AI consulting for B2B: what to expect and what it costs
Let's start with what most AI consulting looks like.
A team of consultants shows up. They run workshops. They interview stakeholders. They produce a 90-page strategy deck with a maturity model, a transformation roadmap, and a lot of arrows pointing from "current state" to "future state."
Then they leave. The deck sits in a shared drive. Nothing ships.
Six months later, you're in the same spot. Except now you're out $150K and your team is more cynical about AI than before.
We've seen this play out dozens of times. It's why we built Shyft differently.
What AI consulting should actually deliver
Good AI consulting produces infrastructure. Working systems. Code you own.
Not strategy decks. Not maturity assessments. Not proof-of-concept demos that never make it to production.
Here's the test: at the end of the engagement, can your team use what was built? Without the consultants in the room? Without a monthly license?
If the answer is no, you didn't get consulting. You got a dependency.
Shyft's approach: audit, build, scale
We run three phases. Each one produces something your team can use immediately.
Phase 1: Audit (1-2 weeks)
We map your entire tool stack. Every SaaS product, every integration, every data flow. We find the gaps -- where data gets stuck, where teams work around broken handoffs, where AI could actually help.
The audit produces:
- Tool stack map -- every tool, who uses it, how data flows between them
- Gap analysis -- where integrations are missing or broken
- AI readiness score -- how prepared your stack is for AI agents
- Priority roadmap -- what to connect first, ranked by impact
- Quick wins -- things you can fix this week without us
This isn't a theoretical assessment. We look at your actual systems. Real data. Real workflows.
You can start right now with a free AI scan. It takes five minutes and gives you a baseline.
Learn more about our audit service.
Phase 2: Foundation (4-6 weeks)
This is where we build. The foundation phase connects your tools through MCP servers and creates the unified data layer that makes AI useful.
What gets shipped:
- MCP servers for your core tools (CRM, billing, support, analytics)
- Data pipelines that keep everything in sync
- AI agent framework -- the base layer for building custom agents
- Authentication and access controls -- who can see and do what
- Documentation -- everything your team needs to maintain and extend the system
Every line of code is yours. We deploy to your infrastructure. No proprietary middleware. No monthly platform fee.
We build it. You own it.
Learn more about our foundation build.
Phase 3: Scale (2-4 months)
With the foundation in place, we build AI agents that do real work. Not demos. Not prototypes. Production agents your team uses every day.
Examples of what we build in the scale phase:
- Lead scoring that uses product usage data, support history, and billing patterns -- not just firmographic data
- Outreach personalization that references a prospect's actual tech stack and pain points
- Churn prediction that combines billing signals, support volume, and engagement drops
- Pipeline reporting that pulls live data from every connected tool
- Customer health dashboards that update in real-time, not weekly
- Automated handoffs between sales, CS, and support based on account signals
Each agent is scoped, built, tested, and deployed. Your team trains on it. Then we move to the next one.
Learn more about our scale service.
What it costs
Let's talk numbers. AI consulting pricing is all over the map. Here's what the market looks like and where we fit.
Traditional consulting firms
- Big Four (Deloitte, McKinsey, BCG, Accenture): $300-500K+ for a strategy engagement. $1M+ for implementation. Minimum 6-month timeline. You get a strategy deck and a team of junior consultants.
- Mid-tier consultancies: $150-300K for strategy + pilot. 3-6 month timeline. Better attention, but still deck-heavy.
- Boutique AI firms: $75-200K for end-to-end. Variable quality. Some build, some just advise.
What Shyft charges
We price by phase, not by hour. You know exactly what you're paying before we start.
Audit: Starting at $5K-15K
- 1-2 weeks
- Full stack assessment
- Priority roadmap
- Quick wins you can implement immediately
Foundation: Starting at $25K-75K
- 4-6 weeks
- MCP server implementation for core tools
- Unified data layer
- AI agent framework
- Full documentation and training
Scale: Starting at $50K-150K
- 2-4 months
- Custom AI agents for your specific workflows
- Production deployment
- Team training and handoff
- 30-day support after deployment
Total for the full journey: $80K-240K depending on stack complexity and number of agents. That's less than a single senior hire. And you get infrastructure that compounds.
Compare that to a Big Four engagement where $500K buys you a PowerPoint.
The free starting point
Not ready to commit? Start with our free AI scan. It analyzes your tool stack and shows you where you stand. No sales call required.
Timeline: what a typical engagement looks like
Week-by-week for a mid-size B2B company (50-200 employees, 15-25 tools):
Weeks 1-2: Audit
- Day 1-3: Tool inventory and access setup
- Day 4-7: Data flow mapping and gap analysis
- Day 8-10: Report, roadmap, and recommendations
Weeks 3-8: Foundation
- Week 3-4: MCP server development for primary tools (CRM, billing)
- Week 5-6: Secondary tools (support, analytics, communication)
- Week 7: Integration testing and data validation
- Week 8: Deployment, documentation, team training
Weeks 9-20: Scale
- Week 9-10: Agent scoping and design
- Week 11-14: Build and test first batch of agents
- Week 15-16: Deploy and train team on first batch
- Week 17-20: Build, deploy, and train on second batch
Total: 5 months from kickoff to a fully operational AI infrastructure. Some teams go faster. Complex stacks take longer.
In-house vs. outsourced: building your own AI team
Most companies approach this as a hiring decision. It's not. It's a sequencing decision.
You have two paths. Path one: hire internally -- an ML engineer, a data engineer who can build and maintain MCP infrastructure, and an AI product manager to translate between the business and the technical team. Path two: bring in external help for the foundation build, then maintain in-house once the infrastructure is running.
Most startups get this wrong because they start with the hire.
Here's what happens. You post the job, spend three months recruiting, land a strong AI engineer at $220K. They show up, map your stack, and discover your CRM doesn't talk to your data warehouse, your customer data lives in four different systems, and there's no documented API layer to build on. They spend six months on infrastructure cleanup before the first AI workflow ships. You've spent $110K in salary alone before anything works.
The right order is: map use cases first, audit your infrastructure second, then decide what to build in-house versus buy or outsource.
The hiring math is straightforward. A senior AI engineer costs $180-280K in salary plus benefits. A data engineer who can build and maintain MCP infrastructure runs $150-220K. A minimal internal AI team costs $300-500K per year in headcount. Compare that to an external foundation build plus scale engagement at $80-240K total -- with deliverables defined upfront and a fixed endpoint.
That doesn't mean hiring is wrong. It means timing matters.
Hire in-house when you have ongoing AI development needs that won't end, when your data is too sensitive to expose externally even with proper controls, or when you've already built the foundation and need engineers who can compound on it.
Use external consulting when you're doing a first build, when you need a stack audit before making infrastructure decisions, or when you need to move faster than a four-month hiring process allows.
The most common middle path: bring in a consultant for the foundation build -- usually six to ten weeks -- then hire one AI-capable engineer to maintain and extend it. You get speed upfront and ownership on the back end.
How to evaluate an AI consulting proposal
Most proposals look similar on the surface. The work is in knowing what to ask before you sign anything.
Before the call
Start with their case studies. Do they include specific outcomes -- time saved per week, workflows automated, specific tools connected? Or is it client logos and vague language? If you can't find a single concrete number on their website or LinkedIn, that's a signal. Anyone doing real implementation work has results they can point to.
During the scoping call
Ask these five questions directly:
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"What will we have at the end that we own outright?" -- The answer should be: working code, documentation, and deployed infrastructure in your environment. Not a strategy deck.
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"Where will our data go during this engagement?" -- It should stay in your infrastructure. If the answer involves their proprietary platform or anything you don't control, understand exactly what that means before proceeding.
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"How many of your engagements have shipped within the timeline you're proposing?" -- If they can't give you a number or a range, flag it.
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"Which specific tools in our stack have you connected before?" -- They should name specific APIs, specific MCP servers, specific integration patterns. If they talk abstractly about "enterprise system integrations" without naming anything, they're either early in their practice or they haven't done the specific work you need.
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"What does your handoff look like?" -- There should be a documented handoff process, internal training for your team, and a defined post-deployment support window. Thirty days minimum.
Red flags in proposals
Watch for retainers baked into the initial build scope. A foundation build should have a fixed endpoint. Any pricing that requires a signature before they've scoped your actual stack is a problem. Deliverables described as "strategic recommendations" are not deliverables. Shipped infrastructure is.
The reference check
Ask for two or three clients who have gone through the full build. Ask them: did it ship on time, do they still use it without the consultant involved day-to-day, and what broke after the engagement ended. That last question tells you the most.
What to look for in an AI consulting partner
We're biased, obviously. But here's what we'd tell any founder shopping for help.
They should build, not just advise
Ask one question: "What will I have at the end that I can use without you?" If the answer involves a strategy deck, a roadmap, or "ongoing advisory," keep looking.
You need infrastructure. Working systems. Code.
You should own everything
Every MCP server, every pipeline, every agent, every line of code. If they want to host it on their infrastructure or charge a monthly platform fee, that's a dependency, not consulting.
They should know your stack
AI consulting that doesn't understand HubSpot, Stripe, and Zendesk at the API level isn't AI consulting. It's theory.
Ask about specific tools. Ask how they handle auth. Ask about rate limits and data sync.
The timeline should be weeks, not quarters
If someone tells you AI implementation takes 12-18 months, they're either building something too complex or billing by the hour.
A useful AI foundation ships in weeks. Custom agents ship in months. If it takes longer, the scope is wrong.
Pricing should be fixed, not hourly
Hourly billing incentivizes slowness. Fixed-price, phased engagements incentivize shipping. You should know exactly what you're paying before work starts.
Red flags to watch for
Run away if you hear any of these:
- "We'll start with a 3-month discovery phase." Discovery shouldn't take more than 2 weeks. If it does, they're padding.
- "You'll need our proprietary AI framework." Proprietary means lock-in. The best infrastructure uses open standards like MCP.
- "We recommend building a custom LLM." No B2B company with 10-500 employees needs a custom LLM. You need better infrastructure connecting existing models to your data.
- "We can't give you a fixed price until we scope it." They should be able to give you a range based on team size and tool count. Scoping is the audit. It shouldn't be free, but it should be fast and cheap.
- "Our team will manage the AI agents for you." That's outsourcing, not consulting. You need your team to own and operate what gets built.
- "We'll need access to all your data in our environment." Your data should stay in your infrastructure. Always.
What happens after the build: maintaining your AI infrastructure
Most consultants don't talk about this part. They should.
AI infrastructure is not static. It needs ongoing attention -- not constant development, but consistent maintenance. Here's what breaks without it.
API changes -- your CRM releases a major update and the MCP server connecting it to your AI agents uses deprecated endpoints. Connections start failing -- sometimes silently. If nobody owns that connection, you may not notice until a workflow has been returning bad data for weeks.
Credential rotation -- API keys expire. OAuth tokens need reauthorization. Service accounts get recycled during IT audits. If credential ownership isn't documented and assigned, these connections fail quietly.
Agent prompt drift -- an AI agent configured on a prompt written three months ago may behave differently as the underlying model updates. Someone needs to review agent outputs on a regular cadence -- quarterly at minimum.
Model updates -- Claude or GPT releases a new version and behavior changes. Agents that ran reliably on one model version may need prompt tuning when the model underneath them changes.
A reasonable maintenance schedule: monthly check on all active MCP server connections, quarterly review of agent outputs for drift, semi-annual review of agent prompts against current business logic, and credential rotation on a defined schedule tied to your security policies.
It doesn't need to be an engineer. It should be an ops-oriented person who understands the data flows -- someone who can read logs, spot a failure pattern, and know when to fix a config versus when to call in technical help.
Our 30-day post-deployment support is designed for this transition. We're in the room while your team takes ownership, not dropping a GitHub repo and disappearing.
Who AI consulting is for (and who it isn't)
Good fit:
- B2B companies with 10-500 employees
- Series A through D, or profitable SMBs
- Running 10+ SaaS tools with no unified data layer
- Revenue team (sales, marketing, CS) spending time on manual data work
- Technical enough to maintain infrastructure once built (or willing to hire for it)
Not a fit:
- Pre-product startups (build your product first)
- Companies looking for a chatbot on their website (that's a product, not infrastructure)
- Teams that want someone to "figure out their AI strategy" (you need to know what problems you're solving)
- Organizations that can't give tool access during the engagement (we need to see your actual systems)
Start here
Three options, depending on where you are:
- Just curious? Run our free AI scan. Five minutes. No sales call. See where your stack stands.
- Know you need help? Look at our services page. Pick the phase that matches where you are.
- Ready to go? Book an audit. We'll map your stack in two weeks and give you a roadmap you can execute on -- with us or without us.
We build infrastructure, not strategy decks. You own everything.