AI Agent Operational Lift for Burke Institute in Cincinnati, Ohio

Why AI matters at this scale

Burke Institute sits at a critical inflection point. As a mid-market market research firm (201-500 employees) founded in 1975, it possesses deep domain expertise and a loyal client base but faces mounting pressure from tech-enabled competitors and AI-native startups. The firm's core work—designing surveys, fielding studies, cleaning data, and delivering insights—is fundamentally data-intensive and rule-based, making it highly susceptible to AI automation. At this size, Burke is large enough to invest in dedicated AI resources but nimble enough to deploy changes faster than enterprise behemoths. Adopting AI isn't just about efficiency; it's about survival in an industry where clients increasingly expect faster, cheaper, and more predictive insights.

Concrete AI opportunities with ROI framing

1. Intelligent survey programming and scripting

Translating a client's research brief into a programmed survey is a labor-intensive bottleneck. Generative AI, fine-tuned on historical surveys, can draft complete questionnaires in Qualtrics or Decipher formats in minutes. For a firm running hundreds of projects annually, reducing programming time by 70% could save thousands of billable hours, allowing researchers to take on more projects without expanding headcount. The ROI is immediate and measurable in labor cost avoidance and increased throughput.

2. Automated open-ended response coding

Manually categorizing thousands of verbatim responses is one of the most tedious and expensive steps in quantitative research. Modern NLP models can perform sentiment analysis, theme extraction, and categorization with accuracy rivaling human coders. This shifts a multi-day process to near-instant, slashing project timelines and enabling iterative, real-time analysis during live fieldwork. The cost savings are substantial, often covering the AI investment within a single quarter.

3. AI-augmented insight generation and reporting

Drafting reports from cross-tabulated data is a repetitive, high-effort task. Generative AI can produce first-draft executive summaries, highlight statistically significant findings, and even suggest strategic recommendations based on patterns in the data. This doesn't replace the consultant's judgment but accelerates the journey from data to story, improving margins on fixed-bid projects and enhancing client satisfaction with faster deliverables.

Deployment risks specific to this size band

Mid-market firms like Burke face unique risks. First, talent churn: hiring and retaining AI-skilled staff is difficult when competing against tech giants offering higher salaries. Mitigation involves upskilling existing researchers rather than relying solely on external hires. Second, data privacy: clients entrust Burke with proprietary data; using public AI APIs could violate NDAs. The solution is deploying private, tenant-isolated LLM instances or using enterprise agreements with strict data handling terms. Third, legacy integration: decades-old workflows and tools may not plug neatly into modern AI pipelines, requiring careful change management and phased adoption to avoid disrupting ongoing client work. Finally, over-reliance on black-box models could erode the methodological rigor that is Burke's brand; AI outputs must always be auditable and explainable to maintain client trust.