Today, numerous AI technology solutions are available, with hundreds of providers claiming their offerings can address your needs and that AI assistants are the ultimate solution to your issues. For instance, research indicates that a company with 5,000 agents using generative AI resolved incidents 14% faster and reduced management time by 9%, emphasizing the necessity of effective planning and application to achieve real benefits.
However, before investing in AI, consider the type of solutions your vendors provide. There are two main categories:
- Custom AI Products: Tailored solutions for specific companies to address unique pain points. These solutions must align with the company’s specifications and expectations.
- Out-of-the-box, 'Plug-and-Play' AI Products: Standardized solutions that require minimal configuration and are intended for a broad audience.
According to Gartner, by 2027, over 50% of generative AI models will be industry-specific, a significant rise from about 1% in 2023. This underscores the importance of planning for the deployment of diverse generative AI models. Organizations should seek domain-specific models that can be adjusted to meet their needs.
While many believe 'plug-and-play' products represent the future of AI-enabled customer experiences, developing custom AI software from scratch can be costly and time-consuming, requiring specialized expertise for training and calibration. AIMultiple (one of the largest European companies offering comprehensive high-tech analysis) highlights that sourcing data and building high-performance models can be particularly challenging in areas lacking mature AI solutions.
Many businesses find that existing AI offerings do not satisfy their specific requirements, leading them to seek custom development options.
This article aims to provide insights (with real bases) into what to analyze when considering AI technology investments. We want to guide companies in navigating the path to success with generative AI, helping them discern the best alternatives. Evaluating market tools about your actual needs, company type, corporate culture, and employee reality is crucial. This is a decision that deserves careful consideration… Let's begin.
Key points of Plug & Play Solutions
Benefits
- Quicker Deployment: Reduced product development time lowers the overall deployment cost.
- Lower Upfront Costs: Ideal for budget-conscious organizations since initial licensing fees for commercial solutions are typically lower than custom development costs.
- Included Support: AI providers generally offer support, including updates for new features and bug fixes.
- Predefined Architecture and Data Format: Packaged solutions come with established requirements for input parameters, eliminating the need for custom architecture design.
Disadvantages
- Implementation Constraints: Integrating third-party solutions may limit flexibility and require project scope alterations if the solution does not fully meet functional and non-functional requirements.
- Increased Licensing Costs: Popularity may drive up licensing fees, and some vendors may utilize complex royalty structures based on the end product's revenue.
- Need for Specific Knowledge to set up a third-party AI solution. One drawback of low-code or no-code AI platforms is the necessity for learning software configuration, with no assurance that this knowledge will be applicable in future projects. Additionally, many off-the-shelf solutions suffer from inadequate documentation, and vendors frequently make significant changes that require retraining.
Key points of Custom-made AI Solutions
Benefits
- Customization: Tailored specifically to unique processes and needs.
- Support and Maintenance: Enhanced technical support and maintenance customization.
- Medium-term ROI: While initial costs are higher, long-term savings and increased efficiency justify the investment.
- Performance: Custom AI solutions often outperform standard options.
- Configuration of Existing Solutions: Many companies use well-known ERP software that requires extensive configuration to meet diverse requirements, a process that can take months but is essential for effective use.
- Emerging Market: AI is still developing, and no mature solutions exist for all business functions or industries.
Disadvantages
- Implementation Time: Customization demands a deep understanding of company needs and detailed process analysis, which can be complex and time-consuming. This includes developing specific algorithms and integrating proprietary data, increasing costs and development time. Changes in business processes may also necessitate ongoing adjustments.
- High Support and Maintenance Costs: Customized AI solutions require specialized ongoing support, as issues must be resolved by personnel familiar with the specific implementation. Maintenance includes algorithm updates, feature additions, and compatibility resolutions, which can be costly and challenging without a skilled technical team.
- Maturity of the AI Field: As an emerging field, AI lacks standardized solutions for all business functions, making custom development often the only viable option despite its associated challenges and costs.
Use Cases and Real Situations
When to Choose a Custom Solution:
- High Customization and Integration Needs: If your company has complex processes requiring extensive customization and integration, a custom AI solution may be appropriate.
Example: Customers needing to transfer services due to relocation often face a cumbersome process involving multiple interactions. An AI-powered virtual assistant can streamline this by allowing customers to easily request service moves via various channels. Its natural language capabilities ensure accurate information collection, enhancing customer satisfaction.
- Financial Performance and Efficiency: Standard solutions may underperform. For applications impacting finances, such as sales efficiency, a tailored solution can offer significant advantages. For instance, a customized conversational AI can handle routine customer inquiries, freeing agents for more complex issues and improving customer satisfaction.
- Configuration and Data Requirements: Standard solutions may require significant adjustments and may not perform well if company data does not match the model's training data. Enhancing performance might necessitate additional training data or consulting assistance.
When to Opt for a Plug & Play Solution:
- Fast and Economical: For quick and cost-effective general needs, plug-and-play models are ideal as they are pre-trained and ready for immediate implementation, albeit with limited customization.
- Rapid Implementation: If you need a solution quickly, plug-and-play models can be deployed in days without extensive development.
- Low Investment Cost: For companies with limited resources, plug-and-play systems save on development costs and reduce time to market, allowing for quicker ROI.
In the following table we show you an evaluation where you can analyze which option meets your needs:
Considerations for Decision-Making
Before Choosing Plug-and-Play:
a. Companies should assess their need for quick, cost-effective implementation, the generality of their processes, and their willingness to depend on vendors for upgrades and support. They should also evaluate their in-house technical capabilities for effective configuration and maintenance.
b. Key characteristics include a limited budget, urgent implementation needs, standard processes requiring minimal customization, and an infrastructure that supports easy integration with existing systems.
Before choosing Custom AI Solution:
a. Companies should determine if their needs are complex, if they can afford higher upfront costs for long-term benefits, and if they possess the technical and human resources for development and maintenance. They should also assess the integration capabilities of the customized solution with current systems.
b. Essential characteristics include unique and complex processes requiring high customization, sufficient budget for upfront costs, a qualified technical team or the ability to hire specialists, and a flexible technology infrastructure for efficient development and integration.
Conclusion
When deciding between plug-and-play and custom AI solutions, it is essential to evaluate your needs and resources. Plug-and-play solutions are suitable for quick implementation and limited budgets, allowing for fast AI integration with minimal configuration.
Conversely, if your company has complex processes, a custom AI solution may provide greater long-term benefits despite higher initial costs and longer implementation times. These solutions offer flexibility and customization that can significantly enhance operational efficiency and reduce costs over time.
Choose custom AI if you have the necessary technical and human resources for development and maintenance, and if you require deep integration tailored to your unique needs.
