Deciding on AI: Deploy vs. Build for Enterprise Success

Deploying vs. Building AI Solutions for Enterprises

Enterprises are in the midst of a transformative era, where AI holds the promise of solving immediate business challenges and unlocking new opportunities. In our previous article, we discussed how to identify AI use cases within your organization to drive immediate value.

In this dynamic landscape, decision-makers face a critical choice: should they deploy existing AI solutions or embark on the journey of building custom solutions tailored to their specific needs? This pivotal decision significantly impacts not only the trajectory of AI adoption within an organization but also its long-term success. Let's look at the nuances of deploying versus building AI solutions and explore the key decision points that guide enterprises on this transformative journey.

Deploying AI Solutions: The Path of Expediency

For many enterprises, the allure of deploying AI solutions lies in its expediency. Off-the-shelf AI solutions offer ready-made answers to pressing business challenges without the need for extensive development cycles or specialized expertise. By leveraging pre-built AI solutions, organizations can swiftly integrate AI capabilities into their existing lines of business, thereby accelerating time-to-value and gaining a competitive edge.

Decision-makers opt for deploying AI solutions when:

1. Urgency is Paramount: When time is of the essence and immediate results are imperative, deploying AI solutions provides a rapid and reliable means to address pressing business needs.
2. Resource Constraints: Limited access to AI talent or technical expertise may compel organizations to deploy off-the-shelf solutions rather than invest in building custom solutions from scratch.
3. Proven Track Record: Established AI solutions with a track record of success in similar industries or use cases instill confidence in decision-makers, mitigating the perceived risks associated with custom development.

While deploying AI solutions offers a pragmatic approach to addressing immediate business challenges, it's essential for enterprises to weigh the trade-offs carefully. For certain use cases, off-the-shelf solutions may lack the level of customization required to fully align with unique business processes and objectives, potentially limiting their effectiveness in the long run.

Building Custom Solutions: Tailoring AI to Fit

In contrast to deploying off-the-shelf applications, building custom AI solutions empowers enterprises to tailor AI capabilities precisely to their unique requirements and preferences. This path involves the development of bespoke models, datasets, and applications, crafted to address specific business challenges. While the journey of building custom solutions demands greater investments of time, resources, and expertise, it may be suitable for extremely tailored solutions.

Enterprises opt for building custom solutions when:

1. Unique Business Needs: When off-the-shelf solutions fail to address the intricacies of complex business processes or industry-specific challenges, custom development becomes a necessity rather than a choice.
2. Hybrid Approach: When an off-the-shelf solution works but there is value in using a custom, fine-tuned model. This solution may still use components that are off-the-shelf while tapping into an organization's own fine-tuned model(s).

While building custom AI solutions offers flexibility and potentially a strategic advantage, it's not without its challenges. The development process entails inherent risks, including cost overruns, technical complexities, and uncertainties surrounding scalability and maintenance. Moreover, the shortage of AI talent presents a formidable obstacle for enterprises embarking on the journey of custom development.

Decision Points: Balancing Expediency and Customization

The decision to deploy or build AI solutions hinges on a myriad of factors. Here are some key decision points that guide organizations in striking the delicate balance between expediency and customization:

1. Business Objectives: Aligning AI initiatives with overarching business objectives is paramount. Organizations must assess whether the benefits of custom solutions justify the investment of time and resources, or if deploying off-the-shelf solutions suffices to achieve immediate goals.
2. Technical Expertise: Evaluating the organization's internal capabilities and external partnerships is crucial. While some enterprises possess the requisite AI talent and infrastructure to embark on custom development, others may benefit from deploying an off-the-shelf solution to get their feet wet in understanding how to operationalize AI workloads within their environment.
3. Scalability and Flexibility: Anticipating future growth and scalability requirements is important. Building or deploying AI on a predictable and scalable infrastructure is essential. In a future article we will discuss the topic of AI infrastructure in greater detail.Risk Tolerance: Assessing risk tolerance is integral to decision-making. Enterprises must weigh the risks associated with custom development, including technical challenges, resource constraints, and market uncertainties, against the potential rewards of innovation and differentiation.

It's also important to note that an organization may opt to deploy in certain use cases (e.g. private copilot) yet choose to build for others.

In conclusion, the journey of AI adoption is rife with opportunities and the decision to deploy or build AI solutions carries profound implications for enterprises. By carefully weighing the decision points organizations can navigate this transformative landscape with confidence, unlocking the full potential of AI to propel them towards a brighter future.

🔑 takeaway: opt to deploy an off-the-shelf solution for common use cases but consider building when a deeply customized solution or model is required.