Build, Buy, or Borrow: Mastering AI Implementation Approaches for Business Transformation

AI implementation approaches play a pivotal role in helping businesses leverage cutting-edge technologies, bring their product ideation to life, and improve business productivity. They are imperative for product and technology companies looking to enable automation, accelerate innovation, streamline core operational workflows, and deliver future-ready AI solutions.

As GenAI-powered virtual assistants, Large Language Models (LLMs) like Open AI ChatGPT, and Agentic AI frameworks become integral to digital transformation, decision-makers need to adopt AI implementation approaches that align with their organizational goals. This will not only elevate user experiences and maximize business ROI but also outshine competitors in the long run.

This blog post explores the significance of top AI implementation approaches and their essential use cases. You will learn which strategy works best for optimizing AI investments, supporting existing technology infrastructure, reducing time-to-market, and achieving optimal results. Let’s begin.

What are AI Implementation Approaches?

AI implementation approaches refer to the strategies businesses use to adopt AI for product success. These strategies determine how organizations harness the latest AI technologies to resolve critical business challenges, enhance operational efficiency, and create value for their end-users.

There are 3 major techniques to successfully implement AI – Build, Buy, and Borrow. The table below defines each of these techniques to give you a better idea.

Businesses need to adopt these AI development strategies based on their specific goals and the available resources. Many product and technology companies are moving toward the ‘buy’ and ‘borrow’ approaches for faster AI adoption. On the other hand, various global software organizations are opting for the ‘build’ strategy.

Build, Buy, and Borrow: Key Advantages and Applications

All the 3 AI implementation approaches bring their own set of unique differentiators to the table. Here’s a brief overview to the advantages of Build, Buy, and Borrow strategies and how they cater to distinct business requirements.

Advantages of Building AI Solutions

#1 Competitive Edge

Building AI solutions in-house allows businesses to craft exclusive features tailored to their unique needs, giving them a strong edge over competitors. It positions them as industry leaders by showcasing distinctive AI capabilities.

#2 Complete Control

With proprietary AI systems, decision makers can achieve full ownership of their software product. They can control content updates and maintenance as per set business requirements. This approach helps adapt to emerging challenges and uphold stringent data security protocols without relying on external parties.

#3 Better Integration with Existing Systems

Building AI solutions ensures seamless compatibility with an organization’s existing technology stack and workflows. It eliminates bottlenecks caused by mismatched systems, enhancing operational efficiency and improving decision-making processes.

#4 Scalability

Company-developed AI systems are designed to evolve with their business trajectory, ensuring meaningful impact and efficacy. As demands increase, these systems can handle huge workloads, address various challenges, and allow continuous improvements without overhauling the workflows.

When Should Businesses Build AI Solutions?

#1 Highly Specialized Product Requirements

Technology and product companies should opt for AI implementation approaches like Build when they need specific functionalities that cannot be addressed by off-the-shelf products. This approach is ideal for niche industries or unique operational tasks.

#2 Long-Term Investment

Organizations seeking sustainable AI solutions that align with their long-term goals should consider the Build approach. The upfront costs are balanced by the ability to adapt and scale over time. Additionally, custom development provides lasting value by supporting future innovations.

#3 Need for Innovation

Decision makers can build AI solutions when there is a dire need for technology innovation. It can be an ideal approach to identify new strategies for pioneering new products, resolving complex pain points, and improving user experiences.

Advantages of Buying AI Solutions

#1 Rapid Implementation

Pre-built AI solutions are ready to deploy, enabling businesses to start leveraging their benefits right away. This quick turnaround eliminates prolonged product development and perfectly supports time-sensitive projects catering to competitive markets.

#2 Reduced Costs

Buying off-the-shelf AI solutions reduces the financial burden of technology innovation and development from scratch. It minimizes expenses related to hiring specialized talent and maintaining complex infrastructures.

#3 Technology Support

Purchased AI solutions often come with comprehensive support from technology vendors, ensuring smooth operations post-implementation. Regular updates and patches keep the technology pipeline free from potential vulnerabilities and discrepancies.

#4 Dedicated Vendor Assistance

Technology vendors offer comprehensive assistance, including installation, training, and troubleshooting, ensuring seamless AI adoption. This aid reduces the learning curve, enabling teams to start using the product without any delays.

When Should Businesses Buy AI Solutions?

#1 Immediate Need for AI

Software companies having an urgent need or business opportunity for quick resolution of challenges with AI, should go for the Buy approach. It is a proven way to address immediate AI requirements using plug-and-play solutions without compromising quality.

#2 Standard Product Requirements

AI implementation approaches like Buy are suitable for projects based on pre-defined product features and functionalities. It is designed for wide applicability and can easily address common use cases like process automation, business analytics, and customer support.

#3 Budget Constraints

When financial resources are limited, buying pre-built AI solutions is a cost-effective alternative to product development from scratch. It helps deploy sophisticated AI agents that automate routine tasks and ensure compliance with industry standards without additional development costs.

Advantages of Borrowing AI Solutions

#1 Technology Expertise

Borrowing AI solutions from a trusted technology solutions provider offers access to specialized competencies and cutting-edge technologies. Experienced technology experts are well-versed with the latest AI trends, key business nuances, and custom AI engineering that support seamless product delivery and drive optimal business results.

#2 Error-Free AI Deployment

Experienced providers ensure seamless AI implementation with minimal disruptions to existing business processes. They use proven frameworks like Scaled Agile Framework to accelerate time-to-market by 50%. They embrace the capabilities of AI models and transformers to reduce human errors and enhance operational excellence.

#3 Faster Time to Value

Adopting AI implementation approaches like Borrow helps businesses optimize product deployment timelines to maximize ROI in shorter durations. It avoids lengthy product development cycles, minimizes resource-intensive development efforts, and ensures faster realization of business benefits. Additionally, it introduces personalized products for enhanced customer experience and satisfaction.

#4 Data Privacy

Collaboration with leading providers prioritize stringent data security measures and privacy protocols to protect sensitive product and business information. This partnership ensures adherence to the required compliance standards and industry regulations. Furthermore, organizations will be able to responsibly deploy AI systems without compromising data integrity and confidentiality.

When Should Businesses Borrow AI Solutions?

#1 Experimenting with AI

Borrowing AI expertise from technology professionals is a practical approach for organizations looking to experiment with AI. For instance, a skilling platform can partner with an AI solutions provider to integrate an adaptive learning engine. This enables personalized course recommendations for learners, without building the system from scratch.

#2 Unique Product Requirements

When businesses require a blend of tailored and pre-existing AI functionalities, the Borrow approach helps creating a perfect balance between key business requirements. It combines the benefits of customization with faster AI deployment. It caters to specific use cases by leveraging proven AI frameworks to reduce the development time and expedite workflows.

#3 Need for Future-Proofing

Borrow approach supports organizations in keeping up with modern AI tools such as DALL-E, Midjourney, Synthesia, and GitHub Copilot. It helps decision makers create future-ready software products that adapt to evolving market demands, meet changing business requirements, and maintain scalability and relevance over time.

Build + Buy: The Hybrid AI Implementation Approach

The hybrid Build + Buy approach empowers organizations to combine the vital aspects of custom-built and pre-purchased AI solutions. This combination helps develop business-specific AI components in-house and integrates them with ready-to-use modules. It enables companies to achieve higher business productivity and avoid long timelines of complete in-house development.

For example, a software company might design proprietary AI algorithms for automated document processing while leveraging a pre-configured NLP engine to accelerate product deployment. This dual approach ensures impeccable delivery of relevant documents while reducing cost and manual efforts.

The hybrid Build + Buy AI implementation approach is a way toward experiencing substantial benefits such as:

• Scalable product development with incremental feature additions
• Better technical debt management and precise validation of ROI
• Improved risk mitigation and focused vulnerability troubleshooting
• Enhanced AI capabilities with sophisticated off-the-shelf solutions
• Increased agility in aligning AI tools with key business objectives

How to Select the Right AI Implementation Approach

Should you build AI solutions from scratch, buy pre-configured AI tools, borrow AI expertise, or go for a hybrid approach? This decision is imperative as it underscores how you make the most of proprietary data, company information, and intellectual property while managing risks and operational overheads.

Here are 4 essential steps to select the best AI implementation approach that suits your business needs.

1. Discover Product Requirements

Define specific needs the AI solution must address, considering both immediate and long-term business goals. Evaluate the core functionalities required such as personalization, automation, or data analysis. This will help identify whether a custom software product, off-the-shelf AI tool, or a hybrid model is most appropriate.

2. Involve Major Stakeholders

Engage key stakeholders early in the decision-making process to ensure alignment with overall business objectives. Involve departments like product engineering, IT operations, DevOps, and R&D to gather diverse perspectives on what the AI solution must achieve. This collaboration ensures that the chosen AI implementation approach is practical and can be effectively integrated across the organization.

3. Prioritize Technology Specifications

Assess the technology requirements of your AI project, including integration with existing systems and scalability for future needs. Consider the complexity of data handling, processing power, CI/CD capabilities, and specific automation workflows. This helps determine whether to build a fully custom AI system in-house, adopt pre-configured tools, or approach a technology solutions provider.

4. Strategize Ethical AI Implementation

Ensure ethical considerations are well-integrated into the AI selection process such as fairness, transparency, and data privacy. Establish a strategic roadmap on how AI models will be developed, deployed, and monitored to adhere to ethical standards. Prioritize responsible AI practices to build trust and mitigate any negative impacts.