Rochester firms lead generative AI adoption across industries

Listen to this article

From data and IT to marketing and finance, organizations are beginning to integrate generative AI into their workflows, while continuing to explore where it fits best. We spoke with several local leaders working at the intersection of AI strategy, implementation, and business transformation about what’s working, what’s not, and what’s next.

IT Insights

“We’re seeing organizations use generative AI across a surprisingly wide range of applications,” said Chris Sirianni, president and founder of IT Insights, a Rochester-based managed service provider.

Sirianni says some organizations are leveraging it for quick wins, like polishing emails, generating marketing content, and drafting internal communications, while others are getting more sophisticated and building AI agents to automate repetitive workflows and reduce manual workload in specific departments.

Most organizations, though, are somewhere in the middle, Sirianni observes, experimenting to find what works best for their business.

“What’s becoming clear is that the real value isn’t in using AI for everything, it’s in being smart about where you apply it,” he said. “Using AI to help craft the right response to a high-value sales email? That makes a difference. Using it to write every routine message? That’s more novelty than impact.”

Challenges remain, particularly around data quality, skills and trust.

“Data quality is a consistent issue, AI is only as good as what you feed it, and garbage in still means garbage out,” Sirianni said. “Related to that is the skills gap: most people haven’t yet developed the ability to prompt AI effectively. Better prompts lead to better outputs, and that’s a learnable skill.”

He also points to the need for oversight as technology continues to evolve.

“Hallucinations, where the AI produces confident but incorrect information, remain a concern,” he said. “That means organizations need to hold employees accountable for reviewing AI-generated content before acting on it.”

At the same time, how leaders frame AI internally matters too.

“AI isn’t a headcount reduction tool, it’s a capacity expansion tool,” Sirianni said. “The conversation with employees shouldn’t be ‘AI might replace you.’ It should be ‘AI is going to make you more valuable.’”

FLX AI

As organizations move beyond initial experimentation with generative AI, many are beginning to apply the technology more deliberately across their operations, said Karla George, CEO of FLX AI, a Rochester-based firm that creates custom artificial intelligence solutions for organizations across multiple sectors.

“At this point, most organizations have adopted AI assistants like ChatGPT or Microsoft Copilot to boost individual productivity, primarily for drafting, summarization, and routine tasks,” George said.

That early phase is quickly evolving as companies look beyond basic use cases and begin integrating AI more deeply into their workflows.

“We’re now seeing a shift toward agentic AI, where systems move beyond task support to actually executing workflows in areas like sales, operations, and IT,” she said.

Rather than being tied to specific industries, George said technology is proving most effective in areas defined by the nature of the work itself.

“Generative AI delivers the most value in areas with high volumes of repetitive, structured, or language-heavy tasks,” she said. “The key isn’t the industry, but the nature of the work: the more standardized and repeatable the task, the faster generative AI can deliver measurable gains.”

At the same time, organizations are still working through how to implement AI effectively.

“The biggest challenges we see are around data quality and systems maturity, as well as cultural resistance to changing how work gets done,” George said. “Equally important is training and adoption—without it, even the best AI deployments fail to deliver meaningful impact.”

Cause + Effect Strategy

As organizations look to move beyond basic use cases, many are beginning to explore how generative AI can be embedded more directly into existing workflows, said John Loury, president of Rochester-based data and analytics consulting firm Cause + Effect Strategy.

“I would say the number one most popular use case is personal productivity,” Loury said, pointing to tasks like drafting emails, summarizing meetings and creating first drafts of presentations.

From there, organizations are starting to expand into what he describes as knowledge work acceleration, using AI to tap into internal data and institutional knowledge more efficiently.

“To be able to access that quickly and to synthesize thoughts from that is a really valuable opportunity,” he said.

A third, more advanced stage involves integrating AI into defined processes, allowing tasks to move seamlessly from one step to the next.

“Not many people are at this stage yet,” he said. “But that’s where a tremendous amount of value is.”

As adoption continues, challenges remain around trust, data and execution.

“AI can definitely be wrong, but that doesn’t mean it’s unusable,” Loury said. “Having the right human-in-the-loop checkpoints is critical.”

Bank of America

In the financial services sector, generative AI is being applied in practical ways that support both employees and clients, while keeping decision-making with people, said Hari Gopalkrishnan, chief technology and information officer at Bank of America.

“Across banking and financial services, generative AI is being applied in focused, practical ways that support clients and employees while keeping people firmly responsible for decisions and outcomes,” he said.

Much of that work centers on improving access to information and reducing friction in everyday tasks.

“The most common uses today are natural-language interfaces, search and summarization of complex information, and personalized insights,” he said.

At Bank of America, those applications are already embedded in both customer-facing and internal tools.

“Internally, generative AI helps employees navigate institutional knowledge more efficiently, reducing manual effort and allowing more time to focus on clients and higher-value problem-solving,” he said.

The strongest impact, he noted, comes from using AI to complement human expertise.

“The strongest returns come from augmenting expertise and reducing toil, not automating decisions,” Gopalkrishnan said.

At the same time, deploying AI in a highly regulated environment like banking requires a disciplined approach.

“Key considerations include safeguarding data privacy and security, ensuring accuracy and reliability, and meeting regulatory expectations for transparency and accountability,” Gopalkrishnan said.

Dixon Schwabl + Company

From a marketing perspective, generative AI is quickly reshaping how organizations create and deliver content, said Malorie Benjamin, chief transformation officer at Dixon Schwabl + Company.

“Overall, organizations really range in their use cases and productive areas of use,” Benjamin said. “In marketing specifically, we’re seeing most of the use cases around personalization of marketing assets and delivery of marketing.”

That shift is significant, she noted, because personalization has long been tied to stronger performance but was often difficult to execute at scale.

“We know that personalization in marketing produces the best outcomes,” Benjamin said. “Historically, this wasn’t truly a cost-effective endeavor. Now, with AI, we’re able to generate asset versions at scale.”

Beyond content creation, organizations are also investing in efficiency, particularly through automation and workflow integration.

“We also see a significant investment in efficiency implementations like workflow connectors and automations,” she said.

At the same time, Benjamin cautioned that AI is not a fix for deeper operational issues.

“Adding AI into an already challenged or stressed system won’t repair it,” she said. “AI is really the most useful when it’s enhancing what is going well or building upon a solid structure.”

Still, she sees significant opportunities for organizations that approach the technology meaningfully and thoughtfully.

“For DS+CO, we believe in bringing clarity to our clients and their business problems,” Benjamin said. “AI is a business challenge we’re all facing together.”

Caurie Putnam is a Rochester-area freelance writer.