnerability to PII data leak, adversarial attacks, etc.  

Establish governance policies early

While data governance is a subject with well-defined processes and standards, AI governance is still an evolving field with several vague areas. AI governance is critical in bridging the gap between experimental AI and enterprise AI. Just like data, AI models must adhere to principles of accountability and role-based access control. Once a model is trained with organizational data, there is always the risk of unauthorized people getting access to it and inadvertently having access to secure information through the model output. In certain cases, it is even possible to use adversarial methods to extract PII information. Therefore, it is essential to establish a formal AI governance policy and appoint a dedicated committee with clear stakeholder ownership to oversee the implementation. This exercise must be executed as early as possible in the AI readiness journey. 

Conclusion

AI-readiness refers to an organization’s ability to integrate AI while managing its associated risks. Getting to this stage requires a strategic approach, a robust infrastructure, high-quality data, and AI talent. While defining the strategy and having a road map is relatively more straightforward, defining the infrastructure, getting the data ready, and establishing AI governance are herculean tasks. Executing them from scratch using custom development can take years. Low-code data integration frameworks can make this journey easier. Tools like Nexla can enable organizations to build AI-powered products and services more efficiently.