In the world of private equity (PE)-backed portfolio companies, post-merger integration (PMI) is rarely a onetime occurrence. More than 6 out of 10 PE-backed acquisitions are driven by deal theses that include some form of future M&A or divestiture—perhaps a buy-and-build strategy to capture value through enhanced market penetration, cross selling or economies of scale, or perhaps a platform acquisition strategy that includes a multiyear plan for add-ons in a fragmented industry ripe for consolidation.
For the leaders of those portfolio companies—as well as for PE firms making the investments—PMI must be a capability, not just an event.
Artificial intelligence (AI) can speed up your PMI capability and make it more effective—today. In earlier articles we have shown how to take advantage of generative AI (GenAI) early in the deal lifecycle to improve the due diligence phase and how to apply GenAI during sign-to-close planning to maximize cash generation and improve working capital in the first 100 days.
But AI can do far more beyond the first 100 days. All deals—and especially acquisitions predicated on continuing M&A activity—require significant change in the way newly joined companies will operate. Several daunting and important PMI tasks await, but we see three areas that are critical to PMI outcomes where AI can have an immediate and positive impact: accelerating integration of service delivery models, streamlining organizational design for combined entities, and driving vendor contract rationalization.
Service delivery, organizational design, and contract rationalization are operational issues with huge implications for both cost and revenue. And they are difficult to execute. Historically, integrating and optimizing diverse service delivery models, assessing options for organizational design, and rationalizing vendors and their contracts have required tedious and time-consuming work representing a long and often complex journey through data that is difficult to track down or accessible only through tribal knowledge and extensive interviewing. And where data is scarce, politics can flourish. But the job must be done; and the longer it takes, the harder it is to start realizing the value in a deal thesis. Plus, no matter how many times you do it, it doesn't necessarily get easier or scale efficiently, given that no two companies or integrations are identical.
Enter generative AI
Executives and investors who know how to use GenAI can shorten
the time-consuming analysis
and relieve the administrative burden typically required to do
those jobs and, ultimately, accelerate time to value across the
entire deal lifecycle. GenAI cannot magically penetrate all the
nuances and complexities associated with this job; neither can it
make up for data that is incomplete or wrong. But when AI-enabled
tools are expert guided and combined with institutional knowledge,
they can cut weeks off the integration process. Adding well-taught
and well-managed AI to your PMI tool kit is like provisioning
aerial reconnaissance and precision weapons for those in the
trenches: it can dramatically speed up their ability to zero in on
the information they need, recommend potential solutions to guide
outcomes, and rapidly help frame data and issues so that
decision-makers can act more quickly and knowledgeably.
Let's look at each of them in turn.
Service delivery model
Service delivery models are frameworks or approaches that businesses use for delivering products or services both externally to customers and internally to employees. Some companies own their warehouses and fleets; others rent them or outsource the services. Some sell through intermediaries like wholesalers or brokers; some sell direct. Internally, some companies execute back-office IT, legal, and human resources services in-house; some deliver corporate services via a combination of outsourced and in-house resources; and many outsource such functions entirely, governed by contracts and service-level agreements.
Because no two businesses deliver goods or services in the same way, integration becomes complex—especially when a portfolio company is in a highly acquisitive period and must combine several acquisitions annually. Corporate office locations or manufacturing centers may be dispersed and situated in nonstrategic locations. Different providers might be engaged to deliver the same outsourced services—and as a company grows, certain outsourced services might become cheaper to provide inhouse or consolidate under a single provider. Service level requirements, too, may vary significantly both internally and with regard to third-party support.
In all those areas, management teams and operating partners must
make challenging decisions on what to retain, whom to retain, what
to combine, and what to divest or eliminate. Failure to address
those issues during the critical first months of PMI costs
money—and can also
confuse both customers and internal employees as they try to buy
from or work at the new organization.
GenAI's ability to ingest and analyze complex and disparate data can help operating partners and management teams sort through those questions. Given the right prompts. GenAI can vacuum up data on operational footprint and facilities, such as site-level leased/owned square footage, dedicated head count, office utilization rates, and lease terms and conditions, and then can help propose consolidation or co-location opportunities in a combined company scenario. GenAI can assess business process outsourcing vendor rates, compare and contrast existing terms and service agreements, and help management develop options to optimize spend, including outsourcing, insourcing, offshoring, or nearshoring. GenAI can read and analyze contracts to examine service-level agreements, pinpoint differences, and propose standardization for both internal and external resources.
Organizational design
Organizational design is another area in which GenAI can help
executives untangle complex issues and identify opportunities to
improve performance or reduce expenses. How do you integrate two
functions in a time-efficient manner? Organizational charts, job
titles, and job
descriptions often misrepresent who really does what. Even common
back-office corporate functions such as finance, legal, and human
resources vary drastically from company to company. Many
organizations lack definition of key outcomes and established KPIs
for measuring
performance. As a result, the integration process must be hands on,
requiring numerous interviews with functional leadership, and often
takes weeks and weeks to complete—especially in organizations
with more than a few hundred employees.
AI tools can help rapidly combine census files and roster data.
It can help standardize job taxonomy and enable management to
quickly assess which roles and responsibilities may be duplicative
across two or more organizations. AI can also recommend potential
organizational and direct reporting line alignment for employees
who may have conveyed as part of a carveout
acquisition but whose direct managers did not convey within the
transaction perimeter. The recommendation of alignment helps
integration teams get a quick sense of where to conduct deep
assessments of functional workloads and individual roles and
responsibilities, thereby connecting people with processes and
connecting processes with the future-state, combined
company organization.
GenAI can do even more: It can also assess dozens of peer
organizations based on industry, revenue, or head count, thereby
giving management a sense of its options by seeing the
best-in-class organizational designs of other companies with
similar characteristics. In our experience,
GenAI can reduce the time needed to develop an optimized
post-merger organizational design by days, even weeks. And that
advantage will compound and increase when a PE firm and a portfolio
company deploy GenAI across a series of rollup acquisitions, as the
technology "learns" from one deal to the next.
Vendor and contract rationalization
Vendor selection and contract review can be one of the most
time-consuming PMI activities. Acquired companies may have hundreds
or thousands of contracts with hundreds or thousands of vendors.
Sometimes the acquiring company will have the same vendors, but the
terms differ. Almost always there is opportunity to consolidate
spend and harmonize terms; invariably, the two companies'
internal vendor management processes should be brought together;
and virtually every case offers an opportunity to renegotiate
pricing and terms. Internal vendor spend data is usually messy on
both sides of a deal. Data management in procurement systems is
rarely uniform. We've seen cases in which the same vendor
appeared in databases under more than a
dozen different names. Without AI, reconciling and categorizing
vendors takes days; reviewing and reconciling terms and conditions
takes weeks; and while the clock ticks, value is slipping
away like sand in an hourglass.
GenAI was born to help with this job. Many use cases have
established that GenAI can rapidly:
- Accelerate the harmonization and integration of vendor names and spend categories from disparate systems and formats
- Combine spend categories across multiple legal entities
- Mesh and standardize spend taxonomies
- Classify each vendor into tiered descriptions of spend categories so that management can zero in on the most-important suppliers or identify contracts most suitable for renegotiation
- Identify tactical tail spend—the scores of small, infrequent, or oneoff purchases that elude a formal procurement process and cause waste that often hides undetected
At the same time, AI can help with rationalization of
contractual terms and conditions. Think of AI as a skilled legal
associate able to conduct a line-by-line review of contracts.
Well-trained AI can conduct such rationalization faster and more
accurately than people can. It can pinpoint duplicative contracts
and be taught to discover discrepancies in pricing and terms for
the same
or similar products and services— such as with customer
relationship management (CRM), enterprise resource planning (ERP),
cloud computing, and payroll-processing solutions—to enable
integration teams to zero in on which contract renegotiations would
yield the greatest value.
Service delivery integration, organizational design, and vendor
rationalization are of course interrelated: Together they are
foundational stones for proper PMI. And putting them securely in
place is essential so that a PE firm can turn its investment thesis
into a reality. GenAI is increasingly becoming a critical tool for
operating partners, management teams, and integration teams. Like
any tool, it is only as good as its users: it takes knowledgeable
users to point it at the right use cases and to guide it. It has to
be used by people who know the business problems it can solve and
who know the business value it can help them create. The use of
digital tools in the forms of machine learning, large language
models, and others can help show executives where to find
high-value opportunities quickly, thereby cutting down on time and
administrative burden. Leveraging AI to improve PMI activities will
produce improved operational outcomes, result in faster time to
value, and, ultimately, lead to higher returns—thereby
creating a competitive
advantage for the portfolio companies and the firms that invest in
AI's use.
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