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AI & Data

AI & Data Strategy

Build a strategy for turning AI ambition and data investment into measurable business outcomes.

You have exciting ideas for using AI. But you may be unsure how to translate them into real business value. We help you shape your strategy, pairing AI ambition with a data foundation that puts you on the path to better outcomes.
AI & Data Strategy (1)

AI ambition is outpacing AI readiness.

You’re no longer being asked whether to invest in AI. Now people want to know where to invest first and, more importantly, how you can ensure the investment pays off.

That’s the value of a clear AI and data strategy. It identifies the use cases that will deliver real impact. It articulates the required data foundation. And it outlines a sequence for your efforts, so each step compounds the value of the last.

 

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The CBTS approach

Strategy that meets you where you are

No two organizations come to AI and data strategy from the same place. Some are focused on data and the need to prepare it for use by AI and agentic solutions. Others are focused on a specific business problem AI could solve and need help shaping the solution. CBTS always meets you where you are, with AI and data strategy work tailored to your challenges and needs.

 

AI & Data Strategy capabilities

 CBTS AI & Data Strategy engagements are organized around two parallel disciplines. Most
clients need both, but we shape every engagement to the entry point that makes the most
sense for your business.
 

AI Strategy

AI Strategy

AI Maturity Assessment


A diagnostic of where your organization stands on AI readiness across data, infrastructure, talent, governance, and use case pipeline. Establishes the baseline that every other strategic decision builds on.

AI Strategy

AI Strategy

Use Case Prioritization


A structured process for sorting the long list of AI possibilities into the short list of high-impact, fundable use cases tied directly to revenue, cost, customer experience, and/or risk outcomes.

AI Strategy

AI Strategy

AI Roadmap


A phased plan that sequences the prioritized use cases against your data foundation, infrastructure, and operating model. Built to secure executive funding and to give your teams a clear path from pilot to production.

Data Strategy

Data Strategy

Data Maturity Assessment


A diagnostic of your current data landscape, including quality, accessibility, governance, ownership, and integration, measured against the demands of the use cases you want to support.

Data Strategy

Data Strategy

Data Strategy Development


A clear point of view on how your organization should treat data as a strategic asset: the platforms that hold it, the principles that govern it, and the operating model that keeps it trusted and usable.

Data Strategy

Data Strategy

Data Roadmap


A phased plan that sequences the data work — engineering, governance, platform modernization, and integration — against the AI and analytics outcomes the business is asking for.

Where to start

Advisory engagements

A CBTS advisory is a time-bound, fixed-fee engagement designed to give you a clear answer to a specific strategic question — fast.  

Data Readiness Advisory

Best for: Organizations with active or planned AI, analytics, or cloud data platform initiatives where the data foundation has not been validated. Also a fit for organizations whose prior AI programs have stalled, are facing regulatory audit pressure, or are navigating M&A integration where data ownership, quality, and governance gaps are slowing execution.

What this engagement produces:

  • Current state data landscape map
  • An AI readiness scorecard mapped against specific use case priorities
  • A governance and compliance gap analysis
  • A data security posture assessment
  • A quantified cost of current state analysis
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What success looks like

Three outcomes show up most consistently in the AI & Data Strategy engagements we lead.

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Revenue growth

Strategy work prioritizes the AI use cases tied directly to top-line drivers: new products, new revenue streams, and customer experience improvements.  

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Cost optimization

 Fewer stalled initiatives. Less duplicated investment across teams. The right strategy helps ensure you spend smart and realize tangible return on your investments.

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Reduced risk

  Strategy work surfaces the regulatory, security, model, and organizational risks that pilot teams rarely have the authority to address on their own. 

 “The hardest part of any AI program isn’t the model or the platform. It’s the strategy underneath. That’s the work we do first. If you don’t get clear on which use cases will move the business and what data foundation has to be in place to support them, you’ll waste time building things that never scale.” 

Mark Giles

 Mark Giles   

Vice President, AI 

Don’t take our word for it

“I love the creative, tailored solutions that are delivered in a consistent and reliable way while always doing what it takes to make things right.”

Chief Technology and Information Security OfficerFinancial Services / Banking

“My team at CBTS have been trusted partners for a long time. They provide excellent technical support and pre-sales work. Their breadth of knowledge and ability to bring in the right resources have helped us steer our technology into the future.”

Managing Director, CISO, Head of TechnologyPrivate Equity / Financial Services

“CBTS treats us like a partner and not just a customer. The technical expertise is next to none and the relationship management is some of the best I have experienced.”

Director, Telecom and Architecture ServicesHealthcare

Once strategy is set, we operationalize.

AI and data strategy is the starting point. The work it sets in motion runs across four operational disciplines, each with a deep bench of practitioners. 

Related insights 

Frequently asked questions 

What is AI strategy? AI strategy is the set of decisions that determines where your organization will invest in artificial intelligence, in what order, and with what expected business outcomes. A strong AI strategy answers three questions clearly: Which use cases will move the business? What foundation must be in place to support them? How will the work be sequenced so each phase compounds the value of the last? Without that clarity, AI investment tends to fragment across competing pilots that rarely scale. 
What is data strategy? Data strategy is the set of decisions that determines how your organization treats data as a strategic asset — the platforms that hold it, the principles that govern it, and the operating model that keeps it trusted and usable. A strong data strategy aligns data investment with the business outcomes the data is meant to support, including AI, analytics, and operational decision-making. It typically covers data architecture, governance, ownership, integration, and the talent model required to sustain all four. 
What’s the difference between AI strategy and data strategy? AI strategy answers “where should we invest in AI, and what will it deliver?” Data strategy answers “what foundation do we need to make any of this work in the real world?” The two are deeply linked: AI strategy shapes the data foundation you need, and the data foundation determines which AI ambitions are realistic in what timeframe. Most CBTS engagements address both in a single roadmap; trying to set one without the other tends to produce plans that look complete on paper but break down in execution. 
Do we need a data strategy before we can do AI? Not necessarily — but you do need to address them together. Some organizations have enough data foundation in place to pursue specific, contained AI use cases while their broader data strategy is still being shaped. Others have such significant data gaps that AI investment will stall until those gaps are closed. The right answer depends on your data maturity, the use cases you’re targeting, and your timeline. An AI and data maturity assessment is the most direct way to know where your organization stands. 
Who should be involved from our side in an AI and data strategy engagement? At a minimum, you need an executive sponsor (typically a CIO, CDO, CTO, or business unit leader), a data leader, and one or two business stakeholders representing the functions where AI investment is being considered. For the use case prioritization phase specifically, we recommend including representatives from the business functions where AI value will most likely land (e.g., finance, operations, customer experience, sales, and/or marketing) depending on your priorities. CBTS handles the structure and facilitation; your team brings the institutional knowledge that makes the strategy specific to your business.

Strong foundations start with strategy.

The hardest part of an AI and data program isn’t the technology. It’s knowing where to start, what to build first, and how to make the case for the investment. CBTS helps you answer those questions in weeks, not quarters — with a defined deliverable, a fixed scope, and a team that’s done this work across industries.