AI Foundations for SMBs

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    Company size

    Enterprise: consolidate multi-BU stack sprawl into one data architecture

    Who this is for

    Organizations above roughly 1,000 employees and $100M in revenue—where several business units each run their own operations, and often their own near-identical version of the same platform. Usually a RevOps, IT, or transformation leader who needs a trusted advisor to navigate AI and stack decisions across departments rather than inside one.

    What you'll walk away with

    A cross-BU view of what duplicates what, an integration map showing what's native versus custom API versus not connectable, and a pilot scoped small enough to prove the case and structured to survive procurement.

    Book a Deep Dive

    Place yourself first

    Count how many of these describe your team today. The read underneath tells you where to start, so you don't spend the quarter fixing the wrong layer.

    • Each business unit runs its own ERP or CRM instance—sometimes four variants of the same capability, chosen on interface preference rather than requirement.
    • One BU is on Salesforce and another on HubSpot because a leader brought their preference with them, and neither can see the other's pipeline.
    • There is no single unified understanding of the customer across business units.
    • Cross-sell opportunities go unworked because the data proving them lives in a silo.
    • The same analysis gets produced twice by two teams who don't know about each other.
    • Nobody can say what the combined stack costs across all units.
    • Teams know they need AI and can speak fluently about it, but can't say which processes should get it first.

    Early

    3+ symptoms: the problem is architectural, not tooling. Start with a hidden-tax audit and an integration map before any consolidation decision.

    Building

    2 symptoms: you know where the duplication is but not what it costs to unwind. Scope a pilot against a single BU pair.

    Optimizing

    0-1 symptoms: you have a unified view. Move to sequencing AI against the processes with the cleanest data.

    What's actually going wrong

    Every business unit bought the same capability differently

    Four business units, four ERP or CRM variants, each defended by the operations team that chose it. The differences are usually interface preference, not requirement.

    What it costs
    You pay four times for one capability, then pay again in integration work, admin overhead, and the reporting layer built to reconcile them.
    What fixing it looks like
    A hidden-tax audit across units to quantify duplicate spend, then a capability-by-capability consolidation case with the migration effort attached to each.

    Siloed data blocks cross-sell and distorts strategy

    One unit's customer data is inaccessible to the others. Teams can't see how a neighbouring business operates, so cross-sell motions never get built and account overlap goes unnoticed.

    What it costs
    A genuine macroeconomic problem inside one company: decisions get made on partial data, and revenue that exists across the customer base is never worked.
    What fixing it looks like
    Map what can be integrated natively, what needs custom API work, and what can't be connected at all—then build the unified customer view against the layers that can carry it.

    Duplicate work across units nobody can see

    Two teams build the same dashboard, run the same enrichment, or negotiate with the same vendor independently.

    What it costs
    Salary spend on parallel effort, plus divergent numbers that undermine trust in reporting when they surface in the same meeting.
    What fixing it looks like
    A shared definition layer and a cross-BU inventory of what already exists before any new build is approved.

    AI ambition without a data foundation

    Leadership is bought in and fluent, but the underlying data is fragmented across systems with competing definitions.

    What it costs
    Pilots that produce impressive demos and no production outcome, because the model is reasoning over data the business doesn't trust.
    What fixing it looks like
    Sequence AI against processes where the data is already clean, and treat the rest as a data-architecture project first.

    Where multi-BU duplication usually sits

    LayerWhat teams usually runWhere the gap is
    CRMSalesforce in the core business, HubSpot in a unit whose leader preferred itNo shared account view; pipeline can't be rolled up without manual reconciliation
    ERPNear-identical platforms per business unit, chosen on interface preferenceFour contracts, four admin teams, one capability
    Data integrationPoint-to-point connections built as each need aroseNo map of what's native, what's custom API, and what can't connect at all
    Analytics and reportingPer-unit BI stacks with per-unit metric definitionsThe same metric means different things in two rooms
    Enrichment and data qualityDifferent providers per unit, evaluated separatelyCoverage and match rates never compared against the same sample set

    Pricing and features change constantly—always confirm current details on the vendor's own site before you buy.

    The first 90 days, with named deliverables

    1. 1

      Days 1-30—baseline

      • Hidden-tax audit across business units: what's duplicated and what it costs
      • Integration map—native, custom API, or not connectable—per system
      • A shortlist of pilot candidates scoped to the smallest credible sample
    2. 2

      Days 31-60—pilot

      • Small-sample pilot run against real data with several scenarios, not one happy path
      • Procurement, security, and compliance engaged from the start of the pilot, not at the end
      • Measured results against the incumbent, not against the vendor's benchmark
    3. 3

      Days 61-90—sequence the consolidation

      • Consolidation plan by capability with migration effort and owner per item
      • Unified customer-view design across the units that can carry it
      • Business case with run-rate savings and the cross-sell motions the data unlocks

    Teams we've done this with

    T-Mobile

    IT

    T-Mobile is one of the largest wireless carriers in the United States, serving consumers and businesses with nationwide 5G connectivity and digital services.

    Semrush

    SEO

    Semrush is a leading Search Engine Marketing (SEM) and online visibility management SaaS platform used by 10,000,000+ digital marketers worldwide. The company supports marketers, agencies, and enterprise teams across every major industry.

    Viasat

    IT

    Viasat delivers high-speed, high-quality internet and secure communications to consumers, enterprises, and governments worldwide.

    Questions operators ask us

    How do you consolidate a bloated GTM tech stack to reduce SaaS costs?

    Start with a capability inventory across business units rather than a vendor list—duplication hides behind different product names doing the same job. Quantify the duplicate spend, then attach a migration effort and an owner to each consolidation candidate. Sequence by lowest disruption per dollar saved, and run each change through procurement inside the contract's notice window so the saving actually lands.

    What is the ROI of hiring a fractional GTM operations consultant versus full-time hires?

    A fractional operator is right when the work is architectural and finite: audit, map, design, and hand over. A full-time hire is right when the work is continuous operation. The failure mode is hiring full-time into a problem nobody has scoped yet. You get a capable person maintaining an architecture nobody designed.

    How do you audit and clean CRM data for enterprise-scale ABM?

    Baseline first: duplicate rate, match rate against a known sample, field completeness on the fields routing actually uses, and how many accounts appear in more than one business unit's instance. Clean against a single account definition, then fix the intake so it stops re-polluting. ABM segmentation built on unreconciled account data targets the same company three times.

    How do you integrate Salesforce, HubSpot, and product-led growth data?

    Decide which system is the account system of record before touching a connector—most multi-BU integration projects stall because that decision was avoided. Then map each connection as native, custom API, or not connectable, and build the unified view on the layers that can actually carry the volume and latency you need.

    What does a best-in-class enterprise RevOps tech stack look like?

    One account system of record, one metric definition layer, integrations that are documented rather than discovered, and enrichment evaluated against your own sample rather than the vendor's. The tools matter less than whether every business unit reads the same number the same way.

    How do you migrate a marketing automation platform without disrupting sales pipelines?

    Run in parallel through at least one full cycle, freeze net-new automation in the outgoing system, and rebuild logic rather than exporting it—branching rules and scoring don't migrate cleanly. Keep routing on the incumbent until the new system has matched it on live traffic.

    What are the top criteria for evaluating enterprise GTM strategy consultants?

    Whether they've operated inside a company at your scale, whether they'll pilot before recommending, and whether they can survive your security and procurement review. Ask what they'd measure in the first 30 days—an operator answers with a baseline, a strategist answers with a framework.

    How do you scale lead-to-revenue routing across global sales teams?

    Route on a single account definition, not on lead-level attributes that differ per region. Then make ownership rules explicit and auditable—most routing failures at global scale are ownership conflicts between units, not technical failures.

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