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Buy-and-Build Strategy: A Finance Function Built to Scale

Buy-and-Build Strategy: Building a Finance Operating Model That Scales

July 30, 2026/in Insights/by Amy Chamberlain

Most buy-and-build strategy conversations focus on deal flow: sourcing, valuation discipline, integration playbooks for sales and ops. The finance function usually isn’t part of that conversation at all, until it’s the thing slowing every subsequent deal down. 

A buy-and-build strategy is, at its core, a bet that a platform can absorb acquisition after acquisition without losing operational control. Whether that bet pays off usually comes down to something unglamorous: whether the finance function was built to scale, or built for the business as it existed before any of this started. 

Why finance is the bottleneck in most buy-and-build strategies 

Sourcing and closing deals tends to get faster with practice. Finance integration usually gets slower, because every new acquisition adds another chart of accounts, another set of definitions, and another system that has to be reconciled into the whole. 

By the third or fourth add-on, firms without a finance operating model built to scale spend more time reconciling historical numbers than evaluating the next opportunity. Deal-making keeps working. The reporting behind it doesn’t, and that gap eventually slows deal-making too. 

The problem is largely invisible until it isn’t. Everything can look fine through the first 2 acquisitions: close happens on time, the board package goes out, nobody’s complaining. The strain doesn’t show up as a crisis. It shows up as hours, quietly piling up on the finance team’s workload, until those hours aren’t there to give anymore. 

What a finance operating model built to scale actually requires 

A buy-and-build strategy finance function built to last has a handful of things in common: 

  • A chart of accounts with enough structure to absorb a new entity without a full redesign each time 
  • Standardized definitions for revenue, margin, and key metrics that hold across every company in the portfolio, not just the original platform 
  • A consolidation process that doesn’t depend on one person manually reconciling spreadsheets at month-end 
  • Systems flexible enough to onboard a new entity in weeks, not months 
  • A reporting cadence and format that scales with headcount and portfolio complexity, rather than requiring a redesign at every milestone 

This doesn’t take enterprise software on day 1. It takes deciding early which parts of the finance function are foundational, and building those with the fourth or fifth acquisition in mind, not just the first. 

A polished BI dashboard doesn’t matter much if the chart of accounts underneath it can’t absorb a new entity without weeks of remapping. Get the structural layer right first: chart of accounts, definitions, consolidation logic. The reporting layer on top of it becomes much easier to build and rebuild as the portfolio grows. 

The maturity curve: from first add-on to fifth 

Most finance functions supporting a buy-and-build strategy move through a predictable maturity curve, whether or not anyone plans for it. After the first acquisition, reporting is usually manageable: 2 sets of numbers, reconciled by hand if necessary, without much strain. 

By the second or third acquisition, the manual approach starts to break down. Reconciliation takes longer, definitions start to drift between entities, and the person holding it all together becomes a single point of failure. 

By the fourth or fifth, firms without a deliberately built operating model are usually either investing heavily in a rebuild under pressure, or quietly accepting slower, less reliable reporting as the cost of growth. Firms that invested in the operating model earlier skip that inflection point. Their fifth acquisition integrates roughly as smoothly as their second. 

The investment has to happen before it’s obviously necessary, which is the uncomfortable part. Nobody feels urgency to rebuild the chart of accounts after the first acquisition, when everything still feels manageable. By the time the need is obvious, the rebuild is happening under much worse conditions: mid-close, with a board expecting answers, instead of on a calm timeline chosen in advance. 

Common pitfalls when scaling finance through acquisition 

A few mistakes tend to recur across buy-and-build finance functions, regardless of industry: 

  • Treating each acquisition’s finance integration as a standalone project instead of a repeatable, documented process 
  • Letting the original platform’s chart of accounts stay the default, rather than redesigning it once with future entities in mind 
  • Underinvesting in documentation, so institutional knowledge lives in 1 or 2 people instead of a process anyone could follow 
  • Adding reporting complexity reactively, in response to whatever the board asks for that quarter, instead of designing reporting that anticipates future needs 

Most of these share a root cause: reacting to each acquisition individually instead of building a finance function designed for the strategy as a whole, deal after deal. A one-off fix might get the current close finished on time, but it rarely holds up once a fifth or sixth entity gets added to the mix. 

Signs your finance function isn’t built to scale 

A few warning signs tend to show up before the breaking point: 

  • Month-end close takes noticeably longer with each new acquisition added to the portfolio 
  • Different portfolio companies use different definitions for the same metric, and nobody’s reconciled the difference 
  • One person is the only one who understands how the consolidated numbers actually get built 
  • Every new acquisition requires a custom reporting build instead of plugging into an existing framework 
  • Board packages take days to assemble instead of being a byproduct of the existing reporting process 

A realistic scenario 

Consider a platform company on its fourth acquisition in 3 years. The first 3 integrations went reasonably well. Each one added complexity, but the finance team absorbed it by working longer hours and building one-off reconciliation processes as needed. 

By the fourth acquisition, that approach stopped working. The finance team was spending more time each month reconciling 5 different definitions of gross margin than they were spending on forward-looking analysis. The board package that used to take 3 days to assemble was now taking 2 weeks. 

The fix wasn’t more headcount. It was going back and building the operating model that should have been designed after the first acquisition: a standardized chart of accounts, consistent definitions across every entity, and a consolidation process that didn’t depend on manual reconciliation. The rebuild took a quarter. Without it, every future acquisition would have made the problem measurably worse. 

How to evaluate readiness before the next deal 

Before adding another acquisition to a buy-and-build strategy, it’s worth asking a few honest questions about the finance function itself. Could this consolidation process absorb one more entity without a manual rebuild? Would 2 people pulling the same report land on the same number? Does the reporting cadence still work, or has it quietly become someone’s full-time job to hold together? 

If the honest answer to any of those is no, address it before the next deal closes, not after. A finance operating model that’s already straining under the current portfolio only strains further with each addition. 

Building the operating model before you need it 

Most of what a finance operating model built to scale requires is well understood and not particularly novel: a well-designed chart of accounts, documented processes, consistent definitions. The hard part is prioritizing it before the pain of not having it is obvious. 

That usually means making the case internally that finance infrastructure deserves the same upfront investment as a sales integration playbook or an ops due diligence process, even though the payoff is less visible in the short term. Nobody points to a well-designed chart of accounts as the reason a deal went smoothly. Plenty of firms can point to a poorly designed one as the reason their fourth acquisition took twice as long to integrate as their first. 

Firms that get ahead of this tend to build the operating model in a deliberate window, often between acquisitions, when there’s some room to do it without a deadline attached. That timing decision alone often determines whether the rebuild goes smoothly or becomes its own crisis. 

The bottom line 

A buy-and-build strategy succeeds or stalls based on how well the finance function can absorb growth, not just how well the deal team can source it. Building a finance operating model that scales isn’t a defensive move. It’s what lets a firm keep moving quickly on the next acquisition instead of spending months untangling the last one. 

Firms that treat finance infrastructure as part of the buy-and-build strategy from the start tend to compound faster, because every acquisition after the first gets easier to absorb instead of harder. That compounding is, in a lot of ways, the entire point of the strategy. 

A finance operating model built for scale doesn’t announce itself the way a successful deal does. It just keeps being true, quietly, that the fifth acquisition integrates as smoothly as the second. For a firm running a real buy-and-build strategy, that reliability is worth more than almost anything else the finance function could deliver.

FAQs

Is this worth building before we’ve made more than 1 acquisition?

Yes, if a second acquisition is part of the plan. The chart of accounts and definitions decided today become the default every future acquisition has to work around. Waiting until acquisition 3 or 4 just means rebuilding under pressure instead of designing calmly now.

What does it cost compared to just hiring more accountants as we grow?

Headcount fixes the workload problem for a while. It doesn’t fix a chart of accounts that can’t absorb a new entity or definitions that drift between companies. Firms that add people without fixing the structure usually end up doing both eventually, at a higher cost than if they’d built the structure first.

We already have 4 acquisitions and never did this. Is it too late?

No. The rebuild is more disruptive at this stage because current reporting depends on it, but it’s still the same fix: standardize the chart of accounts, align definitions, and remove the manual reconciliation step. Plan the rebuild for a quieter stretch between deals rather than mid-close on the next one.

Doesn’t every acquisition have different needs that make standardization hard?

Individual businesses do have different operational needs. The finance layer, chart of accounts structure, revenue and margin definitions, consolidation logic, doesn’t need to vary nearly as much as most teams assume. Standardizing that layer doesn’t restrict how each business operates day to day.

How long does building this typically take?

For a platform absorbing its next add-on, a focused rebuild usually takes at least a quarter. Firms that wait until the pain is unavoidable tend to take longer, because the rebuild happens alongside live reporting deadlines instead of on its own timeline.

Who should own this: the CFO, the controller, or an outside partner?

Someone needs clear ownership of the plan itself, whether that’s an internal finance leader or a fractional partner brought in for the transition. The specific tasks, chart of accounts design, systems setup, documentation, can be split across internal staff and outside specialists. What matters is that every piece has a named owner and a deadline, not who holds the title.

https://www.chelseastreet.com/wp-content/uploads/2026/07/Blog-Buy-and-Build-Strategy-Building-a-Finance-Operating-Model-That-Scales.png 630 1200 Amy Chamberlain https://www.chelseastreet.com/wp-content/uploads/2026/09/CSA_Long-Logo_White.svg Amy Chamberlain2026-07-30 20:38:372026-09-06 14:58:37Buy-and-Build Strategy: Building a Finance Operating Model That Scales

Get Buy-In for AI in Finance — From Leadership and Your Team 

July 16, 2026/in Insights/by Billy Cruse

The internal sell for AI in finance is almost always harder than the implementation. And  most people approach it backwards — they spend months building the business case for  leadership, get approval, and then roll out the change to a team that had no idea it was  coming and is now being asked to work differently starting Monday. 

Both conversations matter but they require completely different approaches.

Making the case to leadership 

The pitch that doesn’t work: “AI is transforming finance, and we need to stay ahead of it.”  Leadership has heard some version of this about every major technology shift from every  department for the last twenty years. It’s just noise for them at this point. 

The pitch that works needs to be operational and financial. 

– Here is how many hours per month we spend on work that could be automated.  – Here is what that time costs us in salary and opportunity. 

– Here is what we could be doing with it instead. 

– Here is the specific business outcome that unlocks. 

The more concrete the better. 

– If you had a re-forecast request come in at 4pm on a Friday and the team worked  through the weekend to produce something that was outdated by Monday, use that  example. 

– If your close cycle runs three days longer than it should because of manual  reconciliation steps, quantify it. 

Leadership approves investments in things they can picture and measure. Abstract  efficiency arguments don’t make that cut. 

It also helps to frame the ask in phases rather than as a single large investment. That way it  is easier to approve incrementally and easier to demonstrate progress on. Showing that 

phase one delivered what you said it would will make the conversation about phase two  significantly easier. 

Bringing the team along 

The fear most finance teams have about AI isn’t really about job security, even when that’s  what gets said out loud. It’s about disruption. People have built real expertise around  existing processes. They know reconciliation inside out, they know which numbers to  check twice and which analysts in other departments can’t be trusted to submit on time.  That institutional knowledge feels threatened when the process it lives in is changing. 

The most effective thing you can do is involve the team before the decision is made, not  after. Let them identify the tasks they’d most like to hand off. Ask them where they feel like  they’re wasting time and what they’d do with those hours if they had them back. 

Two things happen when you run that conversation: 

First, you get a much better map of where automation actually makes sense — the people  doing the work know the work better than anyone. 

Second, the team starts to see themselves as participants in the change rather than  subjects of it. That shift in framing changes the entire rollout dynamic. 

Another thing worth saying clearly and repeatedly: the goal is to get people out of the work  they find least valuable, not out of their jobs. When the first reconciliation runs without  anyone touching it, and the output is accurate, and the analyst who used to own it has  spent that time on something that actually required their judgment — the conversation  usually shifts on its own. People stop worrying about what AI is taking away and start  thinking about what they can do with the capacity they’ve gotten back. 

The timeline most people underestimate 

The internal sell being slower than the implementation is almost universally true, and underestimated. 

Technology moves faster than people do. A connected data layer can be stood up in  weeks. Getting a team to fully trust the outputs it produces, and to stop running the  manual version alongside it “just to check,” takes longer. That’s not a failure of the  technology or the team. It’s just how change works. 

Plan for a longer adoption curve than your implementation timeline suggests. Build in time  for the team to run parallel processes while trust develops. Celebrate the early wins loudly  — the first close that ran faster, the first Friday re-forecast that didn’t require a weekend, 

the first scenario that leadership asked for and the team delivered in two hours instead of  two days. Those moments do more for adoption than any rollout communication ever will. 

The buy-in isn’t a gate you pass through once and then you’re done. It’s something you  have to build continuously, in both directions — demonstrating value to leadership through  outcomes, and demonstrating respect to the team through involvement. When you get  both right, the implementation stops feeling like a project and starts feeling like the way things work now. 

FAQs 

What if leadership asks for ROI projections before approving the investment? 

Build the projection around time, not technology. Calculate the fully-loaded hourly cost of  your finance team and multiply it by the hours currently spent on automatable work each  month. That’s your baseline. Then model what a 50% reduction in that time would mean  annually — both in cost and in what that capacity could be redirected toward.  Conservative estimates are more credible than aggressive ones, and a phased approval  means leadership is committing to a smaller number upfront with proof points built in  before the next ask. 

How do you handle a team member who remains resistant even after early wins? 

Usually the resistance at that point is less about AI and more about identity. Some people  have built their professional value around being the person who knows the reconciliation,  runs the model, or owns the close. When that work gets automated, it can feel like a loss  

even when it’s meant to be a relief. The conversation worth having is about what their  expertise actually is — and it’s almost never the reconciliation itself. It’s the judgment, the  institutional knowledge, and the ability to catch things that the model can’t. Make that  explicit, and give them work that requires it. 

Should finance be leading this initiative or should it be driven by IT? 

Finance should own it. IT is a critical partner, especially for the data connectivity and  infrastructure work. But for the design decisions about what to automate, how scenarios  should be structured, and what outputs leadership actually needs have to come from  finance. Implementations that get handed to IT to run tend to produce technically  functional systems that don’t match the reality of how the finance team actually works.

 

https://www.chelseastreet.com/wp-content/uploads/2026/09/CSA_Long-Logo_White.svg 0 0 Billy Cruse https://www.chelseastreet.com/wp-content/uploads/2026/09/CSA_Long-Logo_White.svg Billy Cruse2026-07-16 10:02:482026-08-10 20:32:05Get Buy-In for AI in Finance — From Leadership and Your Team 

Where to Start with AI Finance Operations

July 2, 2026/in Insights/by Kenna Rooney

Most finance teams are approaching AI the same way. They find a tool that looks promising, get budget approved, and start implementation — then six months later they’re wondering why the outputs aren’t trustworthy, and why the team is still working the hours this was supposed to replace.

The problem is in the sequencing. AI finance operations isn’t something you add onto an existing workflow. It’s something you build toward, in a specific order, and that order matters more than most people want to admit.

Start with your data, not your technology

Before any AI conversation, you need an honest answer to this question: where do our numbers actually live?

Not where they’re supposed to live. In most finance organizations, the real answer involves a combination of a planning tool, several Excel files that “just haven’t been migrated yet,” a data warehouse that finance technically has access to but rarely uses, and at least one analyst who maintains a personal version of the model because the shared one can’t be trusted.

No AI tool fixes that — it just surfaces it faster.

The goal at this stage isn’t perfection, it’s consolidation. Get your assumptions living in one place. Establish who owns each data input and how often it gets reconciled. Make sure that when a number changes, there’s one place to update it and one version that everyone works from. That discipline, as boring as it sounds, is what makes everything that comes after actually work.

Then identify what to automate

Once your data foundation is stable, the next question is: what work is our team doing every month that follows a predictable set of rules?

Reconciliations, close checklists, variance commentary, budget-versus-actual reports. These are the processes that eat analyst hours and produce outputs that are almost entirely templated. The analysis embedded in them is minimal, but the execution is significant, and that’s exactly the profile of work that AI handles well.

Start narrow. Pick the two or three highest-volume, most rules-based processes in your close or reporting cycle and automate those first. Get them running cleanly before you expand. Teams that try to automate everything at once usually end up with a complicated implementation and a team that doesn’t trust any of the outputs.

The other thing worth being deliberate about: make sure the team understands what’s changing and why. The goal is to get your best people out of execution and into interpretation. When an analyst who was spending 15 hours a month on reconciliations gets that time back, the question becomes what they do with it. The answer should be better analysis, earlier catches, and more informed points of view in reviews. That shift needs to be intentional.

Build scenario planning last

Scenario planning is where AI finance operations gets genuinely powerful. Running best case, base, and stress scenarios simultaneously — with AI handling the recalculations, sensitivity adjustments, and formatting — changes how the whole finance function operates. Instead of building models reactively under pressure, the team is maintaining models that are already stress-tested and ready to update.

But it depends on the first two phases being stable. Scenarios built on fragmented data aren’t reliable. Scenarios that have to be manually reconciled across multiple tools aren’t maintainable. The reason teams find scenario planning so painful isn’t that it’s inherently hard — it’s that they’re trying to do it without the infrastructure underneath it.

When the data is consolidated and the routine work is automated, scenario planning stops being a project and starts being a practice. The team runs it continuously, updates it as assumptions change, and shows up to leadership conversations with something already built rather than something they’re racing to finish.

The sequence is the strategy

There’s a version of AI finance operations adoption that moves fast and breaks things — buys the sophisticated tool, plugs it into a broken workflow, and spends the next year cleaning up the mess. And there’s a version that moves deliberately, fixes the foundation first, and ends up with something that actually holds.

The teams that are getting it right aren’t necessarily the ones with the biggest budgets or the most advanced technology. But the ones that were honest about where their data actually lived, disciplined about what they automated first, and patient enough to build scenarios on top of a foundation that was actually worth building on.

That sequence — data, automation, scenarios — isn’t glamorous. But it’s the difference between an AI implementation that produces results and one that produces a really expensive set of outputs no one quite trusts.

That may leave you wondering…

How long does it take to get the data foundation right before moving to
automation?

It depends on how fragmented your data is today, but most teams can get meaningful consolidation done in four to eight weeks if they stay focused. The goal isn’t a perfect data infrastructure — it’s a stable and consistent one. Identify your highest-priority inputs, establish ownership, and get them reconciled in one place.

What if leadership wants to move faster than the sequence allows?

Show them what moving out of sequence costs. Find an example — ideally from your own organization — where an AI output was wrong because the underlying data wasn’t reliable and quantify what that cost was. The argument for moving deliberately is about not paying twice, once for the implementation and again for the cleanup.

How do I know when I’m actually ready to move from phase one to phase two?

A practical test: if an assumption changes in your business today, can you update it in one place and trust that it flows correctly everywhere it matters? If yes, you’re ready to start automating. If the answer involves opening three or more files and hoping nothing gets missed, your data foundation still needs work.

https://www.chelseastreet.com/wp-content/uploads/2026/09/CSA_Long-Logo_White.svg 0 0 Kenna Rooney https://www.chelseastreet.com/wp-content/uploads/2026/09/CSA_Long-Logo_White.svg Kenna Rooney2026-07-02 10:02:312026-08-10 20:31:33Where to Start with AI Finance Operations

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Maheen Zubair Khan
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Maheen Zubair Khan

Associate

Maheen works across cash flow forecasting, financial reporting, and dashboard development at Chelsea Street, turning complex financial data into clear, actionable insights that support strategic decision-making for clients. Her unique combination of accounting expertise and technical skill gives her the ability to not just analyze the numbers, but build the tools that make them accessible.

Prior to Chelsea Street, Maheen served as a Financial Analyst in FP&A at Nexamp, where she supported forecasting, budget variance analysis, and built dynamic Power BI reports for executive leadership. She also brings experience from Indus Motors Company, where she automated reporting processes that reduced manual effort by 80% and replaced legacy systems with interactive dashboards.

Maheen is an ACCA Global Prizewinner in Taxation and is passionate about using automation and data visualization to eliminate manual processes — giving finance teams more time to focus on the analysis that actually moves the business forward.

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Charles Davis

Senior Associate

Charles works across audit, financial reporting, and client engagement initiatives at Chelsea Street, bringing a detail-oriented, process-driven approach to every engagement. His background in public accounting gives him a deep understanding of the financial infrastructure that PE-backed companies need to scale with confidence.

Prior to Chelsea Street, Charles spent five years at BerryDunn, where he led audit engagements across healthcare, financial services, and nonprofit clients — managing everything from planning through financial statement delivery. He served as the primary point of contact for clients and internal leadership alike, building the kind of cross-functional relationships that make complex engagements run smoothly.

Charles holds a Master of Science in Accounting and a Bachelor of Science in Accounting and Finance from the University of New Hampshire, with a CPA currently in progress.

He is passionate about improving processes and finding efficiencies that let teams spend less time in the weeds and more time delivering meaningful financial insight.

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Phil DiBuono

Managing Partner

Phil co-founded Chelsea Street Advisors to deliver a hands-on, execution-focused model that supports private equity portfolio companies beyond diligence and into meaningful finance and accounting transformation. A finance leader with deep expertise in private equity-backed companies, SaaS businesses, and M&A transactions, he advises B2B SaaS companies through periods of growth, integration, and value creation.

Prior to Chelsea Street, Phil served as CFO for Intentsify, a PE-backed B2B company in the marketing technology space, where he led complex financial operations, revenue recognition strategy, and integration across multiple acquisitions. Before Intentsify, he was a Partner at RSM, where he built one of the firm’s largest M&A books, and spent the decade before that at Deloitte working in the Boston, New York and London Offices.

Phil holds a BS in Accounting from Providence College and an MBA from the University of Connecticut. He is a Certified Public Accountant.

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Hannah Robinson

Director, M&A

Hannah Robinson, CPA, is a transaction advisory professional with over 10 years of experience in financial due diligence, assurance, and deal support for private equity sponsors, lenders, and corporate clients. As a Director at Chelsea Street, she leads quality of earnings and financial due diligence engagements as well as supporting purchase agreement negotiations and post-close disputes and reporting. Her experience spans buy-side and sell-side mandates across a range of industries, giving her broad perspective on deal risk, EBITDA quality, and working capital.

Prior to Chelsea Street, Hannah most recently served as a Vice President at Houlihan Lokey, where she led financial due diligence on 25+ middle-market transactions across software/SaaS, business services, fintech, insurance, and technology. She also has extensive cross-border experience from her time at PwC spanning the U.S., UK, Europe, and Australia on large-cap and middle-market transactions in retail, consumer, leisure, and technology.

Hannah is known for translating complex financial findings into clear, decision-relevant reporting for boards and investors, and for building tools and AI-enabled workflows that improve consistency and efficiency across engagements. She is a Certified Public Accountant licensed in Massachusetts and earned her Master of Accounting and Bachelor of Science in Management (Finance) from Tulane University’s A.B. Freeman School of Business.

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Emmanuel Gonzalez

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Emmanuel is an FP&A and controllership professional with experience spanning global enterprises including Hitachi Vantara, Pierre Fabre, Dr. Reddy’s Laboratories, and Nissan Motor. He specializes in financial modeling, budgeting, and forecasting, with a sharp focus on keeping execution aligned to Annual Operating Plans.

Across these roles, he’s built a track record of scaling financial operations through automation — standardizing reporting workflows and leading system implementations across SAP, Oracle, Anaplan, and tools like SQL and VBA to cut manual reporting cycles and strengthen data integrity across global sites.

Emmanuel has also served as a cross-functional business partner to operating departments and executive teams, managing high-volume O2C and P2P cycles and translating complex variance analysis into actionable OpEx and CapEx insights. He rounds out his finance toolkit with advanced proficiency in modern data environments like BigQuery, Snowflake, and Looker Studio, alongside hands-on ERP implementation experience.

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Kenna Rooney

Head of Marketing

Kenna leads marketing at Chelsea Street Advisors.

She brings a finance and operations mindset to everything she does, understanding that the functions Chelsea Street supports are the backbone of every business. That perspective also shapes how she thinks about partnerships. Not as transactions, but as long-term investments in people and companies that want to build something together.

Prior to Chelsea Street, Kenna built her career in B2B demand generation with a focus on all things digital. She learned firsthand that the best results come from teams that are aligned, willing to work hard, and thinking about what comes next, not just what’s in front of them.

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Amy Chamberlain

Co-Founder and Managing Partner

Amy co-founded the firm to deliver a hands-on, execution-focused model that supports private equity portfolio companies beyond diligence and into meaningful finance and accounting transformation. She partners with investors and management teams to strengthen financial visibility, build scalable finance operations, and maximize enterprise value from acquisition through exit.

A finance leader with experience across corporate finance, M&A, and audit, Amy advises middle-market businesses through complex transactions and transition periods — helping them move from deal close to operational clarity with confidence.

Prior to launching Chelsea Street, Amy served as VP of FP&A at a private equity-backed, high-growth company, where she oversaw forecasting, financial reporting, and strategic finance initiatives. Previously, she spent over a decade at RSM, where she rose to Director of Transaction Advisory Services, developing deep expertise in financial due diligence across middle-market M&A. 

Amy holds an MBA from Suffolk University and is a Certified Public Accountant in Massachusetts. 

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Billy Cruse

Manager, Finance

Billy works primarily on forecasting, budgeting, and board reporting initiatives, partnering closely with leadership teams to deliver clear financial insights that drive strategic decisions. His experience spans both corporate finance and transaction advisory, giving him a well-rounded perspective on growth, performance, and operational scalability.

Prior to Chelsea Street, Billy most recently served as Manager of FP&A at Nexamp, where he supported executive leadership, owned company-wide forecasting, and built FP&A infrastructure from the ground up.

He also has extensive experience in M&A transaction advisory from his time spent at RSM and KPMG, performing buy- and sell-side diligence, including quality of earnings analyses for mid-sized and large service and commercial businesses.

Billy is passionate about leveraging innovation and automation to make finance functions more efficient—freeing teams to focus less on manual processes and more on meaningful analysis.

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Renee Cruise

Manager, Accounting

Renee works primarily on reporting, revenue accounting, and operational finance initiatives, partnering closely with leadership teams to deliver accurate, timely insights to directly support decision-making. With experience spanning both public and private industries, she provides a well-rounded perspective on technical compliance, process improvement, and scalable finance operations.

Prior to Chelsea Street, Renee most recently served as Accounting Manager at Living Proof Inc, where she oversaw the full revenue-to-cash cycle, supported a two-day month-end close, and partnered with the Controller, CFO, FP&A, and marketing leadership on budget-to-actual analysis and spend management.

In addition, she holds extensive experience from her time at RSM US LLP, where she advanced to Assurance Supervisor, led multiple audit engagements for mid-market consumer and industrial goods companies, and supported considerations for M&A transactions.

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Emily Carroll

Director, Accounting

Emily Carroll, CPA, is a seasoned accounting leader with extensive experience in controllership, financial operations, and scalable infrastructure design. As a Director at Chelsea Street Advisors, she delivers strategic controllership solutions to growing organizations and partners with executive leadership to build scalable finance organizations.

Throughout her career, Emily has built and led high-performing accounting organizations, including as a controller in industry. Her leadership philosophy centers on establishing clear accounting frameworks to build finance functions that support long-term, sustainable growth

Emily’s earlier Big 4 experience sharpened her expertise in transaction advisory, financial reporting, audit compliance, and technical accounting implementation, positioning her as an experienced advisor during periods of transformation and scale.

A Certified Public Accountant licensed in Massachusetts, Emily holds a B.S. in Accounting and Operations Management from Boston College’s Carroll School of Management