AI Solutions for Engineers: The Build AI vs. Buy AI Dilemma
In the rapidly evolving engineering world, engineering leaders are turning to Artificial Intelligence (AI) to accelerate testing procedures, inform decision-making, and drive innovation. AI is no longer an interesting idea, but a vital necessity to keep up with the competition.
The AI landscape has witnessed remarkable growth in recent years, giving engineers more tools to choose from every day. In this blog post, we will cover the "build vs. buy" dilemma from the perspective of engineers and organizations seeking the most suitable AI solution for their engineering pursuits.
Building AI vs. Buying AI
Deciding between building a solution in-house and buying a tool – no matter what the application area or industry – is rooted in the same basic dilemma we ask ourselves: “Can I build my own solution using open-source tools that are good enough for my needs, or should I consider paying for a commercial, ready-to-use software package for functionality and scale?”
When considering an AI solution, the “build” option is straightforward – combine a smart engineer or data scientist team with Open-Source AI frameworks to code up something that gets the job done.
This is an acceptable approach for a “throw-away” solution to quickly analyse some engineering test data to make a few data-informed decisions. However, if you are looking at AI as a potential difference-maker for your business, you must consider both business and technical issues concerning scope, scalability, and maintainability.
A common mistake is starting down a path thinking the “simple, build it ourselves” approach is an obvious choice. Still, later you realise that the simple code you cobbled together is now responsible for a critical step in your process. In many cases, a lone engineer may be responsible for maintaining this critical piece of code as a side job, on top of everything else they usually manage.
Many of our Monolith users begin their journey faced with the build vs. buy dilemma. As AI is becoming more strategic to engineering organisations for AI-driven testing, we’ve seen a stronger shift toward commercial, off-the-shelf platforms for a solution that is more reliable, supported, and longer-lasting.
In this blog post, we’ll highlight the most common reasons for buying an AI solution based on more than 300 AI projects we’ve worked on with our customers – which can be categorised into technical and business considerations.
More Than Ease of Use - A True Productivity Tool
With the Monolith “no-code” notebook environment, engineers can build data science workflows very quickly without extensive programming or data science expertise. In the software, you can quickly load, explore, visualise, and clean your data simply by selecting pre-defined steps from a menu and entering values or options in intuitive dialogues.
With a few simple mouse clicks, users can create very complex and powerful data science workflows in just minutes. In fact, we often talk to experienced data scientists who prefer using Monolith for fast prototyping of new ideas over traditional data science tools because they can create them so quickly.
Whether a software tool is considered “easy” or “intuitive” is very subjective. For the build vs. buy discussion, you must think about larger technical and business considerations that go beyond ease of use when you are after long-term productivity and scalable innovation.
Technical Considerations for Buying AI Tools:
When comparing the relative benefits of building an AI solution vs buying one, the decision does not usually depend on technical differences in the actual algorithms at an atomic level. There are more fundamental issues to weigh.
The long-term benefits of the use model, technical support, and continuous improvement and maintenance built around these functions in a commercial software product are critical to consider. Some of the more common benefits we hear from our customers include:
- Fast Onboarding – Getting up to speed quickly on any new software package is important, particularly for mission-critical tools or systems that require multiple engineers or teams to understand and maintain. Often, programmers who build custom software solutions cannot invest the time and effort for documentation and training.
The Monolith platform has built-in tutorials and training materials so every user can get up to speed quickly with shared knowledge and understanding across the entire team. The Monolith built-in AI training is equivalent to a 3-day workshop covering the basics of the environment, hands-on exercises building data science workflows, shareable dashboards, and more general data considerations when training models.
When your team goes through a course like this, not only do they get up to speed with a tool more quickly, but they are also naturally aligned with a baseline proficiency level that makes collaboration and communication more efficient. When a solution is built-in, there might not be the capacity to document it. But when software is bought, it often comes with clear and exhaustive documentation.
- Long-Term Maintainability – Maintaining software over time, as browsers, operating systems, and feature requirements change, is a painful but necessary burden for any software developer. In addition, tools and features needed to support a team of users, such as user logins, role-based access, and methods for organizing and preserving development projects are often overlooked in custom development.
As a commercial software supplier, Monolith continually adds features and functionality while also ensuring your existing notebooks will continue to run as we introduce new versions and tools, or as general technologies change and improve over time. In addition, you can conveniently organise your data files, notebooks, and dashboards to align with your team or department structure, enabling easy access, collaboration, and maintainability of past work.
- Dedicated Engineering Test-Specific Algorithms – General-purpose data science platforms can be powerful tools for building custom solutions. Still, they may lack the focus and attention to the needs of engineering test applications and users.
At Monolith, we focus on the needs of engineering teams, and in particular validation testing applications. As such, Monolith includes a library of unique, test-specific algorithms and tools for greater productivity and impact for the validation engineer, including:
- Next Test Recommender – Using an active learning model, Monolith users can define more efficient test plans using an iterative modelling approach in Next Test Recommender. By applying custom-tailored AI models to explore the design space during testing, users can identify specific test conditions targeting areas where non-linearities, gaps, and noise appear in your test plan. The result is a more efficient test plan that covers the most important input conditions in the least number of steps.
- Time-Series Modelling – From an engineering standpoint, time-series data is much more than a series of individual data points. In many cases, time-series data are signals or waveforms acquired using a data acquisition system during testing where the order and relationship between points over time are important. The Time-Series Model in Monolith is a new AI model designed speifically to uncover hidden patterns, trends, and dependencies in these waveforms that are essential for accurate predictions and informed decision-making. The time-series model overcomes limitations found in traditional AI algorithms focused on structured tabular data for more accurate and efficient analysis and learning.
- Time-Series Manipulation – In test scenarios, engineers work with waveform data that is acquired using different systems or frequencies, making comparisons and analysis more difficult. Monolith has a library of functions for resampling waveform data to align on a single sampling frequency as well as extraction functions to identify key engineering features of a time-series waveform, such as amplitude, range, mean, RMS, crest factor, and more. In addition, you manipulate the data to find the derivative (to represent acceleration from velocity data, for example), and smooth the data with a moving average or low-pass filters.
- Integration and API Access – When operating within a large, complex department or process, software tools must work together through automation APIs and data sharing to be efficient and effective. Point solutions may not have robust integration capabilities to plug into an enterprise process.
Using the documented Monolith API, you can integrate AI models into your engineering workflow to maximise the value from your work. We support a web API (application programming interface) to allow customers to interact with the software outside of the platform UI. The API allows users to programmatically load notebook steps, edit steps or upload files from automated processes and scripts that don’t require manual work or clicks. You can easily plug the models you create into an automated process for active learning test recommendations, automated review of your raw test data for potential measurement errors, or in-line analysis to drive more efficiency and performance.
Examples of how APIs can be used to call ML models and return predictions, optimal designs, and test recommendations.
- Standard/Consistent Functionality – When multiple users are involved, ensuring a consistent experience and capability set is critical to enable sharing and collaboration. When custom software tools are built and shared, they may suffer from compatibility issues from differences in libraries or functionality included.
Monolith's cloud-based operation eliminates local installations, ensuring consistent access and avoiding compatibility issues. You can be assured that all employees have access to the same functionality without the hassle of local installations involving different packages, libraries, and versions that can lead to compatibility issues. Many times, data scientists struggle gaining adoption for their modelling because the expected consumers are unable to install Python locally, update libraries, or set up training parameters.
"Home-baked solutions are often limited to algorithms that can only run in a specific environment, on specific data, by specific people and teams with specific knowledge. This often slows down and hurts company-wide adoption."
Dr. Joël Henry, Lead Principal Engineer at Monolith
Business Considerations for Buying AI Tools:
The business or organizational decisions around building vs. buying a tool often outweigh the technical considerations.
- Knowledge Sharing/Retention – Engineering leaders have to balance solving short-term pain with ensuring longevity in the software approach they select. Custom-coded solutions may solve the short-term problem but can lock the knowledge in complex source code that other engineers struggle to understand and update.
With interactive dashboards, algorithms built in Monolith are shareable with other users, providing a high-level interface that fosters reuse and collaboration. Such collaborative tools enable employees to make their knowledge and expertise available to the rest of the company.
In a world where an entire generation of engineers is moving quickly toward retirement while younger engineers may be more willing and able to move between companies, organizations must protect their IP and ensure it continues to function as the workforce changes. Such collaborative space enables employees to make their knowledge and expertise available to the rest of the company
- Dedicated Service and Support – When tackling difficult software solutions, technical support and guidance can be the difference in success or failure in critical projects. Like many commercial software companies, Monolith provides technical support and troubleshooting for our customers. Beyond support, when you choose Monolith as your AI tool, you’re getting a business and technical partner as well.
Monolith's Customer Success team, comprised of seasoned engineers, provides comprehensive guidance spanning technical and organizational challenges to help our customers understand how to get the most out of their AI adoption. Many of our team members have years of industry experience in large engineering organizations, so they often help our customers navigate challenges beyond technical issues, such as AI strategy, ROI calculations, data strategy, data compatibility, and process improvements. And, when necessary, our Data Science team, staffed with PhDs in data science, can join in to address new approaches to particularly challenging problems.
- Subject matter expertise – In-house solutions created by a single engineer may be limited in scope and perspective, depending on the developer’s experience and willingness to seek out different ideas and opinions proactively. Buying from companies who solved similar issues for dozens or hundreds of other clients will ensure that the solution is more robust and scalable, offering capabilities that oftentimes go beyond the scope of an internally developed tool.
- Core competency - Most engineering leaders recognise that their core competency is in designing, testing, and manufacturing automobiles, industrial products, or aerospace systems – not building feature-rich AI modelling tools. What starts as a simple algorithm or modelling exercise can often grow unchecked into a huge, unmanageable labyrinth of code that becomes part of a critical process.
The challenge of maintaining custom code quickly outweighs the perceived benefit. Monolith lets organizations concentrate on their main objectives, avoiding the complexities of custom AI modelling tools and reducing associated risks.
AI tools are popping up in every imaginable industry and application area. Some of them are backed with a team and technology that truly adds value, while others may be more “fly by night” wrappers of ChatGPT just trying to take advantage of the recent hype surrounding AI.
Over time, the companies that deliver true productivity and value for their customers will thrive. At Monolith, we’ve worked on over 300 commercial AI projects over 6 years in business. That experience has driven us to build a platform that can sustain, maintain, and scale with our customers.
When considering your next major decision, consider the technical and business considerations outlined here to settle on an approach with truly sustainable value.