Developing an Effective Generative AI Strategy

Generative AI is a rapidly evolving field, and now is a time to experiment, explore and iterate.?But how do you create an effective AI strategy to ensure that this powerful tech is aligned with the needs of your business? Here are a few ideas to consider when developing an AI strategy.

Step 1: Define Objectives and Use Cases

Fall in love with the problem, not the solution.?Ask yourself what problems do you want to solve, for your customers, and for your teams? Prioritise a list of use cases that align with your strategic goals. Do you have data that can help you solve these problems??

Step 2: Create a Data Strategy?

The fuel for Gen AI is data. Do you already have the data you need or do you need to collect more??What preparation is needed in order to make use of your data? For instance, does the data need cleaning or transforming to a different format. Is your data stored appropriately and on the correct platform, enabling it to easily be accessed by AI models??What data privacy and security considerations do you have?

Step 3: Identify Models

When it comes to selecting model, there is no one-size-fits-all answer. Instead, you'll need to identify one or more models, based the use cases you have prioritised, and begin experimenting.?Getting started is easiest with pre-trained models, and then fine-tuning for your domain, for instance, augmenting pre-trained models with custom data, and tuning hyper parameters to enhance model accuracy.

Step 4: Understand Your Infrastructure Constraints and Requirements?

Training models is computationally intense, requiring massive compute for training and operation.?Generative AI projects are therefore likely to be highly dependent on the cloud to be successful. Cloud providers like AWS, Azure and Google Cloud offer high performance computing options that are optimised for AI workloads, including the ability to securely provision your own model endpoints within a virtual private cloud.

Ask yourself what infrastructure constraints do you have today, that need to be considered??Which cloud providers are you already invested in? Do those providers offer the services that you need?

Step 5: Address Model Training and Validation

You can customise models by training them using your own data, however training models is costly.?Experiment using RAG (Retrieval Augmented Generation) which is a method of supplying custom data with prompts to ensure the latest data is being referenced, which means that you can customise without the added overhead of re-training the model unnecessarily.

Define validation tests to evaluate accuracy, identify hallucinations and bias.

Step 6: Define Appropriate Governance for Responsible AI

Establish standards around how you will implement responsible AI, which might include governance around preventing hallucinations and bias, preventing the creation of harmful content, as well as cost optimisation and sustainability. Define security standards, to protect against data leakage, and ensure privacy.

What regulatory compliance do you need to adhere to??For instance if your systems contain Personally Identifiable Information, do you need to ensure that this data is rejected from model inputs, or redacted?

Step 7: Enable Continuous Learning and Innovation

Encourage a culture of innovation and experimentation within your organisation. Stay up-to-date with the latest advancements in generative AI and adapt your strategy accordingly. Continuously improve your AI systems based on user feedback, to enhance usability and value.

In Summary

As we navigate this new era of AI augmented decision making, problem solving and content creation, an effective strategy will ensure that your organisation is in the best position to securely and ethically unlock the value of your data, stay competitive, and thrive in these exciting times.