Lots of hype, no plan, scattered pilots.
What we did
- Ran an opportunity + risk assessment
- Prioritized a roadmap by ROI
- Stood up a governed first use case
Result Replaced scattered experiments with one funded roadmap and a working pilot.
AI Consulting
AI Strategy & Consulting
A clear, ROI-first AI roadmap; where to apply AI, what to build, and what to ignore.
What it is
AI without the hype.
Most AI initiatives stall because they start with tools instead of outcomes. We start with your P&L: we find the workflows where AI moves a real number, then sequence a roadmap you can actually ship.
Core services include
- AI opportunity audit across your operations
- Use-case scoring by ROI and feasibility
- Build-vs-buy and model selection guidance
- Data readiness and governance review
- Phased implementation roadmap
- Risk, compliance, and guardrail planning
Definition
What is AI Strategy & Consulting?
AI Strategy & Consulting is the work of deciding where artificial intelligence actually belongs in your business: auditing your workflows, data, and goals to produce a prioritized, ROI-first roadmap of what to build, what to buy, and what to ignore, before anyone writes code.
How it works
We assess your data, tools, and processes, then rank candidate AI use cases by business impact versus effort and risk, and map the right model and platform to each one, so you invest in the few projects that pay off instead of chasing hype.
Who it’s for
For leaders and operators who know AI matters but are unsure where to start or where they are wasting effort; the outcome is better decisions and a clear, sequenced plan, so budget and team time go to the automations and tools that actually move the business.
In practice
A services company wants to add AI everywhere at once; the roadmap identifies that automating intake and support triage delivers the fastest payback, defers a costly custom model that would not, and sets the model, budget, and governance for each phase.
Who it's for.
- Leaders who know AI matters but not where to start
- Teams burned by a failed pilot
- Companies sitting on data they don't use
- Orgs that need a plan the board will fund
See if AI Strategy & Consulting is the right move for your team.
Request a free quoteBuild, manage, run
We Build the AI Roadmap, Then Run It
Some engagements stop at a slide deck; ours continues into implementation. Our senior team works inside your operation to design the AI strategy, then we build the systems and run them in production. We map where Claude (a preferred model inside Salesforce Agentforce, with deeper integration still rolling out through 2026), OpenAI GPT, and open models like Llama, Qwen, and Mistral each fit your stack, and we own the rollout from first pilot to live deployment.
We run quarterly roadmap reviews against live production metrics, so the strategy keeps moving with your revenue, not the slide deck.
- We audit your data, tools, and workflows, then design a model and architecture plan you can actually ship
- We choose the right mix of models and platforms, Claude, GPT, open models, and Salesforce Agentforce, per use case instead of forcing one vendor
- We stand up governance, security, and cost controls so AI scales without surprises
- We stay on as the team that builds and operates the roadmap, not a consultant who leaves after the deck
See it in action
Every AI idea, scored against your P&L.
AI OPPORTUNITY ROADMAPYour Brand · v1.2 · CFO-reviewed
Invoice matching, Finance3 FTE-days/wk manual · ≈$84k/yr back
BUILD · Q3Support reply drafting, CX≈$31k/yr · blocked on CRM cleanup
PILOT · Q4Homepage chatbotmoves no P&L line we can find
SKIPFine-tuned custom modeloff-the-shelf covers 9 of 10 uses
SKIP14 workflows scored · 6 on roadmap · 8 parkedQ3–Q4 sequence
Illustrative example, styled to show the kind of output we deliver.
Selected work
Representative engagements.
Turning AI hype into a funded plan and a working first use case.
Wanted answers grounded in their own docs, not hallucinations.
What we did
- Built a RAG system over their knowledge base
- Added citations + an evaluation harness
- Access controls + monitoring
Result Deflected common tickets with sourced answers the team could trust.
Examples are anonymized to honor client NDAs and edited to illustrate typical scope, outcomes vary by market, budget, and starting point.
How & why it works
Start from the P&L, not the tool.
Results come from picking the right workflows before touching a model: we quantify where a task's time, error rate, or volume ties to a real number on your P&L, then build only where the math clears a threshold and kill the rest.
- Map workflows to dollarsWe inventory the workflows in scope and attach a baseline to each, volume, cycle time, error/rework rate, and fully-loaded labor cost, so "help with support" becomes a measurable number like tickets/month x minutes x cost, not a vague ambition.
- Score value vs. feasibilityEach candidate goes on a value-vs-feasibility matrix scored on data readiness (is the data clean, accessible, permissioned?), integration effort, regulatory exposure, and expected dollar impact. Low-value or data-starved ideas get an explicit "don't build" so budget concentrates on the few that clear the bar.
- Decide build vs. buy vs. skipFor each surviving use case we choose the cheapest path that hits the metric, an off-the-shelf tool, a retrieval-augmented (RAG) layer over your own docs, a fine-tune, or a workflow fix with no AI at all, and specify where a human stays in the loop for approval on anything customer-facing or high-risk.
- Sequence a shippable roadmapWe order the work so the lowest-risk, fastest-payback pilot ships first (usually one with clean existing data), and dependencies like data cleanup or system access are surfaced up front. Each phase gets an owner, a target metric, and a go/no-go gate before spend scales.
- Set the measurement baselineBefore anything launches we lock the pre-AI baseline and the metric each use case must move, so the pilot's result is a real delta against yesterday's numbers, not a demo, and a use case that misses gets cut instead of quietly expanded.
Worked exampleA mid-market B2B distributor with a 12-person support team and a growing quote backlog wanted an "AI strategy" but no clear target.
- Mapped six revenue- and cost-heavy workflows, then scored each on annual dollar volume, error/rework rate, and data readiness; quoting turnaround and support-ticket triage ranked highest, and an "AI SDR" idea was cut for thin ROI
- Sized the top two against a P&L baseline (avg. quote took ~2 days; ~40% of tickets were repeat questions already answered in docs) and set target metrics before any build
- Sequenced a 90-day plan: a retrieval-assisted ticket-drafting pilot first (existing knowledge base, low integration risk), quoting automation second (needs ERP data cleanup)
- Pilot cut first-response drafting time by roughly 30% on repeat tickets, with a human approving every send, measured against the pre-pilot baseline
Why it works
AI projects stall because they start from a capability ("we should use AI") instead of a constraint on the P&L, so effort scatters across demos that never move a number. Anchoring every candidate to a baseline dollar figure and a feasibility score forces a small portfolio of high-conviction bets and an explicit stop-list, which is why a sequenced roadmap ships and compounds while a tool-first initiative burns budget on pilots that can't prove their worth.
FAQ
Questions, answered.
You get a prioritized roadmap of AI use cases scored on business value, feasibility, and cost, plus a clear build-vs-buy call for each one and a sequenced rollout plan with rough budgets. We also flag the use cases to ignore, since saying no to low-ROI ideas is half the value. The deliverable is decision-ready, not a slide deck that sits on a shelf, so your team can start executing the week it lands.
A focused roadmap typically runs three to six weeks depending on the number of departments and systems involved. We start with stakeholder interviews and a review of your data, tools, and existing workflows, then map and score candidate use cases, and finish with a prioritized plan and ROI model. For example, a mid-market team might come out with a 12-use-case backlog where the top three (a support triage agent, a sales research assistant, and an internal knowledge search) are scoped to ship first.
Every use case is scored against real business impact, data readiness, integration effort, and ongoing run cost, then ranked so the high-value, low-friction work surfaces first. We are model-agnostic across Claude, GPT, and open models like Llama and Mistral, so the recommendation is driven by your problem and not by a vendor relationship. For example, we will often steer a client away from a flashy custom model toward a simpler retrieval setup or an off-the-shelf tool when the math does not justify the build.
We do both. The strategy stands on its own, but NYFTY is a team that builds, manages, and runs the work, so we can carry the priority use cases straight into implementation using tools, frameworks, and platforms like LangGraph, CrewAI, n8n, and Salesforce Agentforce. You are never handed a plan with no one to execute it, and you are free to take the roadmap to your own team or another vendor if you prefer.
No. Data and team readiness are part of what we assess, and the roadmap accounts for your actual starting point rather than an ideal one. If your data is messy or siloed, that becomes a sequenced prerequisite with its own effort estimate, and we will identify quick-win use cases that can ship on the data you already have while the bigger foundations get built. The goal is momentum and ROI early, not a year of cleanup before anything ships.
Let’s make it measurable.