Two artificial intelligence must-haves: an innovation culture and data literacy

In my work as a data scientist, I have a front-seat view of the power of artificial intelligence (AI) in turning vast data into actionable insights—insights that transform how organizations operate and the outcomes they achieve. The potential and promise of AI, and in particular generative AI (GenAI), have captured the world’s attention, but the path to successful implementation can be daunting, as AI technologies and use cases rapidly evolve.

What is the secret to AI-driven innovation?

In helping both commercial and government clients understand where AI can take their organizations, I’ve learned that there are certain fundamentals required for success, regardless of the type of organization, its industry or its ambitions. To make AI success possible, we must focus on building an innovation culture and ensuring data literacy, all while grounding our strategies in a responsible framework.

Does AI really drive innovation?

As more and more artificial intelligence is introduced into the mainstream, its ability to make a difference has been called into question, but our research in the 2026 CGI Voice of Our Clients confirms the importance of both AI innovation and data to business and technology executives across industries. Of the 1,764 executives we interviewed, AI tops their innovation investment plans over the next 3 years, and today, 57% are investigating AI or doing proofs of concept. Executives also cite data management, governance, and quality as top improvement initiatives for their data strategies over the next three years.

Industries that benefit from AI: 

• Healthcare
• Financial
• Manufacturing
• Retail
• Media

How can you build an AI-friendly innovation culture?

Succeeding with these tools requires, first and foremost, a strong AI innovation culture. Because AI is a fast-changing and fast-growing technology, organizations must start reorganizing their workplace culture into the kind of place where AI tools are welcomed with open arms. If your work environment is not already a place where unconventional and creative ideas are encouraged and new technology for AI data analytics is embraced, you’ll want to restructure your workplace values.

3 factors needed for an innovative AI workplace culture:

1. Agility: When a new AI opportunity arises, how quickly can your organization respond? What type of approvals are required? How much red tape is involved? Can you get something started immediately, or are there hoops to jump through first? As the race to exploit GenAI intensifies across industries, agility is critical to responding first and fast.
2. Flexibility: A vital component of an AI innovative culture is flexibility—and talent is at the heart of it. In pursuing a GenAI opportunity, who is available to do the work? Do they have the right skills, or is upskilling required? How quickly can they be transitioned to a project? Building a flexible culture requires investing in people with a wide variety of skills who can be moved quickly from project to project as new demands and opportunities emerge.
3. Motivation: You also need an AI innovation culture that is inspiring. Are your people excited by artificial intelligence technologies and their potential? Do they want to learn new skills and take on new projects? Are there incentives for their efforts?

What is the ROI of cultural change?

What can you expect from creating an AI innovation culture with these attributes? In terms of AU experimentation and adoption, I believe market differentiation is one of the biggest benefits, along with optimizing operations.

With this kind of culture, you can use GenAI to launch new products and services—better and faster than your competition. Ultimately, you can stay at the forefront of AI-driven innovation, leading the charge versus waiting in the wings.

Investing in people and partnerships will help you build such a culture. The right talent with the right skills, along with trusted partners who can provide training and access to tools that drive AI innovation, are key success factors.

Case in point: How embracing AI helped a company generate immediate ROI

We’re working with a large communications company that has made this kind of cultural investment. As a result, it was able to embrace AI early on and pursue it on an ongoing basis. With the advent of GenAI, the company asked us to help it spin up a research and development (R&D) team to explore new use cases to continue its market innovation.

With a strong AI innovation culture in place, we were able to help it rapidly establish, expand and upskill the new GenAI R&D team, develop a proof-of-concept for a Gen AI-powered SQL query generator within eight weeks and then implement a fully baked solution within four months. What’s more, the solution generated an immediate return on investment.

What is AI data literacy?

At its core, data literacy involves knowing what data is available in each business area and how it can support data-driven decisions. For AI data literacy, all data fed into the programs must be accurate, well-governed and relevant to ensure that the application receives a solid foundation for further analysis. To succeed with AI data analysis, you need a fundamental understanding of what’s possible, of what data exists and how to use that data.

How to implement AI data literacy in the workplace

Ensuring an understanding of AI data literacy across the enterprise is a business issue, not a technology one, and it requires a leadership-driven and training-oriented approach. Without insights about how AI can make their jobs easier, people are less likely to adopt solutions and identify valuable use cases.

From the top down, leadership should communicate the need for the organization to become a data-driven enterprise and how AI empowers that. Then, from the bottom up, AI champions can be recruited to educate colleagues on the value of AI-driven innovation based on their own success in using the technology.

A major obstacle is that most people dislike change and often push back on new concepts and processes. However, it’s important for management to help employees overcome this challenge and, even with pushback, move forward with the fundamentals required for AI innovation.

Overcoming the AI and data literacy change curve

A trusted external partner can help organizations create effective change management, training and communications (CMTC) strategies to overcome the change curve. Such strategies often begin with executive communications to build awareness and excitement, as well as sharing stories of people whose day-to-day work has improved through AI and data. Training is provided across the organization for business leaders, technology teams, subject matter experts and end users alike on the concepts of AI and data and what use cases are benefiting their industry, customers/citizens, and individual roles.

Case in point: How prioritizing AI data literacy can lead to workforce benefits  

For an energy infrastructure company, for example, one challenge was getting field technicians who maintain thousands of gas compressors to fully benefit from AI-driven predictive models to minimize costly downtime. We worked with the company to provide training, identify change champions, create testimonials and implement a communications plan to instill a comfort level where technicians could combine their experience and intuition with insightful data from the models. The company saw AI model adoption increase—driving efficiency and increasing revenue—and is working to identify use cases in other business areas.

Invest in AI-driven innovation today

It’s exciting for me to see clients investing in AI-driven innovation cultures and data literacy as they pursue emerging technologies such as AI. The rewards are clear, and the opportunities endless. If you’d like to learn more about the insights I’ve shared or CGI’s AI and data work in general, let’s have a conversation. See my contact information.
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