AI Business Case & Strategy Development

How DayBlink Consulting helped a telecommunications leader build the investment case for AI — distilling hundreds of ideas into a prioritized, value-backed roadmap

Introduction

A major North American telecommunications provider set out to define its enterprise AI strategy and secure leadership investment in its future AI footprint. The organization had no shortage of ambition — hundreds of AI ideas had surfaced from across business units and internal innovation challenges — but it lacked a structured, value-driven case to bring to its Executive Leadership Team. DayBlink Consulting was engaged by the client’s Data & AI leader, who had been charged with proposing the enterprise AI strategy, to build that case. Rather than chase technology for its own sake, DayBlink helped the client apply a disciplined value lens to a sprawling idea backlog, develop rigorous business cases for the highest-potential use cases, and sequence them into a roadmap. Critically, the work reframed individual AI use cases as the vehicle for funding a foundational, reusable data capability — turning near-term wins into a platform for long-term AI maturity.

Problem

Our client had abundant AI ambition but no structured case to justify enterprise investment

The organization had generated an enormous volume of AI ideas — more than 200 from internal teams, and several hundred more through crowdsourced innovation challenges — but had no consistent way to evaluate which ones were worth pursuing. With enterprise budgets largely set and locked, leadership needed an eyes-wide-open, value-driven basis for deciding where, and whether, to invest. At the same time, the data required to power these use cases was fragmented across the enterprise, undermining any attempt to scale AI reliably.

During discovery, DayBlink worked with the client to surface five core challenges:

1. An Unmanageable Idea Backlog: Hundreds of AI ideas had accumulated across business units and innovation challenges with no shared framework to prioritize them by value or feasibility.
2. No Value Discipline: Many ideas promised productivity or convenience but not measurable financial return; leadership needed to separate genuine revenue and cost-savings opportunities from “nice-to-haves.”
3. Fragmented Data Foundations: Critical data was trapped in disparate systems and duplicated across regions, leaving it inaccessible to the teams and AI solutions that needed it and blocking scalability and extensibility.
4. Risk of Point Solutions: Without a deliberate architecture, intersecting initiatives risked building redundant data infrastructure and one-off solutions rather than shared, reusable capability.
5. No Investment-Grade Case: Leadership lacked the standardized, ELT-ready business cases — with quantified benefits, data-readiness assessments, and high-level target architecture — needed to make a confident go/no-go funding decision.

Solution

DayBlink built a disciplined, value-first business case and a sequenced roadmap to mature the client’s AI footprint

DayBlink partnered with the client’s Data & AI leader to convert a broad universe of AI ambition into an investment-grade case for leadership. Working from a backlog of 200-plus internal ideas and several hundred crowdsourced concepts, the team applied a ruthless value lens — prioritizing use cases that could generate new revenue or remove real cost — to distill the field down to a focused set of priority use cases.

Use-case-level business cases. Rather than a single monolithic proposal, DayBlink developed business cases incrementally, one per use case, so that the most ready opportunities could advance to the ELT as soon as they were proven. Each case was built on a standardized template and consistent metrics, ensuring use cases could be evaluated on a like-for-like basis and easily socialized with leadership. Every case paired quantified benefits with a high-level data-readiness assessment and target architecture — capturing key considerations, risks, and indicative costs to give executives the right investment lens without over-engineering the design.

Identifying common data assets. One of the most valuable outputs of the scoping exercise was identifying the data elements shared across multiple use cases. Rather than treating each use case as an isolated build, DayBlink mapped the common datasets — network performance data, customer data, location data, and others — that appeared as dependencies across the priority portfolio. This revealed where a single data investment would unlock several use cases simultaneously, sharpened the ROI case for foundational data work, and gave the organization a clear view of which data capabilities to build first.

Sequencing the foundation. The defining insight of the engagement was structural: the client had a wealth of data but little of it organized for AI at scale. DayBlink framed early, lower-complexity use cases as the means to fund and build a unified data foundation — turning near-term wins into the platform that would unlock a far larger pipeline of future use cases. This shaped a sequenced roadmap that ordered initiatives by both value and feasibility rather than treating them as a flat wish list.

Engaging stakeholders top-down and bottom-up. To ensure the case would hold under executive scrutiny, DayBlink drove alignment from both directions — supporting executive-sponsored communication into the ELT while engaging business-unit leaders and P&L owners directly to validate and sign off on projected benefits. This dual engagement closed the gap between technical solution and business buy-in before the case ever reached the boardroom.

Outcome

DayBlink delivered the client an investment-ready case and a clear roadmap for advancing its AI strategy

DayBlink converted a fragmented, several-hundred-item idea backlog into a focused, prioritized portfolio of investment-grade AI business cases, giving leadership — for the first time — a consistent, value-driven basis on which to make a confident go/no-go decision. Each priority use case was packaged with quantified benefits, a data-readiness assessment, and a high-level target architecture, enabling like-for-like comparison and rapid socialization with the ELT.

A particularly significant output was a clear map of the data assets shared across the priority use case portfolio. By identifying which datasets multiple use cases had in common, the client could see exactly where foundational data investments would deliver outsized returns — funding shared capability rather than siloed point solutions.

Beyond the individual cases, the engagement established a durable strategic thesis: that the value captured from near-term AI use cases can fund the unified data foundation the enterprise needs to scale AI reliably. This reframing turned a scattered set of ideas into a sequenced roadmap that positions the organization to make eyes-wide-open investment decisions rather than leave value on the table — and to build toward an AI capability that compounds over time.

Making the Case for AI Investment

We believe the path to enterprise AI maturity runs through disciplined value, not technology for its own sake. The organizations that win are those that distill ambition into a prioritized, investment-grade case — and recognize that the real prize is the shared data foundation beneath the use cases. DayBlink Consulting’s expertise in strategy, business-case development, and data and AI helps clients turn a backlog of ideas into a clear, fundable roadmap for the future.