How do you move from experimenting with AI to creating measurable business value?
That question set the tone for a thought-provoking session held on 8 July 2026 at Borough of Manhattan Community College (BMCC), where members of the New York Small Business Association gathered to explore the future of AI adoption, workflow transformation, and business productivity.
Our team in New York had the opportunity to share practical strategies for implementing AI beyond proof-of-concepts. Rather than focusing on futuristic possibilities, the discussion centered on something far more valuable: how organizations can redesign the way work gets done using AI Agents, especially to automate finance processes.
AI Adoption Is Accelerating—But Most Businesses Are Still Finding Their Way
AI is no longer an emerging technology reserved for large enterprises. Small and medium-sized businesses are increasingly exploring its potential to improve efficiency, enhance customer experiences, and support faster decision-making. Yet despite this momentum, many organizations remain in the early stages of adoption.
Drawing from the McKinsey State of AI Report (November 2025), attendees learned that while AI adoption continues to accelerate globally, the majority of organizations remain in the experimentation phase.
Among the key insights shared were:
- Nearly two-thirds of organizations are still piloting or experimenting with AI.
- More than 60% of businesses are actively exploring AI Agents.
- Companies are prioritizing AI initiatives that improve efficiency, accelerate growth, and drive innovation.
- Organizations achieving the greatest returns are redesigning business workflows instead of simply inserting AI into existing processes.
Businesses recognize AI’s potential, but many are still determining where it creates the greatest value and how to scale successful initiatives across the organization. The session we had challenged attendees to rethink a common misconception—that simply adding AI to an existing process is enough to drive transformation. Instead, true business impact comes from redesigning workflows around AI.
Bringing Practical AI to New York’s Business Community
Hosted at Borough of Manhattan Community College (BMCC),the session brought together entrepreneurs, small business owners, professionals, and technology leaders who are navigating the opportunities and challenges of AI adoption. As an organization dedicated to supporting the growth and success of small businesses through education, networking, and access to industry expertise, New York Small Business Association (NYSBA) provides a valuable platform for business leaders to explore emerging technologies and practical strategies for sustainable growth. The event served as an opportunity for attendees to move beyond theoretical discussions and gain real-world insights into how AI can be implemented to improve productivity and business performance.
Instead of focusing on AI hype, discussions centered on questions that matter most to businesses:
- Where does AI create the highest business value?
- How should organizations start their AI journey?
- What separates successful AI implementations from unsuccessful ones?
- How can small and medium-sized businesses compete using AI?
For many attendees, the event provided a practical roadmap for transforming AI from an experimental technology into a production-ready business capability.
Rather than viewing AI as a standalone tool, the conversation emphasized AI as part of a broader digital transformation strategy—one that combines workflow automation, enterprise integrations, modern software architecture, and human expertise.
The Evolution of Automation Driven by AI, the Next Generation of Enterprise AI
To help attendees navigate today’s rapidly evolving AI landscape, through the presentation our team introduced the major categories of AI technologies and how each serves different business purposes. Understanding these distinctions helps organizations avoid deploying the wrong technology for the wrong business challenge.
- Automation AI, which performs repetitive rule-based tasks.
- Analytical AI, which identifies patterns, predicts outcomes, and supports decision-making.
- Generative AI, capable of creating new content, summarizing information, and interacting naturally with users.
- AI Agents, which combine reasoning, automation, enterprise data access, and workflow execution into intelligent digital workers.
A Roadmap from Manual Work to Intelligent Operations
The discussion also introduced a practical framework describing how organizations typically mature in their automation journey. Many businesses still rely heavily on manual processes, where employees gather information from multiple systems, reconcile data manually, and produce reports through repetitive effort. As digital transformation progresses, organizations often introduce rule-based automation to eliminate repetitive work. Although effective, these systems typically depend on predefined logic and require technical updates whenever business requirements change.
AI Agents represent the next stage of maturity. Unlike conventional automation, AI Agents can:
- Understand natural language.
- Access real-time enterprise information.
- Reason across multiple data sources.
- Generate summaries and recommendations.
- Execute business workflows with minimal human intervention.
Rather than replacing employees, AI Agents act as intelligent digital coworkers, allowing finance professionals to focus on higher-value decision-making while repetitive operational work is automated.
Helping Finance Teams Focus on Decisions Instead of Data Entry
Finance was one of the primary business functions highlighted during the session, illustrating where AI Agents can immediately deliver measurable value. Finance professionals often spend countless hours performing repetitive administrative work—matching transactions, reviewing reports, reconciling statements, and preparing forecasts. While these tasks are essential, they leave less time for strategic financial planning.
During the presentation, our New York team demonstrated how Gleematic AI Agents help automate these operational activities while keeping finance professionals firmly in control of decision-making. Several practical examples were shared. Together, these capabilities reduce manual effort while improving visibility into business performance:
Bank Reconciliation
AI Agents automatically collect banking information, match transactions against accounting records, identify discrepancies, and flag exceptions requiring review. This reduces manual reconciliation work while improving financial accuracy.
See how Gleematic automates bank reconciliation
Data Chat
Finance teams can also interact with enterprise data conversationally through AI-powered Data Chat, instead of searching through reports. AI Agents retrieve enterprise information and provide conversational answers regarding cash balances, transactions, and financial performance.
Cash Flow Forecasting
By analyzing historical financial patterns, AI Agents generate forecasts that help organizations anticipate liquidity risks and improve financial planning.
See how Gleematic automates forecasting in finance
Beyond AI Experimentation: Building Production-Ready AI Solutions
While AI technologies continue to evolve at a rapid pace, one of the biggest challenges organizations face is knowing where and how to begin. During the session, our New York team emphasized that successful AI initiatives are built on careful planning rather than technology alone. One of the presentation’s most practical discussions focused on the planning required before implementation begins.
Instead of asking “Can AI automate this task?”, organizations should first understand the business problem they are trying to solve and design a workflow that supports meaningful outcomes.
Our team also introduced a practical framework to help businesses evaluate AI opportunities before deployment:
#1. Objectives + Outputs
The first step is defining clear objectives and expected outputs. Organizations should identify what they want AI to achieve—whether accelerating payment processing, improving customer service, generating invoices, or reducing operational costs—and establish measurable outcomes that determine success.
#2. The Role of AI
Equally important is understanding AI’s role within the workflow. Not every business process requires artificial intelligence. In many cases, conventional automation may be sufficient. Where AI is introduced, organizations should clearly define what information, historical data, and business context the AI needs to make accurate decisions and generate reliable outputs.
#3. What are Tasks to Do?
The discussion also highlighted the importance of evaluating the tasks and operational requirements involved in implementation. Businesses should identify which activities will be automated, determine how AI will access enterprise systems securely, define decision points within the workflow, and address governance and cybersecurity considerations from the outset.
#4. Escalations and Exceptions
Finally, no AI system can anticipate every scenario. For this reason, organizations must establish exception handling and escalation processes that allow AI Agents to recognize unusual situations and seamlessly hand complex cases over to human experts. This human-in-the-loop approach ensures that automation remains reliable while preserving oversight for high-impact business decisions.
Empowering Business Growth with AI Agents
We extend our sincere appreciation to Borough of Manhattan Community College (BMCC) and the New York Small Business Association for organizing this meaningful event and creating a platform where business leaders, entrepreneurs, and technology innovators could exchange ideas on the future of AI.
The session at BMCC provided an excellent opportunity to exchange ideas with business leaders who are actively exploring AI adoption and digital transformation. It was encouraging to see strong interest from organizations seeking not just to experiment with AI, but to implement solutions that generate measurable business outcomes.
It was a privilege for our New York team to contribute to these discussions and share practical insights into how AI Agents can help businesses improve productivity, streamline finance operations, and transform the way work gets done. We believe that successful AI adoption is not measured by the number of AI tools an organization deploys, but by the tangible outcomes they achieve. Our mission is to help businesses move beyond experimentation by combining AI Agents, intelligent workflow automation, and enterprise integrations into solutions that create lasting business value.
Together, we can build a future where AI doesn’t simply automate work—it empowers people to focus on what matters most: creating, innovating, and growing their businesses.
Written by: Kezia Nadira
