Interview
GenAI is poised to transform business operations and value creation, rivaling previous disruptive innovations like cloud computing and mobile technology.
According to McKinsey (via the book “Rewired”), GenAI holds the potential to accelerate and redefine processes across industries by automating creativity, enhancing decision making and optimizing workflows. While AI lacks genuine understanding, it excels in processing vast amounts of data, drawing insights and offering suggestions by applying established advanced mathematical techniques, such as vector analysis and cosine similarity.
To explore the state of GenAI, I spoke recently with Brian Lett, research director at Dresner Advisory Services. Lett emphasized that GenAI is still in its initial stages, with businesses experimenting to uncover practical use cases and measurable ROI. He also highlighted a growing interest in GenAI applications, particularly for content generation, customer engagement and decision support.
However, challenges such as data quality, ethical considerations and integration into existing workflows remain significant barriers to go-live implementations that improve operations and processes. As organizations gain clarity on how to balance the promise of GenAI with its limitations, they will determine how to reshape the enterprise.
Table of Contents
- Where Are Companies in Their GenAI Rollout?
- What Data Fuels GenAI — And What’s Holding It Back?
- The AI Advancements Changing the Game
- GenAI’s Road Ahead: Final Takeaways
Where Are Companies in Their GenAI Rollout?
Where are most organizations at in terms of deploying GenAI?
Lett explained, “Our data reveals a significant shift in how organizations prioritize generative AI. In 2023, much of the implementation activity and funding originated not from top-down C-level mandates but from decentralized sources, such as business units or specific functions taking independent initiative. This started to change in 2024, and will continue in 2025.”
Lett added, “Our latest data show that retail and wholesale most often report production use of GenAI, followed next by technology, business services and consumer services. Individuals in the strategic planning function most frequently indicate production use, with 67 percent reporting active deployments.”
What are the most significant GenAI priorities?
“In 2025,” Lett observed, “organizations most often cite boosting productivity and efficiency as the most critical driver for adopting generative AI, far surpassing other reasons. The next most cited drivers considered critical, at roughly the same levels, are improved customer experience and personalization, improved search quality and improved decision making.”
Lett continued, “Priorities vary among industries: government most often perceives increased productivity and efficiency and improved search quality as critical drivers. Those in manufacturing most often see increased creativity and improved customer service/personalization as critical. Healthcare and finance often focus on improved decision making, while technology highly values the potential of GenAI to support market and business expansion.”
Which functions are the early adopters of GenAI?
Lett stated that IT and marketing are the most likely to adopt generative AI first, followed closely by operations, production, sales and C-level executives, who also show strong interest.
Surprisingly, finance and human resources show the least interest, with their enthusiasm exceeding a majority only when combining primary, secondary and tertiary responses. It will be interesting to see if finance and human resources shift their stance with the rise of agentic AI, which has significant applications, particularly in human resources.
Related Article: 5 Common Barriers to Generative AI Adoption — And How to Overcome Them
What Data Fuels GenAI — And What’s Holding It Back?
What are commonly used GenAI data sources?
“Generative AI data most frequently comes in large part from customer/CRM data,” said Lett. “It is the only data source that most organizations consider critical or particularly important. “Organizations second-most frequently expect generative AI to leverage finance and accounting data. The call center and supply chain data follow next; their data have identical response levels.
“Respondents least often expect generative AI to leverage workforce data. This low level of response reflects a combination of high perceptions about potential privacy concerns (such as with personal and personnel-related data), a lack of high confidence in general HR-related content and low perceived value from or need for use cases that would leverage workforce data.”
What are the most highlighted GenAI Concerns?
When considering the adoption of generative AI, Lett said, “Our research shows the issue most frequently considered critical is data privacy. Legal and regulatory compliance also carries a prominent level of high-level concern, followed closely in importance by ethical /bias concerns.
“However, organizations least often cite the lack of quantitative measure(s) of the business value derived from GenAI and poorly defined use cases as being critical issues. The low levels of these data indicate that many organizations in 2025 will continue to emphasize speed and perceived value of GenAI over tighter business alignment and justification. Although it won’t be as bad as being ‘ready, fire, aim’ approaches, GenAI implementations and efforts clearly will continue to have a lot of leeway before they have to start financially and operationally justifying their costs.”
The AI Advancements Changing the Game
What are organizations using: embedded GenAI or built?
Lett explained, “Vendors are rapidly incorporating generative AI capabilities into their products, making it more of a standard requirement. Currently, many vendors offer generative AI product capabilities, and most of the rest soon intend to incorporate generative AI. MIT-CISR has recently contrasted between tools and solutions and what they deliver for organizations.
“The speed at which generative AI arrived resulted in many leveraging and integrating with existing LLMs (large language models), such as OpenAI ChatGPT. A majority report using both approaches.
“Development of new LLMs is company- and/or industry-specific implementations, rather than new attempts at more general-purpose models like ChatGPT. Most vendors will continue to emphasize adding GenAI features and capabilities to existing offerings, both to remain on par with competitors as well as accommodate the needs of an increasing number of organizations that will look to these application vendors to provide ‘good enough’ GenAI capabilities that enable these customers to derive value without committing significantly to investing in and nurturing build-focused GenAI competences.”
What’s with the rise of agentic AI?
There is significant interest in agentic AI,” said Lett, highlighting a trend that is rapidly gaining momentum in the world of generative AI.
From Lett’s perspective, “This evolution represents a significant leap, with several prominent figures in the industry heralding agentic AI as the real breakthrough in GenAI. Unlike traditional generative models that passively respond to prompts, agentic AI systems are designed to operate with autonomy, setting goals, making decisions and learning iteratively — qualities that suggest profound implications for both innovation and ethics.”
Related Article: The AI Agent Explosion: Unexpected Challenges Just Over the Horizon
GenAI’s Road Ahead: Final Takeaways
Generative AI is on the brink of revolutionizing industries, offering the potential to automate creativity, enhance decision-making and streamline operations. While early adoption has been decentralized — driven by business units rather than executive mandates — organizations are already seeing its impact in consumer services, technology and healthcare.
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Priorities vary across sectors. However, challenges such as data privacy, ethical concerns and regulatory compliance remain significant hurdles. As the technology evolves, the emergence of agentic AI — capable of autonomous decision-making — promises to reshape innovation, making it essential for businesses to balance these advancements with strategic foresight.
