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Building In-House vs Partnering with an Automation Expert: Choosing the Best Approach for Enterprise AI

November 5, 2025

CIOs and technology leaders face growing pressure to deliver real business outcomes with AI — not just pilots, but measurable impact. As organizations explore different paths to scale AI — whether building in-house or partnering with an automation expert — the real question becomes how to deliver sustained value without falling into the cycle of endless AI experimentation.

In many ways, today’s challenges feel like déjà vu. We’ve seen these waves of innovation before — from the early days of ERP and 3GL coding tools to cloud and low-code platforms. Each wave began with bespoke builds that promised differentiation but often ended in complexity and costs. Over time, each generation matured into platforms that delivered 80% of the solution out of the box, freeing teams to focus on the final layer of innovation that drives real business value. The AI wave is following the same script — proving that innovation succeeds when it’s grounded in business purpose, not technology enthusiasm.

While it’s never been easier to spin up a flashy prototype — AI has now been democratized to the point where anyone can get started quickly — the reality of digital transformation is far more complex. Therefore, it’s no surprise that MIT research found that 95% of in-house Gen AI pilots fail.

So, what should organizations really be considering to ensure their AI investments deliver measurable and lasting impact? How can you make sure your AI project becomes a success — not another experiment that fails to scale?

This article will help you see beyond the hype and make the right decision when it comes to future-proofing AI systems. Read on to learn more.

Why ‘Building’ Often Fails in Practice

The idea of building a bespoke automation solution is appealing, since it promises ultimate control, flexibility, and the ability to quickly pivot as your business needs change. However, as many organizations have already discovered, the path from prototype to production is challenging. Building in-house isn’t a failure of skill — it’s often a matter of scope. Enterprise AI requires more than models; it demands pipelines, governance, feedback loops, and change management — disciplines most IT teams weren’t designed for. In addition, in-house developers may lack the deep domain and process expertise needed to design solutions that truly reflect business needs. Most organizations excel in their industries — not in software development — which makes building and maintaining enterprise-grade tools even more challenging. The cost isn’t just in the first build, but in ongoing maintenance, accuracy, and compliance over time.

These challenges often show up in several key areas:

Significant Time and Effort to Reach MVP

Even though it's now easier than ever to create impressive demos using open-source AI toolkits, the time and effort required to move beyond the lab is still significant. Many custom AI projects can take up to six months just to reach a minimum viable product, with some never achieving it at all, due to stakeholder misalignment and scope creep.

The “Accuracy Fallacy”

It’s easy to mistake high lab accuracy for real-world performance — a common pitfall known as the “Accuracy Fallacy.” Even the best-performing models can fail when exposed to the variability of live business data. True accuracy requires more than high model scores; it demands operationalization through continuous learning, feedback loops, drift controls, observability, and robust exception handling. Without these capabilities, prototypes that look impressive in the lab often break in production, leading to high failure rates and stalled projects when scaling millions of transactions or unstructured documents.

Challenges with Unstructured Content

Roughly 90% of enterprise content is unstructured (encompassing emails, PDFs, images, and mixed layouts), posing significant challenges for internal teams who frequently underestimate the complexity of managing diverse data types. This results in unpredictable runtime costs, tedious manual workflows, and solutions that can’t handle real-world inconsistency.

Upgrades and Maintenance

Technology is moving faster than ever before, with new models, frameworks, and trends emerging constantly. Building your own solution means taking on the burden of ongoing upgrades, as well as patching and integrating with external services — adding significant cost, effort, and risk to your project. Without a clear strategy for keeping systems current, internal teams often find themselves on a “technical debt treadmill,” constantly refreshing tools and re-validating compliance while trying to maintain production uptime.

Security and Compliance Concerns

Alongside the cost of application maintenance, organizations must take full responsibility for the solution’s security and compliance provisions — a commitment that is continuous and resource-intensive. This includes implementing SSO, maintaining audit trails, and meeting regulatory standards, such as GDPR and PCI. It’s easy to underestimate just how much work this is during the excitement of early development; keeping up with sophisticated security technologies and threats can quickly become overwhelming.

Beyond certifications, enterprise AI systems also require end-to-end trust mechanisms — including automated data redaction, human-in-the-loop exception handling, and continuous assurance processes that help detect drift and minimize hallucinations. These capabilities are essential for maintaining transparency, explainability, and resilience against evolving security and compliance challenges.

Technical Debt and Hidden Costs

In-house builds almost always accrue hidden costs. For instance, quick fixes like custom code and ad hoc integrations soon become unmanageable liabilities as they can create an over-reliance on key staff. Should these specialists move on, you’re left with significant skills gaps and escalating maintenance costs, eliminating any initial savings you may have had.

User Interface and Experience Design

Even the most advanced AI solutions need intuitive, user-friendly interfaces that enable people to act on insights. Designing a modern interface that combines data, documents, and images for tasks like approving workflows, redacting sensitive information, or correcting errors requires specialized UI/UX expertise. These capabilities are often overlooked in internal builds but are critical for adoption and long-term success.

Key Considerations for AI Decision Makers

Before deciding whether to build internally or partner with an automation vendor, executives should ask: Why are we doing this? Is it to chase a trend, or to solve a real bottleneck in customer experience, cash flow, or compliance?

The best AI programs start small, have a clear problem to solve with known ROI, and scale once results are proven. Not the other way around.

Building on that foundation, organizations should then examine the key considerations that drive long-term success.

Are we paying for true business value or just access to AI toolkits and capacity?
How much time and risk are we willing to accept in building/maintaining our own solutions before realizing value?
Is it possible to implement AI at scale, and does our solution adequately address the requirements of all primary stakeholders?
Do we have the team and expertise to manage compliance, governance, security, and other related obligations?
Do we have the resources and budget to operate, monitor, and upgrade this solution for the next 3–5 years without accruing technical debt?
Can we easily support multiple AI providers to avoid vendor lock-in?

If any of these questions highlight internal hurdles or uncertainties, it’s a sign that approaching an intelligent automation specialist may be the best path forward.

Tungsten Automation: Enterprise AI Automation Made Simple

For organizations that decide partnering is the right path, it’s essential to look for a platform that combines flexibility with enterprise readiness. Tungsten Automation offers a comprehensive, enterprise-ready AI automation solution that bridges the gap between innovation and operational excellence.

Our flagship platform, TotalAgility, combines prebuilt workflows, operationalized LLMs, and intelligent automation features (like human-in-the-loop, feedback mechanisms, and robust governance), all in a single, scalable platform.

It also integrates seamlessly with top AI providers, including Microsoft Azure’s OpenAI Service and others— meaning you can avoid the pitfalls of relying solely on hyperscalers or building DIY solutions in-house. You can enjoy the best of both worlds.

TotalAgility: Key Benefits

Faster Deployment: Companies using TotalAgility experience rapid time-to-value, moving from pilot to production in weeks rather than months, while internal projects often face significant delays — even when leveraging market-leading AI toolkits.
Model-Agnostic Flexibility: TotalAgility’s Bring Your Own Model (BYOM) capabilities allow you to combine leading AI providers — including Microsoft Azure’s OpenAI Service and others — as well as your own custom LLMs, to ensure your teams have the best AI solutions at their fingertips.
Explainable, Responsible AI: Demonstrating how AI reaches its decisions is crucial, especially in regulated industries. TotalAgility provides full traceability and explainability through built-in governance, automated redaction, and continuous assurance — with bias mitigation, drift controls, and transparent outputs that satisfy both internal stakeholders and external regulators, delivering responsible AI at enterprise scale.
Enterprise-Grade Security and Compliance: Our solution safeguards your data with encryption, role-based access, tenant isolation, and data residency controls. We also hold industry certifications, including GDPR, SOC2, PCI, and HIPAA, ensuring compliance across global markets.
Built-in Governance & Auditability: Complementing this foundation, TotalAgility provides exception handling, human-in-the-loop controls, audit trails, SLA management, and continuous monitoring — ensuring operational compliance and scalability are consistent from day one.
Cost Predictability: Our transparent pricing model ensures you know exactly what you’ll pay, whether you’re processing a handful of documents or millions of pages.
Continuous Improvement: Backed by a dedicated R&D team of over 150 full-time specialists, we deliver ongoing innovation through quarterly feature releases and a customer-informed roadmap — keeping you “Always Current” through vendor-managed upgrades and continuous compliance alignment.
Energy Efficiency and Sustainability: Streamlined automation reduces infrastructure overhead and energy consumption — helping you meet sustainability targets without compromising performance.
End-to-End Automation: TotalAgility unifies the entire spectrum of intelligent automation in a single enterprise-ready platform. From document processing and agentic workflow orchestration to Knowledge Discovery and integration with downstream systems, our platform manages processes from start to finish.
Broad Ecosystem Integration: As mentioned previously, the platform works ‘better together’ with hyperscalers. Additionally, it’s supported by over 1,000 partners, more than 3,000 pre-trained models, and over 300 pre-built solutions. This unified ecosystem streamlines the automation lifecycle, enabling you to consolidate vendors and simplify your technology stack.

Proven at scale, Tungsten Automation powers more than 25,000 customers globally, processing millions of documents per day with 99.9% uptime, and our enterprise clients consistently achieve measurable ROI. For example, a leading healthcare organization automated 68 internal processes across 8 departments using our platform, driving $9.6 million in value and demonstrating true operationalization of AI-driven automation. See more TotalAgility success stories here.

Better Together: TotalAgility’s Strategic Microsoft Partnership

With TotalAgility, you gain a powerful advantage: the enterprise-readiness and automation expertise of Tungsten Automation, combined with cutting-edge hyperscaler technologies such as Microsoft Azure’s OpenAI Service.

This partnership is designed to maximize your investment. While building directly with hyperscalers provides AI power, TotalAgility accelerates deployment and reduces risk through built-in workflow capabilities that bring structure and speed to AI initiatives.

Unique Partnership Advantages

Operationalized LLMs: TotalAgility integrates Large Language Models (LLMs) from Microsoft Azure’s OpenAI Service, adding features such as prompt orchestration, feedback loops, and drift controls to ensure real-world accuracy and governance.
Prebuilt Workflows and Seamless Integration: You can embed AI directly into your digitized workflows while harnessing hundreds of preconfigured solutions — TotalAgility eliminates the need to assemble and manage multiple tools.
Flexible Deployment Options: Whether you prefer a Microsoft Azure environment, your own cloud, or on-premises, we provide deployment flexibility to meet your business needs — with the added benefit that LLM usage costs and support are included. You don’t need to manage separate vendor contracts, monitor consumption, or coordinate multiple support channels.
Streamlined Procurement: TotalAgility is available through the Azure Marketplace and counts toward your Microsoft Azure Consumption Commitment (MACC), helping you maximize your Microsoft investment while simplifying purchasing and approvals.

Tungsten Automation: Proven Results, Recognized Leadership

Vendor-partnered solutions succeed twice as often as in-house builds (67% vs. 33%), making Tungsten Automation a proven alternative for organizations ready to move from experimentation to impact. With over 40 years of expertise, Tungsten helps enterprises operationalize AI with confidence through scalable, enterprise-grade solutions that accelerate time-to-value and reduce complexity.

TotalAgility delivers a unified, plug-and-play platform built upon hyperscaler technologies such as Microsoft Azure, providing a powerful foundation for intelligent automation. Instead of piecing together toolkits that demand heavy customization and ongoing maintenance, you gain the right AI for the right task—instantly—so your teams can focus on outcomes, not integration.

Gartner® named Tungsten Automation a Leader in Intelligent Document Processing (IDP) in its 2025 Magic Quadrant™, which we believe validates our market position and recognizes the maturity and scale of our platform. Backed by dedicated AI and automation expertise, more than 230 patents, and a global base of over 25,000 customers, we help organizations realize the full value of AI innovation.

Ready to see why leading enterprises trust Tungsten Automation to turn AI potential into measurable impact? Get in touch with our experts today.

Gartner, Magic Quadrant for Intelligent Document Processing Solutions, Shubhangi Vashisth, Tushar Srivastava, et al., 3. September 2025

GARTNER ist eine eingetragene Marke und Dienstleistungsmarke von Gartner, Inc. und/oder seinen Tochtergesellschaften in den USA und international, und MAGIC QUADRANT ist eine eingetragene Marke von Gartner, Inc. und/oder seinen Tochtergesellschaften und wird hier mit Genehmigung verwendet. Alle Rechte vorbehalten.

Gartner unterstützt keine Anbieter, Produkte oder Dienstleistungen, die in seinen Forschungspublikationen dargestellt werden und rät Technologieanwendern nicht, nur die Anbieter mit den höchsten Bewertungen oder anderen Bezeichnungen auszuwählen. Die Forschungspublikationen von Gartner stellen die Meinungen der Forschungsorganisation von Gartner dar und sollten nicht als Tatsachenbehauptungen ausgelegt werden. Gartner lehnt jegliche ausdrückliche oder stillschweigende Gewährleistung in Bezug auf diese Studie ab, einschließlich Gewährleistungen hinsichtlich der Marktgängigkeit oder Eignung für einen bestimmten Zweck.

by Kirsten Gardner Sr. Product Marketing Manager