Boxed or custom AI solution? A guide for decision makers

The adoption of artificial intelligence (AI) in the corporate sector is no longer just an opportunity, it is increasingly becoming a necessity. AI enables automation, supports decision-making, performs predictive analytics, generates reports, and ultimately boosts efficiency. But the question remains: should you choose a boxed AI solution or opt for a custom AI development? In this article, we aim to help you make that decision.

Decision maker cannot decide which AI solution to choose

Boxed AI solutions: fast and cost-effective

Boxed AI solutions are pre-built, standardised software that can be implemented in a short time. These solutions are often cheaper because the development costs are shared between many companies. Example: a package tracking system for a logistics company that can be up and running immediately with minimal customisation.

Benefits:

Cost-effectiveness: for example, a monthly subscription to a popular generative AI tool (like ChatGPT Plus) may be low, which is a good entry point, but more complex enterprise out-of-the-box solutions can cost more - even so, they are typically cheaper than a completely custom development.
Rapid deployment: SaaS-based AI solutions can be up and running in as little as 1 day.
Maturity and reliability: due to their widespread use and large user base, these systems are usually thoroughly tested and provide stable operation. The management of security issues is typically high for professional service providers, but the responsibility is shared between the provider and the user company.

Disadvantages:

Limited customisability: not able to cover all specific needs (e.g. specific logistical challenges).
Rigidity: the company has to adapt to predefined functions.

When to choose?

If your company works with standardised processes, is cost-sensitive or needs business benefits that can be delivered quickly.

Custom AI developments which can provide a seriuos competitive advantage

Custom AI development is fully tailored to your company's specific needs. These solutions are more expensive and time-consuming, but they can address complex problems. Example: a transport company that works with subcontractors and handles non-standard parcels.

Advantages:

Full customizability: unique predictive models that fit the processes exactly.
Long-term competitive advantage: helps differentiate at a strategic level.
Flexibility and scalability: a well-designed custom AI solution can grow with the business, adding new features and adapting to changing business needs.

Disadvantages:

High cost: Development, testing, implementation - all come at a significant cost.
Long implementation time: a simple custom system can take up to 2 months, but in complex cases it can take up to 1 year.
Resource intensive: may require in-house development team or external experts.

When to choose?

If your business manages complex, unique processes, if the use of AI is strategic, and if you need a long-term, scalable solution.

How to choose sensible?

We should support the decision with a cost–benefit analysis – considering not only the initial expenses but also the long-term advantages. Think about workforce needs (recruitment, training), the hidden costs of manual processes, and the lost business opportunities as well.

Key considerations:

Business needs: Standard reporting can be handled by off-the-shelf AI, while complex logistics processes require custom solutions.
Budget: The spectrum ranges from low monthly subscription fees (e.g., individual generative AI tools) to tens of thousands, or in the case of complex projects, even hundreds of thousands of dollars or euros for custom development.
Timeline: Immediate or short-term use (e.g., introducing a SaaS-based AI tool) vs. months- or years-long development (in the case of complex, custom machine learning systems).
Strategic importance: If AI is key to maintaining a competitive edge, the long-term investment is worth it.

The importance of pilot projects

An AI pilot project helps to test the solution in a real environment with reduced risk. This allows potential problems (e.g. data quality challenges, integration difficulties, initial model inaccuracies) to be identified in advance and the expected effectiveness (e.g. faster customer service, more accurate reports) to be measured. This is particularly important before custom developments.

Summary: A wise AI investment

There is no universal solution, the choice depends on the needs of the company, the complexity of its processes and its strategic goals. Out-of-the-box AI solutions are quick, cost-effective answers to standard problems, while custom AI developments can deliver long-term, flexible, strategic benefits. This is particularly true in the area of generative AI, where a 'out of the box' solution might be to use an existing large-scale language model (LLM) API with specific prompts, while 'custom' development might involve fine-tuning a proprietary model on enterprise data, or even creating a completely new, targeted generative model.

The best way forward?

Launching pilot projects, cost-benefit analysis and expert advice, because a well-planned AI investment can determine the future of your business.

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Resources

McKinsey & Company – AI Reports (https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2023-generative-ais-breakout-year)
PwC – AI case studies (https://www.pwc.com/gx/en/issues/technology.html)
IBM Watson – Success stories (https://www.ibm.com/watsonx)