Generative AI (GenAI) is moving at breakneck speed, opening up a world of new possibilities in the enterprise space—from boosting productivity through task automation to creating hyper-personalized, immersive customer experiences. But while GenAI is taking off in the consumer world, enterprises need to ensure they’re taking a more structured approach to enabling it in their business.
In a global survey of IT decision-makers by Enterprise Strategy Group, 42% of respondents said their organization is using GenAI for business and IT use cases, while 43% are currently in the planning or consideration phase. However, many businesses are also struggling to address data issues, cost implications, skills gaps, and governance, compliance, security, and privacy concerns.
Amid these concerns, getting started on the journey to GenAI may seem intimidating or overwhelming. But it’s important to take those first steps since this evolving technology has the potential to profoundly impact your business processes and your people. It starts with understanding the ‘why’ behind an implementation and then building a solid foundation for GenAI based on responsible use guidelines.
Here are some considerations to help you get started on your GenAI journey:
Assess your business maturity
As a starting point, organizations should ensure they already have a mature data ecosystem. How mature is your data governance practice? How are you securing your data and ensuring compliance with privacy regulations? Do you have clear mapping between your data policies and compliance obligations? If you don’t already have a solid foundation for GenAI, you’ll need to build one before embarking on this journey.
Prep your data
As organizations look at deploying GenAI, they may find their data to be of varying quality and their data workflows to be overly complex. Data needs to be trustworthy so business leaders can make data-driven decisions, and it should be organized in such a way to ensure prompt access by data consumers. This process will likely lead to conversations about where there are gaps in the organization, which can be remedied by maturing your data governance practices.
Create guidelines for responsible use
The challenge for all organizations will be creating policies where none yet exist. Before deploying any technologies, ensure you’ve created policies and guidelines as part of a responsible use framework, and ensure all stakeholders (both internal and external) are on board. While there aren’t yet any formal standards around the use of GenAI, it does require compliance with data-related legislation and regulations, including contractual limitations on the use of customer and vendor data.
Educate your users
GenAI changes how people work, which means it requires organizational transformation—and that starts with education and training. There’s also an increased risk of improperly generated content leaking into the public domain, creating brand-trust issues. All employees should understand how GenAI and modern analytics will be used—or not used—in the organization and how they can use it ethically, securely, and in compliance with data legislation and regulations.
Start building out infrastructure
From there, you can start installing technical infrastructure to back up your organizational policies, ensuring that any new capabilities integrate with existing systems and workflows. If you’re a Google shop, you can explore GenAI functionality in tools like Vertex AI, which allows you to interact with, customize, and embed foundation models into your applications—no ML expertise required. Google Cloud continues to make GenAI more accessible with baked-in security, scalability, data governance, and the ability to customize models using your own data.
Prioritize initial use cases
Once you have a foundation in place, as well as a responsible AI framework, you can begin prioritizing potential use cases that can be empowered by GenAI. Determine which low-hanging fruit you can implement, such as an enterprise search or conversational AI solution, to gain stakeholder buy-in before moving on to larger projects.
Pilot one or more GenAI technologies
Once you identify a use case, determine the prerequisite technology changes you’ll need to make in order to create a proof of concept and validate the results. If it works—and you’ve met all the governance and compliance requirements—you can then move into the production stage.
Start getting GenAI in place, the right way
Organizations should view GenAI like any other business tool. Rather than jumping on the bandwagon, consider what problems you’re trying to solve with GenAI and whether you have the in-house expertise to solve for those problems. As you explore the possibilities, you may find gaps in skill sets, such as application development, data science, machine learning, and software engineering.
That’s where a partner can 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.
With our AI/ML Services and our Generative AI Workshop, we can help educate your senior team on the key tools for the creation and execution of your AI strategy with facilitated conversations to create alignment on key strategy priorities, work dependencies, and value measures.
Connect with Pythian to see how we can help you on your GenAI journey.
