AI Adoption — Top 3 Hurdles Most CDOs Face and How to Tackle Them

The increased awareness around AI has highlighted the importance of accurate, trustworthy, and high-quality data. Investing in trustworthy data can significantly influence an organization's success in the coming years by accelerating the achievement of meaningful outcomes.

Challenges in AI Adoption

In working with a diverse selection of customers, we have observed several challenges related to AI adoption. The ones that stand out are getting funding for AI projects and dealing with the overwhelming speed of technological advancements. In addition, mitigating potential risks and liabilities from rapid technology adoption can also be a challenge.

1. Securing funding

Despite the excitement surrounding AI, securing funding remains a hurdle. Organizations need to forecast costs and ensure a tangible return on investment (ROI). A practical approach is to align AI projects with existing business objectives. For instance, many companies aim to drive efficiency through automation. By linking AI initiatives to such pre-approved objectives, organizations can secure the necessary funding and demonstrate progress toward achieving their goals.

2. Keeping up with technological advancements

The fast pace of technological advancements around AI presents another challenge. Companies must constantly update their processes to keep up with innovations. While it may be tempting to rush and adopt the latest technology, a more calculated approach is often beneficial for larger enterprises. Regardless of the pace, one thing must remain a priority — to prepare high-quality data to fuel AI tools and strategies as soon as the organization is ready. 

3. AI governance and data quality

In the rapidly advancing field of AI, it is essential for CEOs, Chief Security Officers, and Chief Data Officers to manage risks and liabilities. AI governance, which ensures ethical, transparent, and accountable AI development, is becoming a top priority. It also encompasses other aspects like compliance with regulatory requirements, employee training on ethical AI, monitoring, security measures, and human-centric design.

A recent example of AI legislation is the new U.S. Algorithmic Accountability Act. This law requires companies to evaluate the impacts of AI on accuracy, fairness, bias, privacy, and security.

Stibo Systems’ platform assists customers by securely managing enterprise data and ensuring regulatory compliance. Although there isn't a single platform capable of managing every aspect, organizations can leverage a tool like master data management. This tool allows them to define what accurate data means for their organization, govern the training process to use high-quality data, and implement workflows to assess the accuracy of machine learning models' outputs. Initially, models are fine-tuned with human involvement. Data governance is an ongoing process, not a one-off task.

Shapers, makers, and takers

Different organizations approach AI adoption in unique ways. We categorize them as shapers (innovators), makers (builders), and takers (consumers). Innovators pioneer novel solutions, builders develop and refine these solutions and consumers adopt and utilize the established technologies. Each group has unique needs and risk profiles. As a learning organization, we acknowledge these different profiles. There is no right or wrong profile; the key is to engage with our customers as long-term partners, meeting them where they are.

A familiar concept in a new world: Collaboration

Successful data management requires cohesive efforts from various departments, including finance, operations, risk and compliance, and data management. CFOs must understand the long-term impact of data investments, COOs may need to adjust processes to adapt to market changes and CDOs should work closely with CIOs and CEOs to align data strategies with business objectives. By championing a purposeful collaboration, combined with the iterative approach organizations can adopt AI, and businesses can solidify their AI strategy based on the results. The key is to acknowledge the importance of the iteration and sign up for the ongoing collaboration.

Powering ahead

AI is a powerful force that can significantly enhance business operations, but its successful adoption requires careful planning, high-quality data, and effective governance. Organizations should approach AI not as an intimidating technology but as a means to amplify human intelligence and create a better future. By taking a thoughtful and strategic approach, companies can realize the potential of AI to drive innovation, efficiency, and growth.

About the Author:

As Stibo Systems’ Chief Product Officer, Neda Nia is responsible for the company’s Product Organization which consists of Cloud Operations, Product Management, UX, Innovation, and R&D.

With over 16 years of global experience spanning technology and commerce, Neda has deep knowledge of Stibo Systems’ market and has proven to establish product strategies that put the customer first. Before joining Stibo Systems, Nia was Managing Director at a global software and technology provider, where she defined a go-to-market strategy in North America, developed partnerships, participated in investment activities and took part in the company’s global strategy exercises.

Nia holds a degree in Business Administration - Information Technology from Seneca College and has completed a Digital Transformation program from MIT as well as a Business Analysis program from the University of Toronto. As an agile enthusiast, Nia has obtained Scrum Master, Scrum Product Owner and PMI Agile Practitioner certifications. She is a student at heart and continues to learn about emerging technologies to keep up with the fast-evolving market needs.