We set the stage by defining the different types of AI and their relative stages of maturity in our article “Demystifying AI to help you deliver impact at scale.”
Through our experiences, we’ve identified a pragmatic approach to AI adoption that can help accelerate your AI transformation journey. Data powers all variations of AI; however, most companies we’ve worked with have less-than-ideal data foundations. Typically, company data is in silos, incomplete, lacking in quality, or all of the above.
The following is our advice on how to sequence and right-size your efforts and avoid common pitfalls.
Data first, AI second approach – If your data is not up to the task of either traditional or generative AI, your efforts will struggle to be successful. Creating a solid data foundation will help ensure reliable insights and outcomes and save time down the road. Our 3-pillars of advice here include:
- Break down data silos. Invest in foundational data projects first. Identify what you need for a specific use case, get that data in order first, and then leverage it for your AI projects.
- Improve data quality and consistency. Implement a robust data governance framework, including clear data ownership and stewardship roles, data quality metrics, and regular audits, supported by automated data cleansing and governance tools. Proper governance can help identify and rectify errors, standardize formats, and enforce data quality in real-time.
- Build in integrations and scalability. An API-first architecture, microservices, cloud-native architecture, and modular design simplify integration of Gen AI solutions as they facilitate seamless communications and scale.
Data privacy and security cannot be overemphasized – Ensuring data privacy and security is paramount (especially with ever-increasing regulatory demands). Even more so, when using generative AI, it’s essential to understand how data can escape or how false data (hallucinations) can creep in. Our 3-pillars of advice here include:
- Protect sensitive data. Ensure that your sensitive company and customer data, such as financial records, personally identifying information, or trade secrets, do not become part of the training for AI models. Gen AI models don’t have ‘filters,’ and if sensitive data falls into the models via training, that information may be inadvertently disseminated to whoever consumes the Gen AI output.
- User controls. Users must be cautious when interacting with GenAI models to avoid inadvertently sharing sensitive company data, personal data, or context information that is then rolled into model training.
- Regulatory compliance. Data privacy and AI regulations are continually evolving, and many obligate companies to have capabilities in place to comply. For example, California states that companies must be able to recall where a user’s data is stored and delete it if requested. If a user’s data gets rolled into an AI model, it may be impossible, or at minimum, very difficult to delete that data from the model.
Organization, process, and policy design are essential enablers – Implementing AI strategies requires specialized talent and a cultural shift within organizations, often impacting the entire enterprise. Resistance to change and a lack of buy-in from key stakeholders can impede adoption and successful outcomes. Our 3-pillars of advice here include:
- Close skill gaps. Promote a culture of continuous learning and cross-training, including encouraging employees to work side-by-side with data and AI consultants for on-the-job training and sponsoring them for online courses and workshops.
- Be budget conscious. Prioritize AI use cases using product management best practices, starting with lower effort and higher impact projects to deliver quick wins and prove ROI. Be intentional about design to minimize processing costs.
- Integrate change management. Engage stakeholders (starting with executives) early and often to gain buy-in, address concerns, and share frequent wins and progress. Implement a comprehensive change plan that includes regular communications, AI ambassadors, team member training, feedback solicitation, and support to align the team with organizational goals, generate excitement, and ensure sustained adoption.
Accelerate your company’s AI journey today.
The AI landscape is evolving at an unprecedented pace, with new opportunities and concerns being raised daily spurred by rapid innovation, innumerable use cases, learnings from personal experiences, and an evolving regulatory environment. Unquestionably, advancing your company’s AI capabilities is complicated, but it’s equally a guarantee that you will realize significant benefits. These benefits can range from improved efficiency and productivity to customer engagement and competitive advantage.
Our best advice is threefold, whether you’re a leader or a laggard. First, don’t let perfection be the enemy of progress—your data will never be perfect; instead, aim for incremental improvements and embrace experimentation. Second, you don’t have to build AI solutions in-house to benefit from them—look to your partners’ expertise and product development muscle to advance your company’s outcomes through their investments in AI. Finally, harness the kinetic effects of the media buzz around AI to catalyze your organization to go faster on your quest to realize the transformational benefits AI can offer.
For more insights on how to leverage data, analytics, and AI in your business, contact a Cuesta Partners expert today.
