People, Process, and Tech: Dataiku’s Claire Gubian on AI and the Enterprise 

When it comes to AI in the enterprise, the question is no longer if organizations will adopt the technology — it’s when. And increasingly, the answer to that question is now. According to a survey by the Conference Board, more than half of US employees “are already using generative AI tools, at least occasionally, to accomplish work-related tasks,” with one in ten employees saying they use AI on a daily basis. 

Despite the growing adoption of AI tools, many enterprises struggle with effective implementation and ROI. The issue isn't purely a technological one—it’s a human one as well. Misalignment between technical teams and the rest of the company, insufficient AI and data literacy among employees, and fears about job displacement can all hinder the successful deployment of AI in an organization.

Recently, Claire Gubian, Global VP of Business Transformation at Dataiku, joined us on the Data Humanized podcast to talk about how enterprise organizations are navigating this paradigm-shifting transformation, and why effective AI implementation comes down to three key factors: people, process, and tech

Below, we share key takeaways from the episode, including the importance of data literacy, aligning data initiatives with business goals, the impact of AI on job roles, and the need for measurements to track progress. 


Bridging the Gap: The Growing Need for Data Literacy in Enterprise AI

As data continues to evolve as an invaluable tool for uncovering strategic business insights, companies are beginning to understand the need to educate employees at every level to handle their immense responsibilities.

The mass embrace of AI and its subsequent implementation has skyrocketed the demand for data-literate employees who can understand, analyze, and communicate data findings. New AI tools are collecting overwhelming amounts of data, which, in turn, requires new employee skills to transform the data insights to achieve business goals.

Gubian has seen firsthand that "very successful companies invest mostly [in] change management and upskilling of employees" when preparing a workforce for a digital transformation — "Not just giving them a new tool, or a new technology, but really investing on how to change the way decisions are made."

Gubian also points out that aligning "data science and the strategic parties in the company" makes the transition to enterprise AI readiness more accessible. True alignment comes from brainstorming and developing use cases where data literacy can help drive successful digital transformation.

Achieving Alignment: Bringing Technical Teams and the Organization Together

As businesses embark on their digital transformation journey, it's vital to avoid siloing AI and other technical projects as solitary operations. Instead, the projects should closely align with broader business objectives. This alignment helps guarantee that the insights derived from data science initiatives have practical relevance and can drive meaningful change within the organization.

Bringing multiple domain experts together under the same roof is a prerequisite to achieving alignment. According to Gubian, "The best projects are when you have the domain experts put together in one platform." It allows for cross-functional collaboration and breaks down silos between teams.

From there, Gubian recommends shifting focus to processes. Her team "really believes in the importance of spending time reviewing use cases, qualifying them, and prioritizing them against what value they are going to bring."

Considering the potential risks is equally important. Every AI project comes with its own set of risks, including data privacy concerns, technical feasibility issues, and potential impacts on customer trust. By proactively identifying these risks and implementing effective mitigation strategies, companies can ensure that their data science initiatives won't inadvertently harm their business or reputation.

Addressing Job Impact: Empowering Employees Through AI Adoption

The prevailing thought of most workers around AI adoption is one of fear. According to a Goldman Sachs report, AI has the potential to disrupt over 300 million full-time jobs globally. To help an enterprise adopt AI, digital transformation leaders have a responsibility to their stakeholders to inform them of how AI enhances their personal capabilities, streamlines workflows, and supports higher-value tasks.

"AI can be applied to any business decision to make it better or any business process to make it faster and improve it. It can help make people extraordinary by giving them more capabilities," Gubian explains. One of Gubian's clients cut down the time spent on P&L forecasting from 2,000 hours per month to 70 hours per month using AI technology.

AI is capable of superhuman analytical feats, and stakeholders can now harness this power for the good of the company. Instead of threatening job roles, AI should empower employees to become more valuable to the company than they previously thought possible.

Transformation leaders are the cornerstone of AI adoption. By informing employees of the positive impact AI has on job roles, these leaders can help dispel fears and concerns while driving a successful digital transformation that leverages data literacy as a powerful tool for growth. As Gubian sums up, "We're talking about spending less time on manual tasks, going faster, and being able to make better decisions."

Measuring Success: Key Factors in Tracking Digital Transformation

Without a clear sense of direction and a method to gauge success, organizations will struggle to understand if transformation efforts are yielding the desired results. Three critical factors help guide and define the progress of digital transformations: KPIs related to tool usage, employee engagement, and value generation.

KPIs serve as a quantifiable measure of progress toward strategic objectives. They provide a way for businesses to quantify their goals. Employee engagement, on the other hand, is a qualitative measure. It illustrates how invested employees are in the transformation process. Value generation measures the output against the amount of effort teams contribute. It also ensures progress isn't focused on one tool or process but leads to the entire organization becoming AI-ready.

According to Gubian, two primary methods are invaluable for discovering metrics that inform employee engagement and value generation. "Asking customers and looking at some very successful programs" will uncover strategic insights that organizations can use to shape their KPIs, employee engagement, and value-generation strategies.

Leveraging People, Process, and Tech for Successful Enterprise AI Readiness

Competing in today's digital world requires embracing data analytics and AI tools. However, many organizations are ill-prepared to handle the incoming tsunami of information flooding their platforms. Establishing clear processes and educating the workforce before a digital transformation occurs are how to stay ahead of competitors during this tumultuous period.

Here are a few proven steps to immediately drive successful digital transformation within an organization:

  • Prioritize use cases: Define specific use cases to solve with digital transformation that are relevant to the overall business objectives. Alignment between data teams and all other stakeholders is one of Claire Gubian's top recommendations for enterprise AI readiness.
  • Set clear objectives and KPIs: Gubian highlights the importance of measurement and tracking, which involve setting clear business objectives and measurable goals. Defining what success looks like during the transformation efforts will streamline the organization's progress.
  • Assemble cross-functional teams: Get all the key domain experts together on the same team, including data scientists, business strategists, and IT professionals. This will eliminate silos and foster collaboration among all stakeholders.
  • Implement targeted training: The rate of progress in the tech sector creates a short window of relevance for training programs. Continually invest in new training programs to keep teams updated on the latest skills and technology.
  • Launch, monitor, and adjust: Start with small pilot programs that individual teams can test out without affecting the entire company. Monitor progress and make adjustments where necessary, using data to inform any decisions.

Are you an HR leader who is thinking through the impact that AI will have on your organization and how to effectively manage the transformation? Correlation One's AI Workforce Development Council is an industry group that provides HR leaders a forum where they can come together to discuss the impact AI is having on their business and workforce. If you're interested in learning more about the AI Council and how you can be involved, reach out today