The questions mid-market leadership teams ask us most often, with short answers and a link to where each topic is covered in more depth.
Hive works with mid-market companies up to $2B in revenue, typically 50 to 5,000 employees, across healthcare, financial services, manufacturing, logistics, law, accounting and professional services. Engagements are led by senior advisors from Capgemini, Accenture and McKinsey, with no handoff to junior staff. The firm is based in Nashville, and engagements are not limited to Tennessee.
No. Hive is vendor-agnostic and does not resell software licenses or accept platform referral fees. That independence means recommendations on which tools to use, and which to leave alone, rest on the client's situation rather than a commercial relationship. We regularly work alongside the cloud, data and automation platforms a company already runs.
The first conversation is a 30-minute briefing with a senior advisor, and it is not a sales pitch. It covers where AI is likely to create leverage in the business and what peers are doing. An NDA can be in place before anything confidential is shared, and a sample deliverable, the methodology and client references are available before any agreement.
For most mid-size companies, the right first step is an honest view of current readiness across data, technology, talent, process and governance, before money goes into tools. That view shows which use cases the business can support now and what needs work first. Hive's AI Readiness Assessment is a six-week, senior-led engagement built to produce it.
An AI readiness assessment looks forward: where AI can create value and what the organization needs in place to get there. An AI governance review looks at the present: where AI is already being used and whether that use matches what the company has committed to in contracts and policy. Many companies need both, and the order depends on which question is more pressing.
Yes, in many cases. Focused automation of repeated work, such as rekeying data between systems or first-pass document handling, can pay back within a short engagement. Hive's AI Automation & Quick Wins work ranks those opportunities by payback, scopes the best few and provides senior oversight while your team or chosen vendors deliver them.
Shadow AI is the use of AI tools at work outside anything the company has approved or paid for, often through personal accounts. It is common: MIT NANDA's 2025 research found workers at more than 90 percent of surveyed companies using personal AI tools for work, while only 40 percent of those companies had bought an official subscription. The risk sits in what data goes into those tools.
Most mid-market companies benefit from a proportionate AI governance framework, meaning a clear record of which tools are approved, what data may go into them, who owns decisions and how use is reviewed. It does not need to be heavy. What tends to matter most is that the framework reflects how AI is actually used in the business, rather than a policy written in isolation.
In our conversations with professional services firms, AI use has started to come up at professional liability renewal, and many firms do not yet have a documented answer. A current inventory of where AI is used, and a policy that reflects it, is the kind of record that makes those questions easier to answer. Law and accounting firms tend to feel this first.
Companies are using AI in procurement for intake triage, contract analysis, RFx preparation, spend classification and supplier risk monitoring. Alongside those deliberate choices, AI is also arriving through updates to software already under contract and through suppliers' own use of AI. Hive's four-week diagnostic for procurement maps both and reconciles them against contract terms.
The most reliable way is independent research with the client's senior leaders, because clients tend to speak more openly to a neutral third party than to a supplier. Hive's Client 360 holds those conversations across a defined set of accounts and returns account profiles, a cross-account synthesis and the implications for product roadmaps and forecasting.
For manufacturers, AI often creates value first in predictive maintenance, demand forecasting, quality and supplier risk, using data the plant already produces. For distributors and logistics operators, route and load planning, warehouse labor planning, freight buying and exception management are common starting points. In both cases the focus is on decisions people make every day.
A 30-minute conversation with a senior advisor.
30 minutes · No pitch · Senior advisor · NDA before disclosure