Generative AI’s Powerful Potential to Boost Productivity and Build Trust in Data

Ulrike Bahr-Gedalia: What is your concept of trust in data and its significance for productivity, especially in the context of generative AI?

Mike Branch: Gen AI relies heavily on trust in data. Trust in data is like the confidence placed in crossing a bridge. Solid data foundations instill confidence. Gen AI depends on this trust in its data for its acceptance and effectiveness. Maintaining this trust requires clear transparency, privacy safeguards, reliability, and quality data. These pillars of trust will help deliver on the technology’s promise to boost productivity.

UBG: In what ways do you see Gen AI impacting trust in data, and how does it influence productivity outcomes?

MB: Gen AI can speed up time-to-insight by allowing users to interact with complex business datasets using natural language to obtain answers. To build trust in its use, it’s essential to ensure responsible deployment of AI.

Trusted AI boosts productivity by enabling quick, insightful decisions. For example, a fleet manager can use Gen AI to identify the least productive regions in seconds rather than hours or days. By democratizing data and providing actionable insights, Gen AI enhances decision-making, customer value, and competitive strategy.

UBG: With the increasing concern over data privacy and ownership, how are you navigating the complexity to ensure trust in data, while respecting privacy rights?

MB: Geotab adheres to diverse data privacy laws in its processing of data from over four million vehicles worldwide. Data standardization ensures customer access while respecting privacy, aligning with the Connected Vehicle Systems Alliance. We have a cross-functional team involving compliance, legal, data privacy, and engineering to develop a global posture that coincides with data sovereignty requirements.

Geotab also innovates in data privacy through anonymization techniques, red teaming, and motivated intruder attacks, preserving customer privacy whilst delivering AI-driven insights to reduce collisions, downtime, and emissions.

UBG: Are there any jurisdictions you can point to that lead by example in terms of trust in Gen AI and data practices, while respecting privacy rights?

MB: As Gen AI adoption grows, jurisdictions are developing frameworks and regulations that foster trust and ensure privacy rights are respected — the EU’s AI Act and Singapore’s AI framework are leading examples.

In Canada, several new acts and charters promote responsible AI use in commercialized offerings. Each of these regions and countries show how a balanced approach between innovation in Gen AI and robust data protection can encourage trust in Gen AI technologies, alongside respect for individual privacy rights. At Geotab, we view Gen AI as a driver of data democratization and insight generation, employing a nuanced deployment approach to ensure optimal value extraction and system safety for users.