AI is clearly top of mind for anyone operating in business today. From the CEO down to the entry level, this breakthrough technology is reshaping how people operate. For some, it’s been a game changer. For others, an anomaly with an elusive (or even false) promise.
Our inaugural AI Impact Forum is an attempt to capture the state of the practical art — to understand how middle-market businesses in Northeast Ohio are using AI in their businesses and to what effect. We also want to understand the challenges and dead ends that not only business leaders have encountered, but to share insight and expertise from a variety of professional disciplines who are seeing the impact of AI from a uniquely positioned lens.
Ahead of that Forum, we asked a series of questions to many of those who will be presenting at the event on August 27 to capture their perspective on this burgeoning technology. Though them, we hope to get a sense of where companies are finding success and stumbling through challenges, how online marketing and hiring will be impacted, and more. Here’s what the experts we spoke with have to say.
Readiness and challenges
Middle-market companies best positioned to succeed with major AI initiatives possess clear project goals, aligned IT and data infrastructure, and structured talent training plans to ease worker transitions, according to Mike Lalich, senior director of technology, business development at Team NEO.
“The implementation of AI, like many new technologies, often relies as much if not more on organizational change and people management than the development of the technology solution itself,” Lalich says.
Payal Thakur, managing director of data analytics, and AI (CDAIO) at JobsOhio, says that structural foundation — not tool acquisition — defines AI readiness. She outlines three prerequisites for any AI launch:
- Clean, accessible and well-governed data.
- A defined business problem paired with a measurable outcome.
- Workforce readiness, meaning personnel understand the capabilities and limitations of the tool and apply critical oversight.
“The lesson for middle-market leaders: have a clearly defined objective for every AI initiative and don’t treat workforce literacy as a nice-to-have you tack on after the rollout,” Thakur says. “Both are prerequisites.”
David Croft, partner and chair of the business and corporate group; section lead of the cybersecurity and blockchain and cryptocurrency groups at Meyers, Roman, Friedberg & Lewis, adds that operational readiness hinges on data traceability, accountability and clear title.
“A company must be able to legally prove it owns or has explicit licensing rights to use its repositories for AI training or providing deliverables,” Croft says. “It also must be organized so that an external auditor or regulator can trace how an input resulted in a specific automated output. As far as elements, the company must have an AI policy defining which operations can use automated systems and which cannot. I would look for data mapping and siloing protected data. Contracts should be reviewed to determine data ownership and ensure vendor restrictions and indemnifications.”
Despite these clear frameworks, deployment often exposes strategic missteps. Thakur identifies three prevalent errors. First, leadership frequently treats AI as a technology purchase rather than an operational transformation.
managing director of data, analytics, and AI (CDAIO),
JobsOhio
“Leadership buys a tool, hands it to a department and expects results without redesigning the process the tool is supposed to improve,” she says.
Second, companies bypass change management, causing detached employees to resist or misuse the technology. Third, leaders often pursue highly visible use cases over valuable ones.
“Companies build a flashy chatbot because it’s easy to demo, while the AI application that would actually make a tangible difference, like demand forecasting or quality inspection, sits untouched,” Thakur says.
Grant Menard, Chief Revenue Officer at Lazorpoint, echoes this sentiment regarding operational priority: “AI doesn’t create ROI. Better operations do.”
Menard notes that the failure lies in treating AI as the ultimate goal rather than the mechanism to solve a business problem.
“The companies seeing the strongest ROI start with operational bottlenecks, redesign workflows and use AI where it creates measurable business value,” he says. “AI is the accelerator, not the strategy.”
Lalich aligns with this view, noting that vague objectives and a lack of key performance indicators directly undercut the returns they aim to capture.
“Successful AI implementation projects are based on problems that are well suited to an AI solution,” he says. “AI is a powerful tool but not necessarily the solution to every business or technology challenge.”
Legal risks
Expanding AI usage introduces distinct legal liabilities that demand corporate governance. Croft warns that organizations must actively mitigate intellectual property infringement, confidentiality breaches, negligence, fraud, over reliance on automated deliverables and regulatory non-compliance.
Thakur highlights three critical risk zones requiring immediate oversight:
- Output liability: If an employee uses generative tools to draft contracts, financial projections or legal summaries that contain fabricated or inaccurate figures, the employer retains full liability, not the vendor.
- Data exposure: Inputting proprietary, client, or personally identifiable information into public AI systems triggers severe confidentiality and privacy violations, particularly when operating under strict regulatory frameworks.
- Intellectual property: Deploying AI-generated content commercially without vetting the underlying training data introduces severe ownership risks within a developing legal landscape.
Addressing these risks requires strategic guardrails, she says, rather than total avoidance. Effective protocols include formal internal AI usage policies,
mandatory human review for financial or client-facing choices, and rigorous vendor contract audits to allocate liability accurately.
Menard reinforces this structural focus, saying that the primary threats are operational rather than technical.
“Without clear governance, employees will create unnecessary risk around sensitive data, compliance and decision-making,” he says. “Clear policies don’t slow adoption. They make responsible adoption possible.”
Impact on hiring
The integration of automated tools alters human resource allocation and redefines the skills and experience required of new talent. Lalich says that AI reduces the immediate need for certain entry-level technical skills, such as basic coding, data analytics and graphic design, while elevating the value of human-centric capabilities like project management, customer engagement and sales.
“High-level technical roles are still in high demand, but the drop in demand for entry-level technical roles may create long-term challenges for companies by reducing the pipeline of future talent,” Lalich says. “Companies that position themselves for long-term success will find ways to set their experienced talent up for success while still preserving pathways for entry-level employees to grow into advanced roles.”
Thakur notes that while AI compresses routine entry-level tasks like first drafts, research aggregation and basic data synthesis, eliminating these roles entirely damages the future pipeline of senior leaders.
“The opportunity is that AI can actually accelerate how fast an entry-level employee becomes valuable if the company is intentional about it,” Thakur says. “Instead of having a new hire spend months on rote tasks, AI can handle the rote work while the employee is taught to review, question and improve AI output. That requires structured training, not just tool access.”
Menard adds that technical outputs cannot substitute for professional maturity.
“The organizations that get this right won’t use AI to replace experience,” he says. “They’ll use it to help people build it faster through coaching, feedback and critical thinking.”
The shift from SEO to AEO
Externally, generative technology disrupts traditional digital marketing channels by altering how buyers locate vendors. Menard says that market dominance will shift away from sheer content volume toward brand authority.
The companies that win won’t be the ones producing the most content, Menard says. Instead, the ones AI and buyers trust will have built recognizable brands and consistently publish original expertise.
Thakur considers this shift a fundamental marketing realignment that many executives overlook.
“When a growing share of queries gets answered inside an AI summary, traditional SEO stops being the whole game,” she says.
Consequently, she says companies must pivot toward answer engine optimization (AEO). The objective shifts from securing page clicks to ensuring AI models extract and cite your corporate data when generating summaries. This transformation requires specific operational tactics:
- Data structure: Content must feature clear data, transparent sourcing, and authoritative signals so AI models can easily parse and validate the information.
- Authority over volume: Original research and proprietary data carry superior weight because algorithms prioritize credible, well-sourced material.
- New metrics: Marketing teams must track brand mentions within AI summaries, as traditional web traffic metrics will increasingly undercount market reach.
“The companies getting this right are treating their content as a way to build authority and trust, not just keyword volume,” Thakur says.
Concurrently, Croft says that marketers need to establish defenses against intellectual property theft via AI scraping, loss of brand consistency, advertising fraud and compliance infractions.
Executive perspective vs. reality
While much is touted about the potential for AI, it can be the case that business leaders over- or underestimate its capabilities. Croft observes that overly optimistic leaders view AI as an autonomous, legally independent agent capable of executing contracts or rendering final decisions. In reality, the corporation and its executives remain strictly liable for the actions, errors and discriminatory outputs of their software systems.
Conversely, pessimistic executives believe that a total ban on AI mitigates all legal exposure. This approach typically forces usage underground or positions the enterprise behind more agile competitors.
Meyers, Roman, Friedberg & Lewis
“AI is just a tool,” Croft says.
Menard says most leaders ask where they can use AI, but the better question is where is the business constrained?
“The biggest gains come from redesigning how work flows across the organization,” Menard says. “AI simply helps great businesses execute even better. AI isn’t the competitive advantage. A better business is.”
Thakur challenges the optimistic view that AI is a self-sustaining asset.
“In reality, AI adoption is an ongoing discipline: models need oversight, processes need redesign and value compounds only with continued investment, not a single rollout,” she says. “Leaders in this camp often underestimate how much change management and governance are required to get real ROI.”
Similarly, the pessimistic view mischaracterizes AI as a temporary fad or an immediate existential threat, leaving companies sidelined while competitors build organizational capability. Thakur also identifies a more subtle executive error: assuming the workforce lacks the capacity to adapt. In practice, employees acquire AI proficiencies rapidly when provided structured, practical training.
“The leaders who get the most value are the ones who treat AI as neither magic nor threat, but as a capability that has to be built deliberately, with clear use cases, real governance and a workforce that’s actually been trained to use it well,” Thakur says. ●
From Our Presenting Sponsor, JobsOhio
In Ohio, AI Gets Built — and Put to Work
This month, CNBC named Ohio America’s No. 1 State for Business, the first time in our history that Ohio has held the top spot, and the first time in more than a decade that a Midwestern state has led the rankings. We didn’t get there by luck.
Ohio climbed from No. 34 in 2010 by playing the long game and relentlessly executing on the fundamentals. This year, that work earned Ohio the No. 1 ranking and A+ grades in the two categories CNBC weighted most heavily: infrastructure and cost of doing business. Those also happen to be two of the things artificial intelligence companies — and the enterprises racing to build and adopt AI — need most. That’s not a coincidence. It’s a strategy.
AI represents a $15 trillion global market transformation by 2030, and it will reshape every industry in which Ohio already leads: health care, manufacturing, aerospace, logistics, agriculture and financial services. The winners of this transformation won’t be the companies that stop at developing AI in a lab. They’ll be the ones that invest, build, power and deploy it at scale in the real world. That’s the opportunity we see in Ohio.
It’s why JobsOhio launched the AI Super Sector — not to replicate Silicon Valley’s model in the Midwest, but to build something more complete and primed for growth. We’re building something different, and, I believe, more complete: a full AI ecosystem where breakthrough technology is researched, built, powered and deployed at scale — across factories, hospitals, logistics networks and defense systems.
Nowhere is that convergence more visible than in Northeast Ohio. Cleveland Clinic, one of the world’s leading health care institutions, is applying AI to transform patient care and clinical research. NASA Glenn Research Center anchors a space and aerospace ecosystem where advanced technologies are accelerating innovation in areas ranging from propulsion research to advanced materials. In Youngstown, SoftBank’s $3 billion investment is turning the former Lordstown facility into a manufacturing hub for modular data centers supporting OpenAI — a signal to the world that the infrastructure powering AI will be built here. In Streetsboro, LayerZero has created more than 500 jobs producing the critical power systems that keep data centers running. Across the region’s storied manufacturing base, companies are using AI to shorten the distance between an idea and a finished product, and build the intelligent factories of the future. And this month, Case Western Reserve University led a coalition that secured a 10-year, $160 million National Science Foundation grant to use AI to accelerate the region’s lab-to-market pipeline, moving university research to Ohio’s manufacturers.
This is where Ohio has an advantage that is difficult to replicate: we have the customers. Ohio’s diversified $928 billion economy puts AI innovators next door to leading companies in health care, automotive, aerospace, logistics and agriculture. These companies aren’t just neighbors, but first adopters and scaling partners. Pair that with the second-lowest cost of doing business in the country and the nation’s No. 1 infrastructure, and AI companies can grow faster and more profitably here than anywhere else in America. We hold ourselves to the same high standards. Led by Managing Director of Data Analytics and Artificial Intelligence Payal Thakur, JobsOhio is becoming an AI-powered organization — using AI to sharpen how we identify opportunities, serve companies and move at the speed this AI economy demands.
Then there’s talent. Ohio is home to more than 600,000 STEM students and workers, and we’re moving aggressively to prepare our workforce for what comes next. Through AI Ready Ohio, the nation’s first state-level AI workforce certification program, JobsOhio and our elite partners are helping Ohioans develop practical AI skills that can be applied across industries. The program is already outperforming its certification goals in Ohio and now serves as the national model for the AI Ready America framework.
Our universities are moving, too. The Ohio State University is integrating generative AI education into its undergraduate experience, while Bowling Green State University offers the nation’s first interdisciplinary AI bachelor’s degree. That matters because Ohio doesn’t just need people who can develop Ohio. We need people who know how to put it to work. That is the opportunity in front of us.
In Ohio, companies can dream it, build it, test it and scale it. With AI, they can do it all faster. That’s the conversation we’ll continue on August 27 in Cleveland at the AI Impact Forum, where senior business leaders will gain practical insights and strategies for leveraging AI to drive operational efficiency and deliver measurable results. JobsOhio is proud to sponsor this event because we believe the businesses that move first on AI will define the next generation of economic leadership — and we intend for them to do it in Ohio. ●
AI Impact Forum 2026 Honorees: AI Builders
AgileBlue, led by President Tony Pietrocola, is redefining cybersecurity for mid-market organizations through its AI-native Security Operations Platform. Powered by Sapphire AI, the platform is engineered to augment security professionals rather than replace them. It accelerates threat detection, automates case summarization, enriches investigations with contextual intelligence, and reduces alert fatigue. This enables security analysts to investigate and respond to cyber threats in minutes instead of hours.
Unlike legacy software with bolt-on features, Sapphire AI is embedded throughout the platform to streamline the entire security operations lifecycle to surface actionable recommendations and prioritize high-risk incidents. The platform specifically serves resource-constrained organizations in regulated sectors like health care, financial services, and the public sector that require enterprise-grade security without the overhead of a large in-house team.
By combining automated processing with 24/7 human-led Security Operations Center expertise, AgileBlue bridges a critical workforce gap. This dual approach ensures that all AI-generated insights remain grounded in real telemetry, allowing analysts to validate actions before execution. Pietrocola and his team designed this system to solve the persistent shortage of skilled security talent while managing the growing volume of sophisticated threats. The result is an accessible, transparent, and resilient defense system that empowers smaller enterprises to protect their data with speed and confidence. This operational framework drives efficiency and builds lasting cyber resilience for companies navigating today’s complex digital environment. ●
Aidan Systems, under the leadership of Founder and CEO Quentin Fisher, delivers operational clarity to complex organizations through its AI Control Tower. Rather than functioning as a one-time implementation or a simple chatbot, this managed AI operating platform serves as a permanent operating layer above a client’s existing systems of record. It integrates data sources — including EHR, ERP, and CRM platforms — and translates them into clear business meaning.
The initiative targets mid-market entities in health care, behavioral health, manufacturing, and supply chain sectors that lack the resources to maintain full internal AI teams. The solution relies on five capabilities: a secure data foundation; a business ontology and a semantic layer; predictive models; governed AI observability via Aidan’s Managed AIR service; and structured training through the Aidan Innovation Institute.
The real-world impact of Fisher’s platform is substantial. In health care operations, the system optimized provider capacity for MDLive, yielding $2 million dollars in annual savings and reducing seasonal wait times by 50 percent. For Integrated Services for Behavioral Health, it manages support care for thousands of children, helping the provider secure performance bonuses. In manufacturing, the Control Tower supports production visibility, maintenance intelligence, forecasting, inventory risk, sales growth intelligence and cross-system decision support. Aidan Systems bypasses superficial demonstrations to establish a secure, measurable, and sustainable framework for daily operations. ●
CHAMP Titles, led by CEO Shane Bigelow, is transforming automotive administration with its Compliance Automated Decisioning Engine, known as CADE.ai. This proprietary intelligence layer automates the rigorous validation checks required to process vehicle title and registration transactions. By evaluating transaction data, ownership records, and lien documentation simultaneously, the system identifies all errors upfront. This simultaneous review eliminates the traditional, fragmented back-and-forth rework cycles that historically delayed transactions for up to 60 days.
CADE.ai serves state motor vehicle agencies, automotive dealers, lenders, insurance carriers, and fleet operators. Embedded within CHAMP’s cloud-native platform, it transitions vehicle titling from a manual, paper-reliant chore into a real-time digital workflow. The engine provides flexible levels of automation, ranging from assisted agent review to fully autonomous approvals, while standardizing compliance across jurisdictions.
The practical results under Bigelow’s guidance are clear. DMV title clerks utilizing the platform can now process more than five times as many titles per day, eliminating millions of physical documents annually and reducing corporate administrative overhead. Further, processing timelines drop from several weeks to mere hours or near real-time, drastically reducing uncertainty for consumers and businesses alike. CHAMP Titles successfully combines full digitization with real-time validation to turn an industry bottleneck into an efficient, secure and modern competitive advantage. ●
CPG Radar, led by CEO Afif Ghannoum, functions as an AI-driven market intelligence platform built specifically for the consumer packaged goods and nutraceutical industry. The platform continuously monitors and synthesizes market signals, ingredient trends, competitive activities, and shifting demand data. It serves both sides of the supply chain: CPG brands seeking new formulation opportunities, and ingredient suppliers aiming to identify commercial leads. High-profile clients include Nestlé Health Science, Nature Made, and Kerry.
Ghannoum assembled a specialized team to resolve a major industry challenge: the extreme lag and fragmentation of market information. Traditionally, analysts compiled data in isolated silos, causing companies to miss market windows. CPG Radar addresses this via its True Benefits Analysis framework, which evaluates ingredients across scientific substantiation, market traction, and consumer sentiment simultaneously.
The platform compresses decision cycles from months into days. For a global brand, it identified overlapping ingredient benefits to save $1 million in costs of goods sold. For a direct-to-consumer business, social listening tools flagged negative consumer perceptions, preventing a costly failure. Additionally, the platform’s LeadLogic tool has accelerated lead-to-opportunity pipelines for suppliers like Kerry by mapping fit for sales pitches. CPG Radar successfully removes the guesswork from product development, integrating predictive insights directly into daily workflows to reduce financial risk. ●
Graici, founded and guided by CEO Stephen McHale, addresses a critical administrative barrier in health care with its innovative Data Wallet. Medicaid renewal is historically complex, forcing eligible individuals to navigate fragmented documentation across employment, financial, and health care records. This frequently leads to procedural disenrollments. Graici’s platform resolves this by utilizing generative and conversational AI to securely acquire, verify, and organize user-permissioned data into a single source of truth.
The Data Wallet serves Medicaid members, managed care organizations, health care providers, and government agencies. The AI applications simplify complex notices, offer personalized guidance, and generate verified, pre-filled renewal forms. This human-centered approach allows participants to complete their renewals in roughly six minutes, which is five times faster than traditional methods. Early implementations show an 80 percent renewal completion rate.
Beyond helping individuals retain continuous health care coverage, McHale’s platform delivers massive operational relief to caseworkers and community partners. By enhancing application accuracy before submission, it minimizes downstream administrative review. Graici’s operational modeling indicates that saving just 10 to 12 minutes per renewal across large populations yields $ 12 million to $18 million dollars in annual labor efficiency value. Ultimately, Graici demonstrates that data empowerment can optimize public health administration while strengthening the relationship between individuals and the support systems designed to serve them. ●
K2 Venture Partners, led by Founding Partner John Knific, bridges the gap between industry expertise and software engineering through its proprietary development system, Meridian. Midsize companies often understand their markets and operational challenges thoroughly but struggle to translate that knowledge into technical plans and shipped software. Meridian captures this business intent and converts it into explicit product requirements and technical tasks, supporting AI-assisted development while retaining human oversight from senior engineers.
The platform serves growth-stage and established companies in sectors historically under served by modern software, including manufacturing, health care logistics, commercial insurance, auto collision repair, and education. By utilizing a small, senior onshore team rather than traditional, slow offshore development models, Meridian accelerates product velocity for regional clients.
The regional impact under Knific’s direction is twofold. First, it allows business leaders to stay closer to product decisions, resulting in iterations that accurately reflect how their companies operate. Second, it keeps high-value, AI-enabled product development work local to Northeast Ohio, allowing the firm to hire and retain tech talent from regional state universities. As Meridian is deployed across more projects, it continuously documents architectural choices and patterns. This structured learning model ensures subsequent software development becomes faster and more consistent, making regional enterprises far more competitive. ●
MorelandConnect LLC, under the leadership of CEO Jeff Kavlick, has successfully transitioned from a traditional custom software consultancy into an AI-first software engineering firm. Rather than simply treating artificial intelligence as an optional feature, the company has embedded AI into every phase of the software lifecycle since January 2025. The initiative pairs a repeatable delivery methodology with enterprise-grade platforms to help organizations identify high-value use cases and securely deploy production-ready solutions.
The firm serves middle-market businesses across Northeast Ohio and the United States, including health care organizations, manufacturers, financial services firms, and nonprofits. Its customized applications address intelligent document processing, predictive analytics, knowledge assistants, and workflow automation. Kavlick’s team leverages Microsoft Azure AI services, large language models, and retrieval-augmented generation (RAG) to accelerate development while ensuring strict data security.
The impact of this transformation has altered how MorelandConnect engages its clients, turning AI strategy into a core component of almost every new software project. For clients, this methodology shortens implementation timelines and eliminates repetitive tasks, allowing employees to make faster, better-informed decisions based on complex corporate data. By standardizing its AI architecture patterns and conducting executive education, MorelandConnect has established itself as a visible thought leader, accelerating responsible and secure technology adoption across the regional business community. ●
NCompas Technology Solutions Inc., led by CEO Ramana Bhavaraju, has engineered an automated quoting system that resolves a major operational bottleneck for a United States rubber manufacturer. Historically, generating a quote was a multi-week manual chore. Estimators had to read 2D and 3D engineering drawings by hand, calculate cross-sectional areas manually, and rebuild individual cost models. This slow process capped the number of opportunities the company could pursue and increased pricing errors.
Bhavaraju’s team utilized computer vision and AI document intelligence to build a system that ingests RFQs and engineering drawings directly. The custom vision model, trained on historical records, measures cross-sectional areas automatically — the most error-prone step in manual workflows. It scores each extracted field for confidence, allowing senior estimators to verify only uncertain values before feeding a calculation engine that mirrors the company’s precise pricing logic.
The impact of NCompas’s platform is transformative, collapsing quote turnaround times from two to three weeks down to just one to two hours. This efficiency enables the existing small estimating team to increase capacity from 25 quotes per week to over 100 without expanding headcount. Crucially, the system replaces manual lookups to improve accuracy and codifies over 50 years of pricing expertise from a retiring lead estimator into deterministic rules, eliminating key-person risk and turning quoting into a scalable competitive advantage. ●
AI Impact Forum 2026 Honorees: AI Implementers
Under the guidance of Founder Dr. Jorge Garcia-Zuazaga, Apex Skin, a rapidly growing dermatology practice with 15 locations, has implemented artificial intelligence with a distinct philosophy: augment the human team rather than replace it. Facing compounding pressures from high call volumes, documentation burnout and fragmented patient communications, the practice adopted a deliberate “trial first” model to carefully integrate operational AI solutions.
To address administrative bottlenecks, Apex Skin integrated the conversational AI agent Prosper AI alongside Klara, an AI-assisted patient text messaging platform. By automatically absorbing routine inquiries like scheduling and follow-ups, the AI allowed the practice to redistribute its staff into a dedicated team of human agents focused on complex clinical cases where empathy is most needed. To further reclaim clinical hours, the practice deployed Scribe 2.0, an ambient clinical documentation tool that transcribes patient visits in real time. This voice-driven system has successfully eliminated after-hours charting, giving providers valuable time back to focus entirely on patient care.
Strategically, Apex Skin also developed an innovative AI visibility playbook using the Reputation platform. This ensures that the practice is accurately surfaced and recommended by modern AI search engines, successfully protecting and growing its regional patient acquisition market share. Dr. Garcia-Zuazaga’s successful rollout proves that integrating thoughtful, patient-facing AI can optimize clinical operations and protect business growth without sacrificing the essential human relationships at the core of health care. ●
Under Chief Transformation and Client Experience Officer, Brittney Garrett, Clearstead has mastered a human-plus-technology approach to wealth management. Rather than relying on robo-advisers to completely replace its human teams, the firm utilizes artificial intelligence and machine-learning systems behind the scenes to optimize backend operations and scale its advisory capabilities.
Clearstead leverages advanced technology across several key areas of its business. For its specialized Direct Indexing strategies, the firm utilizes algorithmic optimization software and predictive models to build optimal stock portfolios that mathematically automates tax efficiency. Within its ClearAccess platform, which manages nearly $3 billion in complex alternative investments, Clearstead employs automated data parsing and machine-learning analytics. This tech runs rapid quantitative due diligence by sorting through thousands of pages of unstructured data and pitch books.
Further, during strategic acquisitions, Clearstead deploys AI-powered financial-tech platforms to automate data translation, scrub document history, and eliminate human clerical errors. And its ClearSight Analysis proprietary financial triage tool employs automated document-parsing technology to read tax returns and balance sheets instantly.
By automating heavy mathematical lifting and text parsing, This strategic framework ensures that Clearstead’s advisers spend significantly less time manipulating spreadsheets. Instead, they can direct their energy toward high-touch, tailored estate planning and relationship building. This innovative balance maximizes both corporate efficiency and client satisfaction. ●
Under the leadership of Bryce Sylvester, Managing Director, Cushman & Wakefield | CRESCO Real Estate has taken a proactive and structured approach to corporate artificial intelligence. While many organizations allow AI adoption to happen organically, creating fragmented tool sets across different departments, CRESCO recognized early on that consistency is as critical as speed in the real estate sector.
Rather than supporting a chaotic collection of individual platforms, CRESCO standardized its AI strategy on a single platform, Hatz AI. The company deeply integrated this technology into the everyday workflows that drive their business, including proposal development, reporting, research, information synthesis, and deal-related data extraction. This strategic decision transformed AI from isolated, individual experimentation into a scalable, unified operational capability.
The business impact of this structured framework has been remarkable. CRESCO has achieved an estimated 30 to 50 percent reduction in the time required for proposal creation, reporting, and research. Client deliverables are now more structured, professional, and consistent across the firm, while operational risks associated with fragmented tools have been significantly mitigated. Under Sylvester’s direction, CRESCO has answered a vital question for modern firms: how an organization should use AI, successfully proving that real-world success lies in bringing order to technology. This strategic foresight has established a repeatable, highly efficient framework that can scale alongside the company’s future regional growth. ●
Danny Berman, Chief Technology Officer of Geauga Mechanical Company, has spearheaded the groundbreaking Sentinel Initiative, bridging the gap between legacy industrial trades and cutting-edge technology. Founded in 1950, this third-generation mechanical contractor possessed decades of valuable project data that historically remained disconnected on servers. Recognizing this untapped resource, Berman constructed an in-house performance analyzer using custom API integrations across the company’s existing software systems within Microsoft 365.
This centralized, agent-ready data pipeline powers real-time dashboards on Power BI for project managers, executives, and field leaders. These dashboards allow teams to monitor project health instantly and predict productivity trends, transforming what used to require days of manual analysis into a simple, one-minute task. Additionally, the system automatically flags financial and costing discrepancies, saving the company tens of thousands of dollars in active costing errors.
The Sentinel Initiative has successfully transformed Geauga Mechanical from a reactive organization into an industry-leading, proactive force. Rather than depending on off-the-shelf platforms, Berman’s customized, homegrown AI architecture has eliminated information silos and reduced construction risk. This initiative serves as a shining regional model for how traditional contractors can harness artificial intelligence to drive data-informed decisions, elevate precision, and foster a culture of technological excitement. Under Berman’s leadership, Geauga Mechanical is truly building a smarter future for Northeast Ohio’s contracting industry. ●
Under the leadership of Regional Managing Director Bethany Bryant, Glenmede, an independent investment and wealth management firm, has executed a deliberate, firm-wide embrace of artificial intelligence to elevate its client-centric services. Rather than searching for a single fix, the firm strategically deployed a suite of specialized tools — including Microsoft Copilot, Anthropic’s Claude, and Zocks — tailoring each technology to the specific workflows they do best.
Copilot serves as a foundational efficiency tool, allowing advisors to quickly summarize complex legal documents, such as trust agreements and insurance policies, which saves up to 30 minutes of preparation per meeting. For deep, complex analytical work, Claude reviews external account statements to identify risks and consolidation opportunities in minutes rather than days.
Most impactfully, the AI-powered meeting intelligence platform, Zocks, was piloted by Northeast Ohio advisers before a firm-wide rollout. Zocks listens to client meetings, automatically generates structured summaries, and integrates directly with the firm’s CRM. This eliminates hours of manual post-meeting documentation each week.
By automating these heavy administrative and analytical burdens, Bryant’s team has successfully reclaimed valuable weekly hours. Advisers are now more present in the room, shifting their focus toward deeper human relationships and strategic wealth planning. Glenmede’s thoughtful, adviser-driven implementation serves as a brilliant model for integrating sophisticated modern AI while preserving the essential trust and personalization at the core of wealth management. ●
Under the visionary leadership of President and CEO Rick Organ, Hynes Industries has established a custom, enterprise-wide intelligence platform that sets a new benchmark for advanced manufacturing. Unlike typical firms that deploy isolated technology, Hynes hired a dedicated data analytics expert to construct a unified digital infrastructure that ingests data from production logs, equipment sensors, and ERP systems.
Within this robust environment, Hynes deployed an AI-powered, vision-based quality inspection cell at its Youngstown facility, engineered specifically for high-volume robotic component production. High-resolution cameras and lasers verify critical geometric tolerances within thousandths of an inch in real time. This automation has reduced quality cycle times by over 85 percent, dropping from nearly three minutes to a mere 15 to 20 seconds, while cutting shift-level inspection staffing requirements significantly.
The benefits of Hynes’ data-driven platform extend far beyond quality control. Automated gearbox sensors provide up to two hours of advance warning before unplanned downtime, and custom large language models synthesize production logs to surface recurring systemic errors. By embedding artificial intelligence into core operations, Organ has successfully positioned Hynes to meet the strict quality and speed demands of fast-growing sectors like data centers, solar energy, and robotics, proving that Northeast Ohio manufacturers can compete on precision and intelligence. ●
Under the visionary leadership of CEO SueAnn Naso, Staffing Solutions Enterprises (SSE) has successfully integrated conversational artificial intelligence into the recruiting process while preserving its people-first culture. In May 2024, SSE launched “Jamie,” a 24/7 virtual recruiting assistant designed to immediately engage, screen, and capture qualifications for every job applicant. Jamie directly connects with job seekers when it is most convenient for them, automatically feeding pre-screened data into the firm’s applicant tracking system.
The business and operational outcomes of this implementation have been stellar. In 2025 alone, Jamie successfully contacted 22,145 candidates and connected with 14,652 of them, qualifying 8,663 interested and eligible applicants. This automated front-end screening achieved over a 50 percent increase in candidate engagement rates and drove a 60 percent reduction in the time required to present qualified candidates to clients. Furthermore, SSE achieved a 35 percent reduction in total time-to-fill and raised candidate experience ratings to 4.67 out of 5.
By automating administrative outreach, Naso’s team has liberated human recruiters from repetitive workflows, enabling them to focus on high-value client advisory services and relationship building. SSE’s structured commitment to training and responsible usage policies ensures that Jamie acts as a powerful amplifier of human capability, establishing the firm as a regional leader in operational AI excellence. ●