One dashboard to replace the BI sprawl. $100K out of run-cost. Sub-account margin, finally visible.
How a growth-stage Payment Service Provider collapsed a fragmented BI stack into a single source of truth — eliminating $100K in annual tool spend and surfacing the true platform economics of every sub-account it processes.
- Problem
- Growth-stage PSP running five overlapping BI tools with no consolidated view of sub-account margin.
- Approach
- Embedded as the AI operating partner — designed and deployed a vendor-neutral single source of truth, then rationalized the BI stack on a planned schedule.
- Outcome
- $100K in annual tool spend eliminated, real-time sub-account margin visibility, ~30+ analyst hours reclaimed each month.
How SideB helped a growth-stage PSP eliminate $100K of annual BI tool spend with a single source of truth.
A growth-stage Payment Service Provider had outgrown a fragmented Business Intelligence stack — multiple vendors, overlapping pipelines, and no consolidated view of true platform cost per sub-account. sideb.io was engaged under AIOps Enablement as the AI operating partner: senior expertise embedded into the team to design and deploy a centralized dashboard.
The outcome was both hard-dollar and operational: $100K eliminated in annual BI tool spend, sub-account-level margin visibility for the first time, and a step-change in how fast the leadership team could act on platform economics.
A messy BI stack hiding the answer to “which sub-accounts are actually profitable?”
- Fragmented BI tooling. Multiple overlapping BI platforms, dashboarding tools, and data-warehouse adjuncts — each licensed separately, each maintained by a different team, none telling a consistent story.
- No sub-account margin visibility. PSP economics live or die on the cost-to-serve of every sub-account — interchange, scheme fees, chargeback risk, support load. Those numbers existed across five systems and didn't reconcile.
- Redundant tool spend. Multiple vendors solving overlapping problems — paid for in full on annual contracts, with no clear owner to rationalize.
- Lagging decisions. Margin reviews ran on a monthly cadence at best — meaning by the time an unprofitable sub-account was flagged, another full cycle of losses had already booked.
- Vendor lock-in risk. The internal instinct was to consolidate by buying another all-in-one BI suite — re-creating the lock-in problem one layer up.
sideb.io embedded as the AI operating partner — designed and deployed a vendor-neutral single source of truth.
AIOps Enablement plugged senior AI capability into the org without the cost or lock-in of a permanent hire. Decisions stayed with the client; sideb.io owned design, build velocity, and the rollout playbook.
- 01 BI stack & policy review. Audited every active BI license, pipeline, and dashboard. Mapped overlap, identified the contracts driving the most spend without proportional value, and produced a rationalization plan.
- 02 Single-source-of-truth design. Defined a vendor-neutral data model anchored on sub-account as the primary unit of economics — interchange, scheme fees, chargebacks, support cost, settlement timing, FX exposure.
- 03 Custom margin dashboard. Deployed a centralized dashboard exposing true cost-to-serve per sub-account — replacing five tools the team used to flip between, surfaced in real-time instead of monthly.
- 04 Adoption & ROI playbook. Enabled finance, product, and commercial teams on the new view, with rituals to act on it weekly — pricing, off-boarding, capacity, and risk decisions all anchored on the same numbers.
- 05 Tool consolidation & exit. Decommissioned redundant BI vendors on a planned schedule — locking in $100K of annual run-cost savings without disrupting reporting continuity.
The $100K is the easy number. The compounding value is bigger.
Beyond the headline savings, three structural improvements re-shaped how the business runs.
~30+ hours reclaimed per month across finance & product
Manual reconciliation across five BI surfaces was a standing tax on the most senior analysts in the room. Conservatively: 2 finance analysts × 8 hrs/wk + 1 product lead × 4 hrs/wk = ~80 hrs/month on reconciliation alone. The single source of truth cut that to under 10 hrs/month for spot-checks. At a blended fully-loaded rate of ~$95/hr, that's roughly $80K of recovered analyst capacity per year — redeployed into pricing strategy and risk work that actually moves margin.
Mispriced sub-accounts surfaced and corrected — six figures of margin recovered
Once true cost-to-serve was visible per sub-account, the commercial team identified a meaningful share of accounts pricing below cost on a fully-loaded basis. Renegotiating, re-tiering, or off-boarding the structurally unprofitable tail produced an estimated $120K–$180K of margin uplift annually from full visibility — recurring, not one-off.
From lagging monthly reports to real-time agentic-commerce insight
Margin reviews shifted from a backwards-looking monthly ritual to a real-time operating loop. Pricing, risk, and capacity decisions now happen within the same week a signal appears — not 30–60 days later. That speed compounds: every cycle saved is one less full period of unprofitable volume booked, and one earlier window to capture upside on accounts trending the right way.
~10–12x first-year return on a single AIOps Enablement engagement.
$300K–$360K of combined first-year value against a ~$30K engagement — a conservative 10–12x return in year one, before factoring in the compounding decision-speed benefit. The dashboard, the data model, and the rituals stay with the client.
How we measured this
If your BI stack is sprawling and the answer to “which line of business is actually profitable?” lives across five tools — that's the engagement.
AIOps Enablement plugs senior AI capability into your team as a retainer — no headcount commitment, no vendor lock-in, decisions stay with you.
Frequently asked questions
What is an AI Operating Partner and how is it different from hiring a VP of AI?
An AI Operating Partner is a senior operator who plugs into your business to scope, enable, and optimize AI inside revenue, finance, or operations — without the cost, lock-in, or hiring risk of a permanent VP. SideB runs 4–6 week sprints (longer for complex rollouts) covering AI-stack review, model and tooling selection on enterprise tiers with contractually zero-training terms, playbook authoring, change-management, and an ROI dashboard the leadership team can read.
What cost savings did AI rationalization unlock in this case study?
Collapsing five overlapping BI tools into a single AI-driven source of truth eliminated $100K in annual tool spend and reclaimed 30+ analyst hours per month — without losing any of the reporting capability the team relied on. Specific savings on any engagement depend on the size of the existing tool stack and the depth of redundancy.
Will SideB recommend specific AI vendors or stay neutral?
Vendor-neutral by default. We surface a shortlist that fits your scope, data-residency, and risk posture — then run a structured selection with the client. Where the engagement scope includes deeper sourcing (e.g., enterprise LLM API contracts), we co-pilot the negotiation. We're never on a vendor's payroll.
How do you handle data privacy and model training concerns?
Every model and tool we recommend operates on enterprise tiers with contractually zero-training terms — meaning your data is never used to retrain the vendor's models. We also build the policy layer (data classification, prompt hygiene, redaction rules) so AI is deployed safely from day one, not patched in retroactively.
How is ROI from an AI Operating Partner engagement measured?
Every engagement ships with an ROI dashboard the leadership team can read — covering tool-spend eliminated, analyst time reclaimed, decisions accelerated, and any workflow-level KPIs scoped at activation. The dashboard makes the trajectory measurable rather than anecdotal. Pace and magnitude of ROI vary by scope, starting baseline, and the client team's pace of adoption.