As artificial intelligence reshapes industries at an unprecedented pace, businesses face a critical decision: should they build their own AI solutions or buy ready-made products from external vendors? While the idea of building custom AI systems holds a certain allure — offering control, customization, and innovation — the reality is far more complex. The decision between building or buying AI can significantly impact business outcomes, timelines, and budgets.
On the surface, building an AI solution from scratch appears to offer distinct advantages. Businesses can tailor the system to their specific needs and maintain full control over data. For companies with highly unique processes or proprietary data, this approach might seem like the only way to gain a competitive edge.
However, the journey from concept to deployment is often far longer and more arduous than anticipated. Developing AI internally requires more than just writing algorithms — it involves data acquisition, model training, compliance management, and ongoing optimization. Moreover, businesses must invest heavily in highly skilled talent and infrastructure, and learn about new security requirements.
The Hidden Challenges of Building AI
Many companies underestimate the true scope of building internal AI systems. Data is the backbone of AI, and acquiring high-quality, diverse datasets is a formidable challenge. Once data is secured, businesses must navigate regulatory requirements around data privacy and usage — a particularly complex task in sectors like healthcare and finance.
Beyond the technical hurdles, there’s the issue of time. Developing AI models is an iterative process requiring constant fine-tuning and retraining. Even with a highly skilled team, projects often face delays due to unforeseen complications such as model bias, technical debt, and integration issues. The learning curve is steep, and failure rates are high. Developing a high-functioning system that’s on par with others can easily take years.
The Regulatory Challenges of Building AI
Building internal AI systems is far from a simple task, and it requires a deep understanding of both the technical and regulatory landscape. The journey begins with obtaining high-quality, diverse datasets, which often involves not only sourcing the data but also cleaning, structuring, and ensuring its accuracy and relevance for the specific use case.
Moreover, navigating the regulatory maze – especially in sensitive sectors like healthcare and finance – is a critical challenge. Laws such as GDPR in Europe and HIPAA in the U.S. impose strict requirements on data privacy and security, while also enforcing transparency around how the data is used and who has access to it. Organizations must implement robust data governance frameworks to ensure compliance while also managing risks associated with potential breaches or misuse.
For many companies, the balance between innovation in AI and maintaining regulatory compliance is tough. Additionally, AI models often need to be explainable and auditable, especially in regulated industries, which further complicates the entire process.
Why Buying AI Software Is the Smarter Choice
In many cases, buying AI software from established vendors offers a faster, more cost-effective path to success. Off-the-shelf AI solutions come pre-trained, optimized, and ready to deploy, allowing businesses to sidestep the lengthy development process. These solutions are often backed by robust support teams, regular updates, and built-in compliance features that align with industry regulations.
Furthermore, buying software shifts the burden of maintenance and scalability onto the vendor. This not only reduces costs but also ensures that businesses can access cutting-edge AI capabilities without having to build them from scratch. The vendor’s expertise can significantly accelerate the time-to-value, enabling businesses to focus on core operations rather than wrestling with technical complexities.
Case Study: When Building Pays Off
Robert Bosch, a global engineering and technology company, built its own AI-powered marketing solutions to streamline campaigns and improve efficiency. By leveraging proprietary customer data and tailoring algorithms to their business model, Bosch achieved better personalization and higher conversion rates than off-the-shelf solutions could offer. The investment in custom AI systems aligned directly with their competitive advantage, making the build approach worthwhile. Fortunately for Bosch, their unique skillset and employees allowed them to build this system in-house, something that cannot be claimed for most companies.
Case Study: When Buying Is the Better Option
Redfin, a real estate brokerage firm, collaborated with Amazon Web Services (AWS) to implement an AI-powered recommendation system called Redfin Matchmaker. Instead of building the system from scratch, Redfin utilized AWS’s AI capabilities to analyze extensive datasets and provide personalized home recommendations. This partnership enabled Redfin to deploy advanced AI features rapidly without the substantial investment required for in-house development.
Case Study: When Buying Solves the Problems That Building Creates
One of our current banking clients came to us several years ago and passed on the opportunity to work with us. Instead, they decided to build the AI deal-sourcing capability in-house with their highly skilled engineering team. Several years later, we’re setting up their user accounts as one of our new enterprise users. Millions of dollars were likely spent on their in-house efforts.
Assessing Your Business Needs
The decision to build or buy hinges on several factors:
- Budget: Do you have the financial resources to hire AI experts and maintain infrastructure?
- Timeframe: How quickly do you need the solution in place?
- Expertise: Does your team possess the technical skills required to develop and maintain AI models?
- Compliance: Are there regulatory requirements that external vendors can more easily manage?
- Security: Are you familiar with the security risks that your data may demand?
- Scalability: Will your AI needs expand in the future, and can your solution grow accordingly?
The Smarter Path Forward
While the prospect of building custom AI solutions may seem appealing, the risks and challenges often outweigh the benefits. For most businesses, buying AI software from trusted vendors is the more pragmatic choice — offering faster implementation, lower costs, built-in security, and reliable performance. However, building proprietary solutions for core differentiators while buying off-the-shelf tools for supporting functions may also allow businesses to balance innovation with efficiency.
Businesses should acknowledge that not every tool needs to be built from the ground up. Engineers may gravitate toward building solutions due to the “not invented here” syndrome, but creating non-core tools can stretch resources and distract from primary objectives.
As a leader in AI-powered deal sourcing, Cyndx has opted more toward building because it produces highly specialized niche products like Finder, Acquirer, Valer, and Raiser. However, buying may be an option when external solutions can efficiently support your infrastructure and non-core functions.
When considering your next AI investment, remember that buying software not only saves time and money — it empowers businesses to harness the transformative power of AI without the heavy lifting. Perhaps a thoughtful combination of build and buy can help businesses achieve faster results while staying competitive in an increasingly AI-driven world.
