Not sure how to get started with Generative AI (GenAI)? Some organizations are sitting on the fence, while others are jumping on the bandwagon. Wherever you are in this evolution, it’s important to start with maturing your data ecosystem.
Enterprise maturity plays a critical role in the successful implementation of GenAI. It’s not just about having the right technology, but about building a strong data and analytics ecosystem supporting its rapid advancement.
But be prepared for that ecosystem to be very different than in the past. Vendors are rushing to out-compete each other, GenAI tools are rapidly evolving, and the compliance landscape is struggling to keep up.
Fortunately, there are tools available that can make GenAI more accessible. For example, Google has introduced GenAI support in Vertex AI, its artificial intelligence platform. Vertex AI offers tools for tuning, deploying, monitoring, and maintaining models to build applications, while keeping data protected, secure, and private.
But before you turn to those tools, you’ll need a strong foundation. Whether you’re still considering possibilities or have already started experimenting with Generative AI, here are some key enterprise considerations for assessing your GenAI readiness, so you can build a solid foundation for the future.
Is your data ecosystem ready?
As a starting point, your organization should have a mature data ecosystem in place. For example, data should be organized in a way that ensures prompt access by data consumers. It also needs to be high quality, so leaders can trust it to make data-driven decisions.
These factors mean understanding where you are in the data maturity cycle. Have you unified all of your data silos? Have you identified deficiencies in data quality? Do you have clear control over data owners and what decisions they make?
Draw on technologies like Google BigQuery, Google Cloud Composer, and Google Cloud Dataflow to decompose all data pipelines to the smallest possible steps.
Is your governance structure ready?
As you assess your data ecosystem, you may find that your data is of varying quality and that data workflows are overly complex. Identifying these issues is an opportunity to assess the maturity of your data governance practice—and remedy any issues.
For example, how are you ensuring compliance with privacy regulations? And do you have clear mapping between your data policies and compliance obligations?
Is your security framework ready?
GenAI comes with a host of new security risks, from data leakage to copyright infringement. As an example, there’s an increased risk of improperly generated content going public, creating brand-trust issues—so it’s critical that your IT environment is configured properly.
For example, do you have appropriate user access controls? Have you implemented enterprise-level application security? Robust security is essential to ensure alignment with data protection, zero-trust, and privacy by design.
Train and skill up your InfoSec team on new and existing technologies—such as Security Command Center and Mandiant—and design patterns.
Is your IT team ready?
Most organizations lack expertise around GenAI since it’s a rapidly evolving area without consistently applied standards. Creating conversational AI solution sets requires skill sets in machine learning, application development, and front-end UI design.
Does your organization have advanced analytics expertise, such as machine learning? Are your teams ready and trained for this change?
If not, your in-house team can be trained to develop or augment their skills to gain organizational capabilities around GenAI, or you can work with a partner that has the necessary skill sets already.
Are your workers ready?
GenAI changes how people work, which means it requires organizational transformation. Do you have education and training in place for users, so they understand how GenAI will be used—or not used—in the organization? Do your users understand how they can use GenAI in an ethical, secure, and legislatively compliant way?
Is your GenAI roadmap ready?
Before implementing GenAI, you need a strategy, a roadmap and organizational policies around its responsible use. For example, are you able to integrate GenAI while protecting your intellectual property and adhering to regulations around data security and privacy? And how do you plan to evolve your data governance practice for GenAI models that are trained in a fundamentally different way?
Is your infrastructure ready?
Your technical infrastructure should back up your organizational policies, ensuring that any new capabilities integrate with existing systems and workflows.
If you’re a Google shop, Google Cloud is making GenAI more accessible with purpose-built AI infrastructure, scalable application integration, and secure and private data customization, ensuring models are cost-effective, secure, and manageable.
Taking the next step with GenAI
If you find any gaps when assessing your readiness for GenAI, start by leveraging our AI/ML services and GenAI workshop, so you have a strong foundation from which to build GenAI. Need help? By providing cost-effective GenAI discovery, proof of concept, and production offerings in concert with our existing data services, Pythian is helping companies use GenAI to drive rapid innovation and enterprise transformation.
Once you have a strong foundation in place, you can confidently move forward on your GenAI journey—where the transformational possibilities are endless.
Looking for more help on your GenAI journey? Download our latest eBook, GenAI and Google Cloud: Capitalizing on the Art of the Possible.
