Revenue leaders face a critical decision today when it comes to their journey to the AI-first enterprise: Should they build their own AI tools in-house or invest in already-available solutions? The answer depends on five critical success factors, including strategic alignment, technical capabilities, risk, cost, and speed to market. These build vs. buy considerations often point to investing in already available, best-of-breed solutions as the smartest path forward for modern revenue teams. Here’s why:
Strategic Alignment: Focus on the core business
The fundamental question every enterprise leader facing a build vs. buy decision must ask is: “What business are we in?”
Put another way, would building AI tools from scratch be in alignment with your strategic mission, or does it distract you from achieving it?
For the vast majority of organizations, their core competency lies in their domain expertise paired with their product/market fit in helping their customers solve their problems. Even for organizations in tech or more narrowly in generative artificial intelligence (GenAI), their expertise in what they solve for their customers is unlikely to be the same as what they are trying to solve for themselves, otherwise they’d already be using their own tech to solve the challenge(s) at hand.
Building a custom AI solution requires substantial strategic commitment that extends far beyond initial development. You're not just building software; you're entering the AI business, complete with recruiting and retaining specialized talent (often at premium salaries), ongoing infrastructure costs, ongoing research and development, security audit and compliance investments, algorithm optimization, and staying current with a rapidly evolving industry, AI models, and machine learning techniques.
On the other hand, investing in a purpose-built platform allows enterprise leaders to maintain laser focus on achieving their mission while leveraging best-in-class AI capabilities to drive competitive advantage beyond their core competency. This helps ensure an organization's finite resources and attention are directed towards initiatives and activities that directly drive business results. Often that’s enough to tip the scales towards a buy decision.
Technical Capabilities: The Depth of Specialization Matters
The technical complexity of modern AI solutions is staggering. These systems must seamlessly integrate natural language processing, predictive analytics, real-time data processing, and sophisticated machine learning models that can adapt to your specific industry, business, customer ICP, and use cases.
When you build internally, you're starting from zero on technical challenges that best-of-breed AI solution providers have already solved. Your internal team will inevitably encounter the same obstacles, make the same mistakes, and require the same iterative learning process that established providers have already navigated.
On the other hand, AI solution providers have invested years in developing their capabilities. They've solved the hard problems of data normalization across disparate sources, built robust models that can handle the nuances of B2B relationship mapping, developed multi-agent AI systems with dynamic tool orchestration, objective tracking, multi-step reasoning, and embedded domain expertise, and created algorithms that identify and act on patterns across millions of data points.
In the case of AI-powered account intelligence platforms specifically, they require systems that enable deeply researched intelligence that’s always up-to-date and relevant, available to users on demand 24/7, with alignment to the organization’s GTM motion, selling methodology, value framework, positioning, and messaging. The intelligence has to be fully extensible with integrations to the tech and applications the revenue team uses every day to inform the actions of both people and AI agents operating on behalf of the organization—all while being delivered at scale to support thousands of customer-facing employees and agents supporting even more customers.
Table 1: AI-Powered Account Intelligence Platform requirements
To build an AI-powered account intelligence platform, it’s estimated the build scenario will require at least twelve technical roles dedicated to the project. These include two distributed systems engineers to execute real-time sync and CRDT implementation; two AI/ML engineers to build multi-agent systems and embed pipelines; two security engineers to incorporate SAML, SCIM, and enterprise authentication; two full stack engineers to build a React/Typescript frontend and drive API development; one DevOps/infrastructure engineer to drive multi-service deployment and monitoring; one data engineer for ETL pipelines and external API integrations; one product manager to oversee requirements, project management, and coordination; and one engineering manager to provide technical leadership.
The technical depth required spans multiple disciplines: data science, machine learning engineering, software architecture, and security, not to mention domain expertise in the problem to be solved. It’s a combination that's extraordinarily difficult to assemble and retain internally, and which leads the vast majority of organizations towards buying versus building AI tech.
Speed to Market: The Competitive Imperative
In today's fast-moving business environment, speed to market often determines competitive advantage. While your team spends 18-24 months building an AI solution, your competitors are already leveraging purpose-built AI solutions to improve their GTM performance, enhance customer relationships, and widen their lead over peers encumbered with either the status quo or building their own solution in house from scratch.
This time-to-value gap is particularly critical in AI applications, where the benefits compound over time. The sooner you can start collecting AI-generated insights, the sooner you can begin the iterative process of refining your approach (or your AI solution provider can begin refining their approach based on your feedback) to improve your outcomes. Every month spent in development is more than a month of lost competitive advantage.
Time estimates for successfully building an AI-powered account intelligence platform optimistically range from three to five years, starting with planning and analysis, followed by requirements gathering.
From there, there are three phases to development. The first phase focuses on core infrastructure with emphasis on developing the real-time collaborative editing system with CRDT implementation, followed by building a multi-database architecture with synchronization protocols, and a basic enterprise authentication and security framework.
The second phase focuses on AI, data systems, and integrations. This includes designing a multi-agent orchestration system, building data enrichment pipelines with 10+ external API integrations, and developing observation processing and intelligence generating systems.
The third phase incorporates enterprise-grade security while driving system refinement and polish. Following the three phases of software development, the focus shifts to launching the system and onboarding users with ongoing maintenance and enhancements from there.
Best-of-breed, purpose-built platforms, on the other hand, offer immediate access to sophisticated capabilities that would take years to develop internally. They come with pre-built integrations, proven implementation methodologies, and the accumulated wisdom of multiple customer deployments. This means you can start generating business value immediately rather than waiting for your internal development project to mature.
For example, structured two week trials of Poggio’s AI-powered account intelligence platform have proven the value of tailored points of view (POVs) with quick sales wins, measurable improvements in sales results and operational efficiency, and other organizational benefits. For one Fortune 500 global cyber security leader, this included saving 5+ hours per account in research and prep, advancing a stalled opportunity to stage 2, transforming a skeptical prospect into an engaged buyer in one call, and breaking into new accounts with AI-powered POVs—all while standardizing their GTM motion to make account intelligence a seller competency. And that was just in their structured two week trial of Poggio.
Risk Mitigation: Avoiding the Innovation Tax
Building AI systems internally introduces multiple layers of risk that enterprise leaders often underestimate. Technical risk is just the beginning—there's also timeline, execution, adoption, talent, and obsolescence risk, plus the substantial opportunity cost of diverting resources from core business activities that would have otherwise helped achieve your organization’s strategic mission.
In addition to the technical capabilities noted previously, multi-agent orchestration requires deep AI expertise, and is difficult and costly to debug and optimize, introducing significant technical risk. AI solution providers have already overcome these challenges with platforms that work as designed, complimented by optimization efforts that are continually improving the AI’s capabilities, user experience, and resulting business impact.
Launch delays and timeline risks are often inevitable as organizations building tech in-house—especially AI—have a tendency to underestimate the complexity of the work. Common challenges include encountering system integration issues and discovering emergency issues at scale that require rework. AI solution providers have already crossed these bridges over years of development with AI platforms that are already delivering enterprise value for customers today.
The risk of building something that doesn't actually solve your business problem is often understated, yet significant. Most AI providers have refined their solutions through countless customer implementations, learning from real-world deployments across diverse industries and use cases to continually improve their platform. This practical experience is invaluable and not easily replicated in internal development which often faces these execution risks.
Users may not adopt the internal solution at a high enough rate to drive the desired outcomes whereas AI solution providers have learned and innovated from customer deployments to optimize the experience and value achieved for users. Further, proof of value trials both demonstrate the AI solution’s impact and the technology’s usability and adoption rate with real users to further mitigate adoption risk.
The competition for AI talent is fierce, and the specialized skills required are scarce, making talent risk a common roadblock. Even if you successfully recruit top talent, you're constantly at risk of personnel departures that can derail projects. AI solution providers have already assembled teams and have the organizational depth to ensure business continuity.
AI innovation moves at breakneck speed, and what seems cutting-edge today may be outdated within months or even weeks, introducing costly obsolescence risk. Purpose-built platforms are designed to evolve continuously, with dedicated teams focused solely on staying ahead of both customer needs and technological curves. Internal builds, by contrast, often become legacy systems that are difficult and expensive to not only maintain, but modernize.
The summation of all of these risks represents the substantial opportunity cost of diverting resources from core business activities to not only build the AI system, but mitigate these risks through the life of the software. When faced with the choice of focusing on the core business to enable customer and business success versus pursuing a custom-built AI solution, most organizations and their investors encourage staying true to the organization’s strategic mission and north star.
Cost Considerations: The True Economics of AI Development
While the upfront cost of purchasing AI solutions may represent a significant investment, the total cost of ownership for internal development is almost always higher—and often dramatically so.
Internal development costs extend far beyond initial software development. Organizations must factor in recruiting and retaining specialized talent (often at premium salaries), ongoing infrastructure costs, scope creep, continuous research and development, security audits and compliance investments, documentation and training, performance optimization, bug fixes and technical debt, and ongoing support and maintenance.
Moreover, internal builds typically require 2-3 times longer to reach production readiness than initially estimated. This means you'll be paying development costs for extended periods while receiving no business value. Meanwhile competitors have adopted best-of-breed, purpose-built AI solutions and are actively advancing their GTM motion, capturing market share, and generating ROI immediately.
The subscription model of AI platforms also provides cost predictability and aligns expenses with business outcomes; particularly when pricing is aligned to customer value. In the case of Poggio’s pricing per account POV, you’re pay for the value you receive, get value across your entire GTM team without having to worry about who does and does not have access to critical AI capabilities for account intelligence, and are able to scale costs with your growth—all while avoiding the lumpy capital expenditures associated with internal development projects.
Perhaps the most significant cost, however, is the impact to an organization’s core focus with multiple person-years of engineering time and effort not spent on core business differentiation. This is not only a significant risk as noted above, but also a substantial opportunity cost with your best technical resources diverted from achieving your strategic mission.
Conclusion: The Path Forward
The decision between buying and building AI capabilities ultimately comes down to strategic priorities and resource allocation. While building internally may seem to offer more control, the reality is that purpose-built, best-of-breed AI platforms offer superior capabilities, faster time-to-value, lower total cost of ownership, and reduced risk—all while allowing your organization to maintain focus on its strategic mission and core differentiation.
The enterprises that will thrive in the AI era are those that understand when to build and when to buy. For AI account intelligence, the evidence overwhelmingly supports buying a best-of-breed solution to achieve immediate value.
At Poggio, we've seen countless enterprises deploying our AI-powered account intelligence platform. They are bringing their revenue into the intelligence age to achieve GTM, sales, and business transformation. To see how Poggio can accelerate revenue growth, reduce costs, and power your path to the AI-first enterprise, start your free trial today.
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