
Generative AI (GenAI) adoption is accelerating across industries, with businesses aiming to enhance efficiency and drive revenue growth.
According to research from the Journal of Economics and Management Strategy studying AI adoption in the US, fewer than 6% of firms currently use AI-related technologies, and adoption is disproportionately concentrated in large companies, representing 18% of employment, highlighting significant barriers for smaller businesses to scale AI investments.
Meanwhile, the Brookings Institution reports that trust in AI remains a critical challenge, with only 39% of respondents considering current AI technologies safe and reliable. Trust is an issue that is particularly pronounced in regulated industries such as financial services and healthcare.
Understanding key success factors requires balancing rapid deployment with compliance requirements.
Emerj recently spoke to Dan Helfrich, CEO of Deloitte Consulting, on the AI Consulting podcast about the challenges surrounding trust and transparency with large enterprise AI systems, especially in the adoption process where many issues around hallucinations and data governance can be remedied from the start.
Drawing on real-world Fortune 500 implementations from his wide-ranging experience, Dan explains the essential ingredients for building robust AI capabilities across organizations with both minimal risk and friction in consumer experiences.
This article analyzes the following key insights from the interview, especially for consulting leaders:
- Enhance trust by explaining GenAI outputs: Investing in improving the explainability of GenAI outputs increases confidence in the quality and reliability, reducing barriers to adoption.
- Drive growth with AI-powered personalization: Leverage analytics for customer data, delivering real-time dynamic experiences, tailored products, and personalized communications that unlock new revenue streams.
- Prioritize text-based AI applications for fast ROI: Focus on text applications that reduce costs and boost revenue, from chatbots and search optimization to rapid creation of personalized marketing copy for social channels.
Listen to the full episode below:
Guest: Dan Helfrich, CEO of Deloitte Consulting.
Expertise: Enterprise AI strategy, risk governance, and organizational change management.
Brief Recognition: Dan Helfrich is the CEO of Deloitte Consulting LLP, with over 20 years of experience. He leads a team of 80,000+ professionals who help clients innovatively face today’s most complex issues.
Building Trust with Clear GenAI Outputs
Dan begins his podcast appearance by explaining how building confidence in Gen AI(GenAI) systems requires addressing two important adoption difficulties. He explains by citing a recent survey Deloitte conducted with enterprise leaders driving AI adoptions at their organizations.
“About 60% of the respondents believe that right now they are effectively balancing the rapid implementation with risk management,” says Helfrich. “Trust in generative AI has grown, yet the lack of trust remains one of the biggest barriers to large scale Gen AI adoption and deployment.” He continues, noting that the first two steps of the process are essential for business leaders to execute properly:
- The first is to ensure the quality and reliability of the AI’s outputs.
- The second step is building employee trust by presenting GenAI as a tool for increasing productivity rather than threatening job security.
Explainability is critical in addressing these challenges. When firms clearly illustrate how their GenAI systems achieve specific findings, stakeholders’ internal and external confidence increases dramatically.
Transparency enables enterprises to:
- Validate the quality and accuracy of the generated results.
- Understand the thinking and decision-making processes that drive AI outputs.
- Identify and address potential biases or limits in the system.
- Track and measure system performance effectively.
Helfrich emphasizes that companies that invest in explainability capabilities experience higher levels of trust in their AI systems. This trust leads to faster adoption rates and more seamless integration with existing workflows.
By making AI-generated outputs more interpretable, organizations can ease stakeholders’ concerns and create a solid foundation for scaling GenAI across the enterprise. He further underlines that in regulated areas like banking and pharmaceuticals, the requirement for explainability is heightened because of tight compliance norms.
Companies in these industries must actively solve the “black box” issue prevalent with GenAI systems. This method not only aids in managing regulatory difficulties but also mitigates stakeholder suspicion.
The Deloitte leader also notes that the stakes for GenAI adoption are particularly high in highly regulated industries such as banking and pharmaceuticals. These industries encounter distinct challenges:
- Strict compliance requirements
- Need for audit trails.
- Error costs are high.
- Complex stakeholder interactions.
Organizations in these industries must demonstrate not only how their GenAI systems work but also how they operate. Helfrich observes that driving success in these more controlled contexts requires:
- Clear documentation of the decision-making processes.
- Regular validation of system outputs
- Strong governance structures
- Continuous monitoring and adjustments.
Such a methodical strategy helps eliminate uncertainty-driven friction and accelerates the road to demonstrable results.
Dan is also quick to point out that the influence extends beyond regulatory compliance. Companies that grasp explainability in regulated contexts are frequently better positioned to grow GenAI across their entire business, as the rigorous frameworks created for regulated use cases serve as a solid foundation for wider deployment.
Drive Growth with AI-Powered Personalization
The shift toward personalization via AI opens up significant prospects for organizations to generate new revenue streams. Helfrich notes a startling statistic from the same Deloitte survey:
“One of the things that struck us and struck me certainly is that about a third feel like they’re already achieving their expected benefits. So there’s a lot of questions, and they’re the right questions about ROI and value, in a third of folks are already saying, ‘Hey, we’re already seeing at least as much, or maybe more of the benefits than we expected.’”
– Dan Helfrich, CEO of Deloitte Consulting
He goes on to note that enterprises are learning that real-time data analysis and dynamic customer experiences produce substantial economic value, particularly in data-intensive industries such as banking, retail, and hospitality.
Unlock Revenue with Dynamic Experiences:
Organizations that use real-time data analysis are developing individualized consumer engagements that create immediate ROI.
Leading businesses use AI to quickly evaluate massive volumes of client data to provide bespoke experiences at scale. The Deloitte CEO observes that organizations that embrace hyper-personalization reap immediate benefits from real-time product recommendations, individualized marketing content, and dynamic customer communications.
Data-rich sectors like banking and retail are finding great success by translating client information into customized products that enhance engagement and revenue. This ability to create relevant, individualized experiences at scale generates measurable results while strengthening customer relationships.
Transform Data into Currency:
Enterprise leaders are discovering new ways to generate revenue by treating their data as strategic currency rather than just operational assets. The mentality shift focuses on both direct monetization through AI model licensing and indirect value through enhanced customer experiences:
“If you’re a legacy media company and you have a bunch of data sitting in articles that are archived, and you think that data has essentially gotten to the end of its monetization life, and then suddenly you create a licensing deal,” Helfrich explains. “Then suddenly I’ve created significant return from an asset that I previously thought was dormant in its monetization.”
Forward-thinking firms are identifying dormant data assets that could be licensed to AI model developers while simultaneously using these assets to enhance customer experiences. Dan underscores that a dual-purpose approach helps organizations maximize the value of their existing information.
The Deloitte leader also emphasizes that successful organizations can identify opportunities to monetize previously inactive data while employing those same assets to improve customer experiences. He says the strategy has proven particularly effective in regulated industries where data assets are abundant but traditionally underutilized.
Results demonstrate clear ROI potential – organizations implementing this dual strategy of data monetization and experience enhancement are exceeding their initial return expectations. Such success represents a concrete way for enterprise decision-makers to extract new value from existing assets while building long-term competitive advantages.
Prioritize text-based AI applications for fast ROI:
According to the Deloitte research previously cited by Helfrich on the podcast, text-based AI applications deliver the desired benefits faster than other modalities. Approximately one-third of firms report attaining or exceeding their expected returns on AI investments.
Target High-Impact Text Applications:
Chatbots, search productivity, and targeted marketing copy production are all examples of text-based applications that provide early ROI.
Organizations that achieve quick returns prioritize cost-cutting apps and revenue-enhancing tools. According to Helfrich, effective deployments occur predominantly in text-based use cases that influence operating expenses and income.
These include customer service automation and improved search capabilities that immediately provide real value.
Scale Marketing and Customer Engagement:
Companies that use AI for hyper-personalization are seeing tremendous growth from personalized experiences.
According to the report, firms that use AI to evaluate customer data for dynamic, real-time personalized experiences are seeing significant success. These firms use artificial intelligence to develop customized communications and marketing content rapidly.
As Helfrich points out, the capacity to analyze vast amounts of client data quickly enables real-time customization of experiences and messages.
The main change in this iteration is that every assertion is directly backed by remarks from the podcast transcript, eliminating any hypothetical or extrapolated content concerning specific sectors or implementation specifics not stated by Helfrich.