Rethinking the "Build vs. Buy" Decision in the Era of AI-Assisted Software Development

Introduction

Traditionally, organizations have had to weigh the benefits and costs of building custom software solutions versus buying off-the-shelf products. Factors such as cost, time, resources, and the need for customization have typically influenced this decision. However, with the advent of AI-assisted software development, this equation is starting to change.

The advent of advanced AI tools like the large language models (LLMs) powering ChatGPT is causing a paradigm shift in the way organizations approach their software development processes. These tools, known as TuringBots, enhance developers' capabilities throughout the software development lifecycle, from design to delivery of code and applications. Their potential extends to code generation, unit testing, and even generating functional tests.

AI tools like LLMs are democratizing the software development process, ushering in a new era of "low-skill software development". This development approach allows even those with limited technical knowledge to contribute to the software development process, with AI tools providing guidance, suggestions, and even generating code. This shift is lowering the barriers to entry for custom software development and making the "build" decision more viable for a broader range of organizations.

The Paradigm Shift: From Manual Coding to AI-Driven Development

Firstly, let's consider how AI is revolutionizing the process of software development. Organizations like Freshworks have reported that their coders, by leveraging the capabilities of LLMs, have managed to reduce the time it takes to create complex software applications from 10 weeks down to less than a week. What's even more noteworthy is that LLMs can create code in several widely used programming languages and also act as a coding tutors, which amplifies its benefits across a wide range of applications and skill levels.

The implications of this are profound. Speed and agility are crucial in today's dynamic business environment, and this drastic reduction in development time can provide a significant competitive advantage. Moreover, the ability of AI-driven tools to generate code also reduces the risk of human error and can potentially increase the overall quality of the software.

AI adoption in organizations has more than doubled recently, with 50% of businesses embedding at least one AI capability in their processes or products. The average number of AI capabilities used by organizations has also doubled, with robotic process automation, computer vision, and natural language text understanding leading the way. This significant increase in AI adoption, coupled with the rise of AI in software development, is transforming the way organizations approach the build vs. buy decision.

LLMs, like ChatGPT, have seen growing use in software development. It has been employed for a range of tasks, from suggesting creative aspects of software to generating codes. This tool can introduce developers to new programming languages, generate code from English prompts, document existing code, and translate between programming languages. ChatGPT is proficient with over 50 programming languages, including popular ones like Java, Python, and SQL, as well as less common ones. The implications of this are profound, particularly when considering the build vs. buy decision.

Empowering Non-Technical Firms with Automated Software Development Pipelines

The influence of AI in software development extends beyond just tech firms. Even non-technical companies can benefit significantly from adopting automated software development pipelines that push to a custom software environment. By making modest investments in these technologies, companies can empower their employees, streamline their processes, and enhance their capabilities.

The integration of AI-powered tools into software development pipelines can democratize the development process. It allows non-technical employees to contribute to software development and increases the organization's capacity to innovate and adapt. This democratization can also lead to more diverse perspectives in software design, which can result in more inclusive and effective solutions.

Traditional Factors in Build vs. Buy Decisions and the Impact of LLMs

Traditionally, organizations have considered several key factors when deciding whether to build custom software or buy off-the-shelf solutions. Here's how each of these factors are impacted by LLMs and AI-powered tools:

1. Cost: Historically, building custom software was often seen as significantly more expensive than buying an off-the-shelf solution. However, with the advent of AI-powered tools like LLMs, the cost of building custom software can be significantly reduced. LLMs can help generate code, offer creative input, assist in learning new programming languages, and even help document existing code. This can reduce the number of man-hours needed to develop software and consequently reduce the associated costs.
2. Time: Custom software development usually takes more time compared to implementing an off-the-shelf solution. But with LLMs and similar AI tools, development time can be decreased. The ability of LLMs to generate code from English prompts can accelerate the coding process, and its proficiency in over 50 programming languages means it can assist in a wide range of projects.
3. Skillset: Building software requires a specific set of skills that not all organizations have in-house. In the past, this often tilted the build vs. buy decision in favor of buying. But the advent of "low-code" or "no-code" tools and AI solutions like LLMs that can generate code and aid in software development has made it possible for non-technical teams to participate in the software development process.
4. Customization: A key advantage of building software in-house is the ability to customize the solution to exactly fit the organization's needs. While LLMs don't alter this factor directly, it makes the build option more feasible for many organizations, thereby making this level of customization more accessible.
5. Maintenance: Maintaining custom software has traditionally been more challenging and resource-intensive than maintaining off-the-shelf solutions. AI tools like LLMs can help here as well by automating some of the maintenance tasks and by helping to document code, which can make it easier to understand and maintain over time.

The Value of API-Driven Features

However, this new era of "low-skill software development" does not diminish the value of buying software. In fact, it increases the value of API-driven features when organizations decide it makes more sense to buy a new capability rather than build it internally. APIs allow for greater flexibility and customization of purchased software, aligning with the organization's specific needs. As a result, the purchased software can be tailored to the organization's requirements, ensuring a better fit and higher return on investment.

APIs (Application Programming Interfaces) allow different software systems to interact with each other, enabling organizations to integrate purchased software with their existing systems or with other purchased software. This integration can enhance the functionality of the existing systems, create new capabilities, and improve efficiency.

In the era of low-skill software development, organizations can leverage APIs to maximize the value they get from purchased software. They can use AI tools like LLMs to build custom integrations between their purchased software and their existing systems, tailoring the software to their specific needs and making the most of their investment.

Evaluating Support in SaaS Models vs. Internal Developments: The Impact of LLMs

The introduction of AI tools like LLMs brings about a new dimension to the build vs. buy decision-making process, particularly when it comes to support services. Let's revisit the critical areas of support typically provided in SaaS models, and explore how LLMs influence each when a solution is built in-house.

1. Technical Support: SaaS models come with comprehensive technical support to troubleshoot and resolve issues. When building a solution internally, LLMs can act as an AI-assisted support tool that aids developers in debugging and problem-solving, minimizing the need for a large dedicated internal support team. However, the organization would still need to provide high-level support for complex issues.
2. Software Updates and Maintenance: While SaaS providers handle software updates and maintenance, organizations building in-house must undertake this task. Here, LLMs can be instrumental in maintaining and updating software, as it can generate code for feature enhancements and suggest patches for vulnerabilities. However, the responsibility for implementing these updates remains with the internal team.
3. Security: SaaS providers implement robust security measures, which must be managed internally for built solutions. LLMs can contribute by suggesting code to enhance security features, but it cannot replace a dedicated team to enforce security policies, conduct audits, and ensure regulatory compliance.
4. Scalability: SaaS solutions are designed to be scalable, while scaling internally built solutions might be challenging. LLMs can aid in this aspect by generating scalable code structures and suggesting efficient databases and architectures, helping to make scalability a less daunting task.
5. Training and Documentation: SaaS providers offer comprehensive training materials and documentation, which would need to be created for an in-house solution. LLMs can assist in the creation of user guides and documentation, generating natural language explanations of software features, and even creating training material scripts.

The integration of AI tools like LLMs undeniably alleviates some of the support responsibilities that come with building software internally. However, they do not completely eliminate the need for dedicated support structures. Thus, executives need to weigh these factors and understand that while LLMs can be a significant ally in building in-house, it is not a complete replacement for the comprehensive support services typically provided in a SaaS model.

Actionable Steps for Executives to Leverage AI in Build vs. Buy Decisions

In light of the influence of AI tools like LLMs on software development, executives can take several strategic actions to better leverage this technology in their build vs. buy decisions:

1. Invest in AI Literacy: Understanding the capabilities and limitations of AI is critical for making informed decisions. Executives should ensure that their teams, especially those involved in software decision-making, are educated about AI, its potential uses, and its challenges.
2. Experiment with AI Tools: Given the potential of AI tools like LLMs to lower development costs and speed up the development process, executives should consider running pilot projects to explore their use in software development. This could provide valuable firsthand experience and insights into their potential benefits and drawbacks.
3. Reevaluate Existing Assumptions: The advent of AI has changed the dynamics of the build vs. buy decision. It's important for executives to revisit their existing assumptions about the cost, time, and skills needed for in-house software development.

By taking these steps, executives can better position their organizations to leverage AI in their software development processes and make more informed build vs. buy decisions.

The Road Ahead

As AI tools continue to advance and become more integrated into software development processes, organizations will need to reassess their build vs. buy strategies. With the potential to reduce costs, increase efficiency, and enable greater customization, AI-assisted software development is set to redefine the software landscape.

It's worth noting that while AI tools provide significant benefits, they are not a silver bullet. Developers should still apply caution and discipline when using AI-generated code, ensuring that it is thoroughly reviewed and tested. While AI tools can generate code that looks accurate, it may not always function as intended .

The rise of AI-assisted software development is causing organizations to rethink the conventional wisdom on the build vs. buy decision. As AI continues to evolve and become more integrated into software development processes, organizations will need to adapt and evolve their strategies to maximize the benefits.