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Eight takeaways on data and transformation for CIOs

The future lies in data, but true transformation addresses not just tech but also people and culture. Here are how CIOs are driving change.

In brief

  • AI projects should be carefully evaluated before they are deployed. When considered thoughtfully, they can have a huge impact.
  • CIOs therefore have a responsibility in setting priorities and building the foundations for the future, as well as developing governance.

No chief information officer (CIO) needs to be convinced of the power of data and emerging technologies like artificial intelligence (AI). But enabling these technologies typically requires transformation at a large scale and with the collective focus of the entire organization. How can these executives gain greater buy-in from the C-suite and everyday professionals — and be the change agents for greater growth and new business models?

Building on the momentum of its introductory meeting, the Tech Icons Council — a select group of data and technology leaders — gathered in January 2023 with other thought leaders leaders including two leading professors, Iavor Bojinov, Assistant Professor of Business Administration & Richard Hodgson Fellow at Harvard Business School and Tasadduq Shervani, professor of strategy at the Cox School of Business at SMU, to help facilitate discussions around the convergence of AI, data and tech talent in today’s economic environment.The current failure rate for AI projects is a staggering 60% to 80%,1 and Tech Icons members expressed various cultural hurdles, such as “gut feelings” trumping data and tools being left unused, reminding us that people must transform as much as tech.

How EY can help

Here are the key takeaways for CIOs to consider:

 

  1. Think in terms of impact and feasibility. Prioritize data and AI projects with these variables in mind. High impact and easy-to-implement projects can drive organizational momentum forward. High impact and low feasibility are often the most interesting, as these long-term plays force you to consider the skills and infrastructure that evolve into a foundation.

  2. Adopt a mindset of privacy by design. Governance is too often considered a bolt-on to AI efforts instead of embedded from the beginning. Regulatory considerations like the “right to be forgotten” in the European Union’s General Data Protection Regulation are important to build into AI efforts from the start.

  3. Avoid continually starting from zero. Development is slow and painful because data scientists typically must slog through ad hoc processes straying from their value-added skills, like building data pipelines. AI platforms are accelerators that democratize data science for more workers.

  4. Iterate rapidly with thorough experimentation. Begin with a hypothesis-driven mindset that advances as your tools evolve — into more sophisticated A/B testing that can be tweaked in real time, for example. Experimentation platforms help model out the ROI on big initiatives.

  5. Build trust. One Tech Icon participant asked her employees in a call center about the task they hated the most, then made the case for automating it, gaining confidence from those concerned about layoffs. Another added process descriptions to tools showing how data is turned into insights.

  6. Hold lines of business accountable for data integrity. Data needs to be controlled at the point of origination, so an updated operating model is integral. One participant helped to establish data domains and to assign owners in her organization; those who identified problems could file a claim, and she would know whom to contact. Accountability will prove to be vital as an organization goes deeper into AI and data-driven models.

  7. Understand your vendors. Unintended consequences from AI can be significant, like improper data tagging, can be significant and affect your entire value-chain. It’s critical to first understand the mechanisms by which an AI product or service operates before implementing it.

  8. Make sure your data initiatives have sponsorship at the top levels. Do you have an enterprise mandate? It’s a question of trust and credibility because data ownership can’t be centralized anymore.

Summary

When done well, data and AI projects transform businesses — but as of today, they are more likely to fail. Proactive CIOs craft a narrative for change, generate buy-in, consider the enablers and ultimately help the organization embolden its decision-making and unleash its potential.

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