Build vs. buy: choosing your enterprise AI assistant

The enterprise AI revolution has reached a critical juncture where organizations must decide between building custom solutions or purchasing ready-made platforms. Companies across industries face mounting pressure to implement AI assistants that can transform how employees access information, automate workflows, and make decisions — yet the path forward remains unclear for many leadership teams.

This decision carries significant implications for resource allocation, competitive positioning, and long-term operational efficiency. With over 80% of businesses expected to use generative AI by 2026, the question has shifted from whether to adopt AI assistants to how organizations should acquire these transformative tools.

The stakes are particularly high as enterprises navigate talent shortages, budget constraints, and the rapid evolution of AI capabilities. Making the wrong choice can result in wasted resources, delayed implementations, and missed opportunities to enhance productivity across engineering, sales, customer service, and other critical functions.

What is an enterprise AI assistant?

An enterprise AI assistant represents a sophisticated evolution beyond basic chatbots — it's an AI-powered digital partner that understands natural language and enables employees to complete complex tasks with unprecedented speed and accuracy. These systems integrate directly with your company's existing infrastructure, from communication platforms like Slack and Microsoft Teams to IT service desks, HR systems, and document repositories.

What sets enterprise AI assistants apart from consumer tools is their ability to deliver personalized, context-aware responses based on your organization's actual policies, data, and workflows. When an employee asks about travel policies, the assistant doesn't provide generic information; it accesses your specific guidelines, relevant forms, and can even pre-fill details based on the employee's role and history. This level of sophistication requires deep integration with enterprise systems and robust security measures to ensure data remains protected while enabling seamless access.

Core capabilities that define enterprise AI assistants

The transformation these assistants bring extends far beyond simple efficiency gains. They fundamentally change how teams collaborate and access institutional knowledge. Engineers can instantly surface relevant code documentation during debugging sessions; HR teams can automate repetitive onboarding tasks while maintaining a personal touch; sales representatives can access competitive intelligence without leaving their CRM. This represents a shift from reactive information seeking to proactive knowledge delivery — the right information surfaces at the precise moment it's needed.

The real cost of building vs. buying

Choosing to develop an AI assistant in-house requires a substantial allocation of financial and human resources. Initial costs can vary widely, starting from tens of thousands and potentially reaching into the millions, dictated by the complexity of the project. This undertaking necessitates assembling a multifaceted team — comprising specialists in data science, machine learning, software development, user experience design, and industry-specific knowledge. The journey from concept to deployment is often long, typically spanning several months to years before delivering concrete results.

The commitment does not end at launch; ongoing maintenance and updates represent a continuous financial obligation. This involves regular software enhancements, addressing emerging security threats, and evolving features to align with changing business needs. Such projects frequently exceed budgetary limits, with many experiencing cost overruns and delays, resulting in only a minority meeting their initial schedules. This scenario poses significant risks to strategic goals, pulling resources away from critical business functions.

Conversely, opting for an off-the-shelf AI solution presents a more straightforward path with predictable costs and a reduced initial financial burden. These solutions can be integrated swiftly, often within days, enabling companies to quickly harness AI benefits. Prepackaged solutions typically include comprehensive maintenance, security updates, and feature improvements as part of the service, relieving internal teams from these responsibilities. This model not only boosts productivity immediately but also ensures that vendors manage the complexities of infrastructure and compliance, allowing businesses to concentrate on maximizing AI's strategic value.

Key factors in your decision

Determining whether to build or buy an enterprise AI assistant involves a nuanced assessment of your organization's specific circumstances and strategic objectives. Building a custom solution might be the right path when your organization requires distinctive AI functionalities that existing products cannot deliver. This approach is well-suited for companies equipped with the necessary technical talent and resources, where developing an AI system is integral to achieving strategic differentiation. If proprietary technology is essential for maintaining a competitive advantage and you're prepared to navigate the complexities of custom development, building may be the way forward.

When building makes sense

When buying is the smarter choice

Opting for a pre-built AI solution often proves more practical for enterprises aiming to swiftly integrate AI into their operations. This strategy benefits organizations that need to implement AI capabilities quickly to maintain or enhance their market position without disrupting core business processes. Off-the-shelf solutions offer tried-and-tested effectiveness, with vendors ensuring compliance and security through ongoing updates and support.

Carefully considering these factors allows businesses to align their AI strategy with organizational goals, ensuring that the chosen path effectively supports long-term success and operational excellence.

Critical capabilities to evaluate

The decision to implement an enterprise AI assistant requires careful consideration of its technical capabilities, ensuring it can meet the demands of your organization's operations. Scalability in response generation is crucial; the AI assistant must efficiently handle increasing volumes of interactions while maintaining accuracy and speed. This ensures consistent delivery of information, supporting productivity without delays.

Another key factor is the AI assistant's ability to integrate seamlessly with your existing systems. It should complement and enhance current workflows and technology infrastructure, reducing friction and maximizing utility. This includes smooth interoperability with databases, applications, and platforms, allowing for streamlined information processing and updates.

Technical requirements

Business alignment

Evaluating an AI assistant goes beyond technical specifications to its fit with your business strategy. The assistant should effectively address specific operational challenges, whether enhancing workflow efficiency, boosting employee productivity, or improving customer service. A well-integrated AI solution not only meets immediate needs but also aligns with broader strategic objectives, driving tangible improvements in performance.

Scalability is another vital consideration; as your organization grows, the AI assistant must adapt to larger data sets and increased user demand. This ability to scale ensures that the AI solution continues to deliver value, accommodating business expansion and evolving requirements. Additionally, the assistant should offer adaptability, enabling it to respond to changes in processes, technological advancements, and market shifts.

Hidden costs and considerations

Building an AI assistant from the ground up involves complexities that often extend beyond initial expectations. In the competitive landscape of AI talent acquisition, organizations face rising salaries for specialists, which makes hiring and retaining the necessary expertise increasingly challenging. This situation, combined with the need for a cross-disciplinary team proficient in AI technologies and industry-specific insights, can create substantial obstacles. Moreover, there exists a risk that the developed assistant might not align with user expectations, resulting in low adoption rates and diminished returns on investment.

Another significant consideration is the potential diversion of resources, which may impact strategic priorities. Shifting focus from core business functions to AI development can lead to inefficiencies or missed opportunities in other critical areas. Additionally, the absence of a well-defined governance structure can lead to a build-up of technical debt. This accumulation can necessitate frequent updates and fixes, detracting from the organization's capacity to innovate and respond promptly to market changes.

The buy advantage

Choosing a pre-configured AI solution provides a suite of benefits that can help sidestep many of the pitfalls associated with custom development. These solutions offer immediate integration of state-of-the-art AI technologies, eliminating the need for substantial R&D spending. As these solutions are already tested in a variety of environments, they provide a reliable foundation that minimizes unforeseen complications and increases system dependability.

The swift implementation timeline of off-the-shelf solutions means that organizations can rapidly realize the advantages of AI integration, enhancing operational efficiencies without delay. Additionally, vendors continuously refine their products, ensuring that users have access to the latest features and technological improvements. This ongoing development allows businesses to maintain a competitive edge in an ever-evolving market without dedicating internal resources to product advancement efforts.

Implementation strategies for success

Implementing an AI assistant requires a strategic approach that ensures both immediate impact and future scalability. Adopting a blended strategy is essential for integrating robust, off-the-shelf solutions with tailored custom applications. This method streamlines the deployment of essential AI functionalities while allowing for bespoke enhancements that address specific organizational needs.

Blended approach

Beginning with a reliable off-the-shelf platform provides a strong base for rolling out core AI capabilities. These solutions offer standardized features that integrate quickly, facilitating a seamless transition to enhanced, AI-powered workflows. Once the foundation is established, the focus can shift to developing custom applications that meet unique business objectives. Utilizing vendor APIs enables these custom solutions to interface smoothly with existing systems, expanding functionality while maintaining continuity in operations.

This strategy directs internal resources towards developing features that set the organization apart from competitors. It emphasizes innovation in areas that deliver a strategic edge, while benefiting from the vendor’s consistent updates and support. As AI technologies advance, maintaining adaptability is crucial. Organizations should stay responsive, ready to incorporate new innovations into their AI framework to remain competitive and aligned with industry trends.

Governance and security

Robust governance and security measures are critical for ensuring the integrity and compliance of AI systems. Before implementation, it is vital to establish comprehensive data governance policies that delineate responsibilities and processes for managing data securely. These policies must comply with relevant industry regulations and data protection laws to mitigate risks associated with data breaches and regulatory infractions.

Implementing stringent access controls and permission management protocols is essential to safeguarding sensitive data. Role-based permissions ensure users access only the data pertinent to their responsibilities, minimizing unauthorized access risks. Conducting regular audits of AI interactions and data management processes enhances transparency and accountability, allowing for the proactive identification and resolution of potential vulnerabilities.

Creating effective feedback loops is vital for the ongoing refinement and optimization of AI systems. By actively gathering feedback from users and stakeholders, organizations can pinpoint areas for enhancement, ensuring that the AI assistant evolves in tandem with business needs. This continuous improvement process fosters an adaptive culture that enables the AI system to thrive in a dynamic business landscape.

Making your final decision

Selecting the right AI assistant strategy demands a strategic evaluation of your organization's specific needs and capabilities. Identify the precise challenges AI is expected to address within your business landscape. This clarity ensures that the chosen solution aligns with your operational objectives and creates tangible improvements. Reflect on the resources at your disposal, including technical proficiency and budget, to determine whether a bespoke solution is feasible or if an off-the-shelf product aligns better with your goals.

Assessment questions

Next steps

Conduct an in-depth assessment of your current systems and operational needs to make an informed decision. This analysis provides a foundation for evaluating potential solutions and identifying gaps where AI can contribute value. Compare vendors' offerings against your specific requirements, focusing on integration, scalability, and long-term support.

Calculate the total cost of ownership for each approach, considering both initial investment and ongoing expenses. This financial analysis helps pinpoint the most cost-effective strategy, balancing immediate benefits with future sustainability. Initiate a pilot program to gauge the chosen solution's effectiveness in practical scenarios, allowing for refinements based on results and user feedback.

Finally, implement a framework for ongoing monitoring and optimization of your AI assistant. This proactive approach ensures the technology evolves with your business needs and stays aligned with strategic goals. By embedding adaptability into your AI strategy, your organization can leverage AI's full potential to drive innovation and efficiency across all functions.

The path you choose — whether building custom or buying ready-made — will shape how AI transforms your workplace for years to come. We understand that navigating this decision requires careful consideration of your unique needs, resources, and strategic goals. Request a demo to explore how Glean and AI can transform your workplace and discover how we can help you implement an enterprise AI assistant that delivers immediate value while scaling with your business.