Why are businesses continuing to prioritize artificial intelligence in their strategies? This technology is everywhere today—discussed in strategy meetings, trending on social media, and making headlines as companies share their investments and the results achieved.
In fact, 78% of organizations now use AI in at least one part of their operations. Up from 55% just the year before. So, why the buzz? It’s all about the benefits, as highlighted in research from McKinsey and the World Economic Forum:
- 2.4 times greater productivity and 13% cost savings;
- 30-40% efficiency improvements;
- up to 25% reduction in operational costs and 90% drop in maintenance planning time;
- 50% decrease in customer service resolution times.
But it’s not all smooth sailing. 74% of organizations struggle to scale initiatives beyond AI PoC. Why? From what we’ve seen, the main reasons are a lack of data readiness, a lack of leadership buy-in, unclear objectives, and unrealistic expectations. Even with all the hype, such projects take a lot of effort to get right.
And the most crucial step? Choosing the right development approach—whether it’s a custom AI solution, an off-the-shelf application, or something in between. In this article, Ivan Pohrebniyak, Chief Delivery Officer, and Olga Hrom, Director of Pre-Sales Strategy & Delivery, will break down these options and help you figure out which one is the best fit for your business and why.
At the end of the article, as a summary, we provide a decision matrix to help you make the right choice for your journey.
Table of Contents
An Overview of AI Development Approaches
Now that this technology is gaining momentum, it’s important to decide how to implement AI in business. Here’s a quick breakdown of the three principal pathways.
Custom Artificial Intelligence Development
This type is all about building a solution that fits your business perfectly. Instead of integrating off-the-shelf tools, you can design something that directly addresses your challenges, fully matches operational workflow, and capitalizes on the available datasets.
Go for custom AI development services if:
- You’re addressing unique pain points. If your company faces complex or niche requirements that ready-made instruments can’t solve, made-to-order artificial intelligence is the right fit.
- You need total ownership. Custom applications allow you to adjust and grow as your brand/organization evolves. You don’t have to settle for generic features.
- Data security matters. For businesses handling sensitive data, a tailored approach ensures full control and compliance with privacy regulations.
- You’re thinking long-term. While custom AI can take time to develop, it’s built to scale with your firm, so you’re investing in the future.
- You want to stand out. With a bespoke solution, you can create capabilities that give you a competitive edge, whether it’s advanced analytics or specialized automation.
Examples of custom-configured AI:
- Supply chain prediction. An algorithm that adapts to changes in demand, weather, and other variables to keep things moving smoothly.
- Fraud detection. Intelligent systems that track and spot fraudulent behavior using patterns specific to your business.
- Predictive healthcare. Tools that analyze patient data to forecast outcomes, improving care with insights unique to your practice.
- Computer vision in manufacturing. AI that inspects products on the line, detecting defects that packaged tools miss.
Off-the-Shelf AI Applications
This type refers to pre-built, ready-to-use solutions that you can quickly integrate into your operations. Platforms like Intercom, Ada, Zendesk, Chatfuel, and Drift offer tools for customer service automation, chatbots, and other AI-driven services. These offerings are ideal for businesses looking for quick, reliable implementations without needing extensive customization or development.
It is optimal when:
- You face a tight timeline. If your business has common operational necessities, like simplifying FAQs or basic client interaction, out-of-the-box products can be deployed rapidly.
- You have limited AI expertise. These apps are plug-and-play, meaning you don’t require a specialized team to set them up.
- Your budget is constrained. Packaged tools are a more cost-effective option compared to custom engineering, making them a good choice for smaller budgets.
- The aim is to try or prove a concept (POC). If you’re testing the waters with AI and need a straightforward solution, off-the-shelf is a low-risk alternative.
Examples:
- Automating the knowledge base and self-service articles for consumers to find answers quickly.
- Live chat and messaging across multiple channels to handle common inquiries.
- Helpdesk and ticket management systems to streamline customer support.
- Document processing for automatically extracting and organizing data from forms, invoices, or contracts.
- Translation tools for converting content to different languages for global clients.
Hybrid AI Development Approach
This one blends the benefits of off-the-shelf with the flexibility of custom AI solutions. It allows businesses to rapidly deploy pre-built systems and then calibrate them to accommodate individual demands. This approach offers a balance of speed and adaptability, making it an excellent choice for companies seeking a quick implementation with the option to fine-tune over time.
Hybrid AI is ideal when:
- A fast rollout is essential, but there’s still a need for ongoing configuration as the business matures.
- There are specific requirements that out-of-the-box solutions can’t address, but bespoke development isn’t necessary for every feature.
- The aim is to start with a simple tool and expand its capabilities as the brand grows.
- Budget constraints exist, but some level of customization is still required to meet operational necessities.
Examples:
- A client support assistant can be enhanced with custom analytics to track and evaluate effectiveness on the go, helping to optimize employee performance.
- Implementing real-time translation to facilitate nuanced conversations, while adjusting the system to handle specialized language for a particular industry or client segment.
- Integrating legacy software or payment processors into an existing AI platform, enabling smooth data flow and seamless transactions between systems.
- Using a pre-built chatbot and adding tailored features like personalized recommendations, user data insights, and specific conversational flows to improve support quality and speed.
In-Depth Comparison of AI Development Approaches: Custom vs. Off-the-Shelf vs. Hybrid
1. Cost Considerations and ROI
| Feature | Custom AI | Pre-built AI | Hybrid AI |
|---|---|---|---|
| Initial Cost | High ($50,000 – $300,000+) | Low ($5,000 – $50,000/year) | Medium ($15,000 – $150,000+) |
| Maintenance Cost | Ongoing (Variable, $5,000 – $20,000/year) | Up to $40,000 for simple apps or higher for enterprise solutions | Moderate (Depending on customization, $2,000 – $15,000/year) |
| Upfront Investment | High (Custom development, data collection, integration) | Low (Subscription or licensing model) | Proportionate (Subscription + some custom development) |
| Return on Investment (ROI) | High in the long term (Efficiency gains, competitive advantage, long-term scalability) | Low to moderate (Quick savings, but limited ROI in specialized tasks) | Balanced (Quick wins, with flexibility for future scaling) |
| Long-Term Costs | Scalable, but can become expensive over time | Predictable, but may require additional integrations and add-ons | Flexible, but may incur ongoing costs for further modifications |
Custom AI Solution
- Initial investment typically ranges from $50,000 to $300,000, covering design, development, and integration.
- Maintenance expenses go from $5,000 to $20,000 annually for updates and performance optimization.
- High long-term ROI, especially for complex use cases. Tailored solutions often result in increased productivity, cost savings, and new revenue streams. For example, predictive maintenance can reduce machine downtime by 67%, improving production.
Off-the-Shelf AI
- Starting capital: $0 to $50,000 per year for subscriptions or licensing fees.
- Maintenance is normally included, though there may be hidden costs for scaling or additional features.
- Moderate ROI in the short term. Quick deployment offers immediate value, but scalability and far-reaching business impact can be limited. For example, a customer service chatbot can reduce response time by 50%, but may not significantly boost sales or retention.
Hybrid AI
- Entry investment falls between $10,000 and $150,000 for the initial setup, covering both pre-built tools and further adjustments.
- Ongoing costs vary between $2,000 and $15,000 annually for platform subscriptions and iterative customization.
- Balanced ROI, with rapid short-term benefits and the potential for long-term scalability. Such solutions offer expedited rollout while also being adaptable as company objectives change.
Seeking deeper insights? Discover an in-depth analysis of AI development cost
2. Time-to-Market and Deployment Speed
| Feature | Custom AI | Pre-built AI | Hybrid AI |
|---|---|---|---|
| Deployment Speed | Long (Typically 3–12 months) | Fast (Can be implemented in days or weeks) | Moderate (Typically 1–6 months) |
| Development Time | High (Requires thorough development, integration, and testing) | Low (Plug-and-play, minimal customization) | Modest |
| Implementation Effort | High (Requires dedicated team, resources, and planning) | Low (Easy integration with minimal effort) | Medium |
| Testing and Adjustments | Extensive testing and iteration required | Minimal testing required | Some testing needed, but quicker than custom |
Custom AI Solution
- Development typically takes 6–12 months to fully design, test, and integrate the tool.
- The implementation effort is high, requiring specialized expertise and a dedicated team.
- Extensive testing and multiple iterations are needed for tailoring and ensuring seamless integration.
- Done from scratch, this application is completely configured to your enterprise requirements and operations.
Off-the-Shelf AI
- Deployment can happen within days or weeks, making it a quick solution to implement.
- The rollout struggle is low, as these are pre-built, plug-and-play tools that require relatively simple setup.
- Testing is usually minimal, as ready-to-use applications are already validated for standard use cases.
- Customization options are restricted, and these are best for solving common business problems.
Hybrid AI
- Launch generally takes 1–3 months, and the effort is moderate, since you’ll be integrating pre-built tools and calibrating them to match your specifications and targets.
- Testing and adjustments are required, but they are less intensive than with handcrafted systems.
- This approach balances rapid deployment with the flexibility to expand and recalibrate alongside your organizational journey.
3. Scalability and Flexibility
| Feature | Custom AI | Pre-built AI | Hybrid AI |
|---|---|---|---|
| Scalability | High (Built to scale with your business) | Restricted (Depends on the vendor’s capacity) | Modest (Can scale, but with some limitations) |
| Flexibility | Very High (Fully customizable as needs grow) | Low (Limited to the features provided) | Moderate |
| Adaptability | Highly adaptable to changing business requirements | Fixed features and functionality | Adaptable to most business necessities with some customizations |
| Customization | Fully modifiable from scratch | Limited to preset features | Within predefined limits |
Custom AI Solution
- Designed for long-term scalability, ensuring the system grows alongside your brand without losing performance or efficiency.
- Full control over the architecture and future configurations, enabling you to continuously improve and modify as needed.
- Highly flexible, with the capacity to adjust to changing business dynamics, market demands, or industry standards.
Off-the-Shelf AI
- Limited scalability, typically suited for smaller businesses or basic use cases, making it less ideal for growing or dynamic operations.
- Fixed functionality, offering a predefined set of features that cannot easily be modified to meet specific, evolving requirements.
- Difficult to adapt to more complex or fluctuating environments, leading to potential limitations as your demands grow.
Hybrid AI
- Balanced scalability, combining the strengths of pre-built solutions with the ability to scale through modifiable elements that fit your case.
- Moderate flexibility, allowing integration of ready-to-use platforms with unique modifications to strengthen performance and responsiveness.
- Increasing adaptability, capable of evolving over time with some adjustments, though not as limitless as a fully personalized tool.
4. Integration with Systems and Data Control
| Feature | Custom AI | Pre-built AI | Hybrid AI |
|---|---|---|---|
| Integration with Legacy Systems | Seamless (Custom-built to fit into existing systems) | Limited (May require workarounds or external tools) | Moderate |
| Data Control | Full control (Complete ownership of data and privacy) | Vendor-controlled (Little control over data) | Shared control (Some control over data, depending on customization) |
| Data Security and Compliance | Fully modifiable to meet strict standards | Fixed (Depends on vendor security) | Restrained (Can be customized but with some limitations) |
| Customization of Integrations | Highly adjustable (Tailored to specific business systems) | Limited | Moderate |
Custom AI Solution
- Perfect for businesses requiring integration with legacy systems, unique databases, or proprietary tools.
- Full ownership of data, ensuring privacy regulations (e.g., GDPR, HIPAA) are met with tailored security protocols.
- Seamless connections to current tools, enhancing information flow, and minimizing interruptions.
- Maximum flexibility to adjust as your company and systems change.
Off-the-Shelf AI
- Limited integration with legacy software, often needing additional connectors or third-party instruments.
- Data is typically managed by the platform, reducing your influence on how it is processed or stored.
- Standard protection features may not align with your specific industry or compliance conditions.
- Restricted modification for proprietary system connections, leading to potential inefficiencies or extra costs.
Hybrid AI
- Shared data control, providing more authority over your information compared to ready-to-use options, but still reliant on the vendor.
- Security and compliance can be customized, but some limitations may occur due to reliance on external infrastructure.
- Moderate flexibility in integrating with existing programs, although it may require more effort than bespoke solutions.
5. Ownership, Intellectual Property, and Vendor Lock-In
| Feature | Custom AI | Pre-built AI | Hybrid AI |
|---|---|---|---|
| Ownership of Solution | Full ownership (You own the AI, the data, and the code) | Vendor-owned (License or subscription model) | Shared ownership (Custom parts owned by you, but the vendor owns the rest) |
| Intellectual Property (IP) | Full control over IP | Limited control over IP (The vendor owns the technology) | Mixed control |
| Vendor Lock-In | Low (No dependency on any vendor) | High (Dependent on vendor for updates and support) | Moderate |
| Ability to Adapt Over Time | High (Fully adaptable as your business evolves) | Low (Restricted by the vendor’s roadmap) | Modest (Some flexibility, but depends on vendor support for core features) |
Custom AI Solution
- Full ownership of the application, data, and code, giving you complete control over its evolution.
- No vendor lock-in, allowing you to manage updates and maintenance independently.
- Maximum flexibility to make changes, additions, or integrate with other systems as needed.
- Intellectual property (IP) belongs to your business, ensuring a competitive advantage and the ability to keep tech in-house.
Off-the-Shelf AI
- Platform-administered technology, where you license the AI and trust the provider for upgrades and service.
- No possession rights to IP, with improvements and updates determined by the vendor.
- Vendor lock-in is significant, making you dependent on them for ongoing support, recalibrations, and customizations.
- Limited adaptability, as the platform dictates the roadmap and features, restricting your capacity to modify the solution.
Hybrid AI
- Restricted proprietorship over bespoke functionalities, providing control over modifications and adjustments to suit your needs.
- Platform-controlled core technology, meaning you remain reliant on them for enhancements and assistance for the base tools.
- Moderate lock-in, as you can adapt tailored elements, but still rely on the supplier for pre-built blocks.
- Intellectual assets for specialized components belong to your firm, while the vendor retains ownership of the pre-built parts.
Decision-Making Framework: Finding the Right AI Development Flow
Now that we’ve covered the basics of custom, off-the-shelf, and hybrid AI flows, let’s make your decision a little easier. We know it can be tough to choose the right solution for your business, so we’ve put together a simple decision-making framework. This will help you figure out what makes the most sense for your unique needs, goals, and resources.
Take a look at the image below; it’s a quick way to get clarity on the best AI approach for you!
| # | Evaluation Criteria | 0 pts | 2 pts | 4 pts |
|---|---|---|---|---|
| 1 |
Problem Uniqueness How unique is your business challenge? |
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| 2 |
Business Criticality How critical is this AI solution to your core business operations? |
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| 3 |
Budget Availability What is your available budget for this AI project? |
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| 4 |
Timeline Flexibility How flexible is your implementation timeline? |
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| 5 |
Scalability Requirements How important is it that the solution scales with your business growth? |
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| 6 |
Data Security Requirements How strict are your data security and compliance requirements? |
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| 7 |
Data Control Needs How important is complete control over your data? |
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| 8 |
Intellectual Property Ownership How important is owning the AI technology as your intellectual property? |
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| 9 |
Vendor Independence How important is avoiding dependency on a single vendor? |
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| Total Score: | 0 / 36 | |||
Before finalizing your decision, ask yourself:
- Does this recommendation align with your current roadmap?
- Do you have the resources (time, money, expertise) for this approach?
- Will this method allow you to achieve your long-term objectives?
If you answered “no” to any of these, contemplate the approach with the next highest score or turn to our AI strategy consulting experts.
See what’s possible with the right AI partner. Tell us where you are. We’ll help with next steps.