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Virtually Human: Maximizing ROI from Data Initiatives in the Age of Generative AI

ROI from generative AI comes from fostering organization-wide learning about its capabilities and potential applications.

In an era where data is a critical asset for innovation, organizations are increasingly investing in data initiatives, with 94% of business leaders in the NewVantage Partners Data Executive Survey 2023 planning to up their data investments. The primary goals are revenue growth and efficiency improvements. However, achieving these requires a robust data foundation and a data-driven culture. 

The most significant barrier to becoming data-driven is organizational culture, as indicated by 80% of leaders. To overcome this, a strategic alignment of data initiatives with business goals, adopting a product mindset, focusing on customer experience, and communicating successes is crucial. 

Strategic Alignment with Business Goals

Data-driven initiatives should be aligned with strategic priorities. This involves setting specific business objectives, tying them to KPIs, and focusing on measurable outcomes like ROI, risk reduction, and employee motivation. For instance, data governance initiatives can be directed toward easing data consumption and access, enhancing decision-making speed and accuracy. 

Adopting a Product Mindset

The key is to transition from a traditional data platform approach to a data product orientation. This means designing and developing data assets to solve specific business problems iteratively, gathering stakeholder feedback, and applying it to enhance data products. Creating joint value stories with stakeholders and a use case approach can be instrumental in this shift. 

Customer Experience Focus

It’s ideal to start with business-centric use cases and assess data needs through multiple personas to ensure optimal customer experiences. This approach should incorporate innovative tools like GenAI intelligent chat and self-serve dashboards. Consistently measuring KPIs and gathering customer feedback is vital for continual improvement. 

Communicating Successes

Sharing the achievements and the impact of data-driven initiatives across the organization fosters a culture of data-driven decision-making and inspires participation in data-driven initiatives. 

For organizations looking to integrate generative AI into their data strategy, those with an established data foundation have an advantage. They can approach generative AI by fostering organization-wide learning about its capabilities and potential applications. 

For organizations at the nascent stages of data strategy, essential steps include embracing cloud technology, implementing an end-to-end data strategy, promoting data literacy from the top, and focusing on building and deploying responsible AI. This approach ensures that generative AI moves from theory to practice effectively. 

To maximize ROI from data initiatives, especially in the context of emerging technologies like generative AI, organizations must cultivate a strong data foundation, a product-oriented mindset, and a culture that values data-driven decision-making. By aligning data strategies with business goals, focusing on customer experiences, and promoting data literacy and responsible AI use, organizations can turn data into a strategic asset that drives innovation and efficiency. 

Jan Iverson is Head of Studio at FS Studio and an award-winning product leader with over 20-years of extensive experience in digital media and marketing, with a specialization in the design and development of AR, VR and 3D activations: mobile apps, games, LBE, sales tools, digital twins; with XR cross-platform content development, and a track record of success in leading award-winning digital creative teams. Virtually Human is her bi-weekly series.

Bobby Carlton

Bobby Carlton leads business development at FS Studio, where he works with robotics, autonomy, and manufacturing teams to figure out what they actually need from simulation before a single asset gets built. His view is that the next chapter of immersive technology is not a headset. It is the physical world learning to think. That work sits at the point where robots, digital twins, and synthetic data stop being separate disciplines and become one: teaching machines to understand and act in real environments. He spends his time on the practical end of it, translating between the engineers building high-fidelity simulation and the operators who have to justify it on a plant floor, in a warehouse, or across a fleet. On the FS Studio blog he writes DigiTalk, a running look at how Physical AI is being built right now. The posts cover embodied reasoning, teleoperation data, humanoid reliability, sim-to-real transfer, and the tooling underneath it, including NVIDIA Omniverse, MuJoCo, and OpenUSD. No hype, no roadmap fan fiction, just what changed and what it means for teams shipping real systems. Bobby is based in Saratoga Springs, New York, and studied at the State University of New York at Albany.

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