AI consulting without the hype
Most AI consulting today is sold by firms whose business model is convincing you to spend a lot of money on a vague platform. The deliverable is a 60-slide deck, a list of vendors to evaluate, and a recurring retainer. The actual business problem stays unsolved.
Preisser Solutions runs AI consulting the opposite way. The deliverable is a short written roadmap that an owner can hand to their team — or to Preisser Solutions for a build. The recommendations are specific (which workflow, which tool, what it should do) rather than abstract ("adopt AI").
Tyler runs every engagement personally, in Hays, in person where possible. The conversation is plain English. Buzzwords get translated. Owners leave the room knowing what to do next, not feeling vaguely overwhelmed by AI.
Where AI actually helps small businesses
Across Hays small businesses, AI pays off reliably in a handful of places. These are the patterns that have shown up over and over in real engagements:
- Lead handling — qualifying inbound leads, capturing the right details, and routing the qualified ones to the right human fast
- Customer reactivation — working through the dormant customer list with personalized outreach the team would never have time to send manually
- After-hours and overflow — catching calls and messages when the office is closed, capturing the work, and routing emergencies correctly
- Document and invoice processing — reading invoices, contracts, and forms; extracting structured data; routing for approval
- Internal knowledge — letting the team ask an AI agent about pricing, procedures, and customer history instead of paging the owner
- Reporting and dashboards — pulling data from across systems and surfacing the KPIs owners actually need every morning
- Drafting and editing — pulling together first-draft proposals, estimates, and customer-facing emails so the human is editing instead of writing from scratch
Where AI is not worth it
Just as important as knowing where AI helps is knowing where it does not. Preisser Solutions will say no when the right answer is no. A few places AI is usually not the right tool:
- Workflows that are already automated with a simple Zapier or webhook flow — the AI layer adds cost and a failure mode that wasn't there before
- High-stakes decisions where a human will need to review every step anyway — the AI saves no time and adds risk
- Workflows where the inputs are inconsistent and the business doesn't have the time to define them — the AI will be unreliable
- Replacing a person whose job is primarily relationship-driven — buyers can tell, and the savings rarely pencil out
- Anything that depends on integrating with a software system the business is already planning to leave
Build, buy, or stay manual
Every recommendation in a Preisser Solutions AI roadmap falls into one of three buckets: build a custom AI system, buy an off-the-shelf product, or leave the workflow manual for now. Each call is made on the specific economics of the workflow — not on a blanket preference for one approach.
When to build:
- The workflow is core to how the business actually operates — and an off-the-shelf product almost fits but not quite
- The integration with the existing CRM, accounting, or phone stack would require workarounds that erode the savings
- The data the AI needs to be useful is proprietary — products, services, pricing, customer history, internal documents
- The volume justifies the build cost on a 12 to 24 month horizon
AI roadmap, prototype, and implementation
A typical Preisser Solutions AI consulting engagement in Hays runs in three phases. Some clients stop after phase one with a roadmap in hand. Others roll straight through to a build with the same firm doing the work.
- Phase 1 — Roadmap. In-person workshops with the owner and key team members. Map the current workflows, identify the highest-leverage AI opportunities, and produce a written roadmap with prioritization, recommended tools, and rough cost estimates.
- Phase 2 — Prototype. Build a small working prototype of the top-priority AI use case so the team can see it work against real data before committing to a full build. This phase keeps risk low and proves the concept.
- Phase 3 — Implementation. Build the full system, integrate it with the existing CRM, accounting, and phone stack, train the team, and run it through a real shakedown period. Handled by Preisser Solutions or by an in-house team using the roadmap.