Growth Frameworks

Three Ways AI is Impacting Data Strategy for Growth Companies

As AI adoption accelerates across the enterprise, the quality and structure of underlying data have, in our view, become a critical determinant of AI success for companies.

Across our portfolio, we are seeing AI reshape workflows and accelerate product development in ways that are creating both new opportunities and new considerations for execution. As AI adoption accelerates, one dynamic has become increasingly apparent to us: the quality, structure and accessibility of underlying data have, we believe, become a critical factor in what companies are able to achieve with AI across their operations and within the products they bring to market. In our view, data strategy has emerged as one of the more important elements of this equation and is an important part of assessing an organization’s AI readiness. Below, we share three areas where we believe AI is reshaping data strategy and where we see implications for how investors and operators are approaching AI readiness.

Raising the Stakes for Data Strategy

The relationship between AI and data is inherently symbiotic, but the rise of agentic AI workflows and enterprise-scale automation has, we believe, made this relationship more consequential. Agentic AI systems that take action on behalf of users—whether processing invoices, routing customer inquiries or updating records—depend on accurate and complete data to operate reliably. When data quality is poor, these systems can produce unreliable outputs or flawed decisions.

In our view, agentic AI is creating new urgency around data strategy. We see many organizations showing a greater willingness, and, in some cases, a greater urgency, to fund investments in data infrastructure and key systems of record to support their AI strategy. Data quality, governance and integration have, we believe, evolved from back-office concerns into increasingly important requirements for realizing value from AI initiatives.

At the same time, in our experience, organizations cannot assume that AI will resolve underlying data quality issues. While AI tools can help identify anomalies, fill gaps and flag inconsistencies, we believe they cannot substitute for the foundational work of defining data standards, aligning systems and building reliable data pipelines. In our view, organizations that treat data quality as a precondition for AI—rather than a byproduct of it—should be better positioned to capture value from AI initiatives.

Reshaping the Tool Landscape

AI is also offering new tools and platforms that companies can rely on for data capture, storage and analysis. New capabilities are appearing across the stack, from AI-assisted data pipelines and automated schema detection to intelligent query optimization and built-in observability features. To leverage these capabilities effectively, we believe that companies should aim to prioritize modern platforms with an AI-forward vision that integrate well with their broader ecosystem. As agentic workflows become more prevalent, we believe the data warehouse is positioned to serve as an increasingly important source of context, with AI working alongside data engineers to help understand how data flows and surface issues as they arise.

A pattern we have observed is that layering on additional tools rarely solves an underlying data quality problem. In our view, creating AI-ready data often requires rethinking how data are generated in the first place, and the challenge extends beyond the data team to encompass the processes, business systems and production software where data originates. We believe that companies that address this upstream tend to be better positioned; those that do not often find that downstream tooling alone cannot fully compensate for poorly structured or inconsistently captured source data.

A less widely discussed but, we believe, increasingly consequential dynamic is that AI tools are themselves becoming data creators. In our view, this is compounding the data quality challenge. Employees are increasingly using AI assistants to draft documents, generate summaries and produce analyses, which, in our view, creates a new and growing category of enterprise data. Models can also generate synthetic data used to tune and refine further outputs. How organizations capture, validate and incorporate this AI-generated content into their knowledge stores is becoming an increasingly important question; we believe those that address it deliberately will be better positioned to maintain data integrity as AI use scales. In our view, how a company approaches this question is becoming a meaningful signal of organizational maturity and AI readiness.

Redefining the Role of People

Self-service has long been a goal of data strategy, and we believe AI tools are accelerating progress toward that vision. What we are observing across the enterprise, however, is that broader AI access introduces a new layer of interpretive risk. As employees increasingly rely on the AI tool of their choice, there is a real possibility that different teams arrive at different conclusions from the same underlying data. Responses can vary depending on the model used, and even the same sanctioned tool can yield different results when asked the same question. Results may also be skewed when relevant data points are missing from the context provided, such as earlier time periods or concurrent events.

We believe this dynamic elevates the importance of data literacy as a complement to AI tooling, and we see it as a meaningful differentiator among the companies with which we work. In our experience, organizations that have invested in training employees to evaluate data sources, understand analytical methods and interpret results tend to extract more consistent value from AI tools. But we believe the push for training only works when it is paired with the pull of culture: companies where leadership demands rigorous methodology, reproducible results and clear data lineage appear better equipped to scale AI self-service without sacrificing accuracy or consistency. In our opinion, the gap between organizations that treat this as a priority and those that do not will likely widen as AI adoption deepens.

Growth Timeline

January 1, 2024

Acquired by Vista Equity Partners

Rebrands as InvoiceCloud

Began trading on the New York Stock Exchange under the ticker symbol KVYO on September 20, 2023

January 1, 2023

Launched Klaviyo Customer Data Platform (CDP) and reviews - Surpassed 130,000 customers

January 1, 2022

Entered into a strategic partnership with Shopify, including capital investment - Launched partnership with Wix and completed first acquisition, Napkin.io - Opened Sydney office

January 1, 2021

Completes IPO on September 23 (NYSE: ESMT)

January 1, 2020

Rebranded to EngageSmart

Introduced support for Apple Pay, Google Pay

January 1, 2017

Entered the wellness vertical with the acquisition of SImplePractice.

January 1, 2021

Raised additional capital in a funding round led by Sands Capital - Launched SMS product - Announced native integration with Prestashop and partnership with WooCommerce

January 1, 2020

Raised approximately $200 million in new capital from Summit Partners and Accel

January 1, 2019

Raised approximately $150 million in capital from Summit Partners Opened London office

January 1, 2009

InvoiceCloud founded

Focused on local government and utility verticals

January 1, 2018

Surpassed 10,000 customers

January 1, 2017

Launched a partnership with BigCommerce

June 1, 2016

Surpassed 1,000 customers

January 1, 2016

Raised new capital in a funding round led by Astrial Capital

January 1, 2015

Received SAFE financing led by Accomplice

January 1, 2014

Surpassed 100 customers

January 1, 2012

Klaviyo founded

January 1, 2021

Completes IPO (NASDAQ: LFST) on June 10

January 6, 2020

Announces majority recapitalization

January 1, 2020

LifeStance completes 50th acquisition. With COVID onset, transitioned from 300 telepsych visits per week to more than 40,000

January 1, 2020

2.3M patient visits, 370 centers and 3,000+ clinicians

January 1, 2019

1.4M patient visits, 170 centers and 1,400 clinicians

January 1, 2018

930k patient visits, 125 centers and 800 clinicians

January 1, 2017

LifeStance founded with backing from Summit Partners and Silversmith Capital Partners

January 1, 2019

Launced charity streaming - live streaming fundraising

General Atlantic invests alongside Summit and management team

January 1, 2018

Entered the non-profit vertical with the acquisition of DonorDrive

Introduced and integrated telehealth solution

January 1, 2015

Summit Partners invests

Entered the healthcare vertical with the acquisition of HealthPay24

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Looking Ahead

As AI becomes more deeply woven into business operations, we believe the demands on data strategy should grow. We are seeing increased urgency across industries as tools are evolving and the human element becomes more important. In our experience, companies that invest deliberately in data quality, modern infrastructure and a data-literate workforce are better positioned to turn AI's potential into a competitive advantage. The fundamentals of good data strategy have not changed, but we believe the cost of neglecting them has grown significantly.

Related Experience

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About Summit Partners

Summit Partners is a leading growth-focused investment firm, investing across growth sectors of the economy. Today, Summit manages more than $44 billion in capital and targets growth equity investments of $10 million – $500 million per company. Since the firm’s founding in 1984, Summit has invested in more than 550 companies in the technology, healthcare and life sciences, and growth products and services sectors.