While you can drive significant benefits from AI using and integrating existing cloud platforms, sometimes bespoke AI development is necessary. While it offers endless potential for differentiation, it also carries the most expense and risk, so tread carefully.
In this three-part ‘Adopting AI’ series, we look at getting the most out of existing platforms, integrating multiple platforms, and building custom AI tools.
The business case for custom AI
Powerful enterprise platforms like ServiceNow offer extensive customisation and integration options; however, there are limits to what any commercial solution can accomplish.
Custom AI development is worth considering when you need to solve business-critical problems or create unique differentiators beyond what commercial platforms can deliver – even with significant customisation. For example:
Your competitive advantage lies in proprietary processes that could be enhanced or automated through AI
You operate in specialised industries with unique unaddressed requirements
Your data or use cases are highly specific to your business context
Security or compliance mandates complete control over AI implementations
You’re developing intellectual property that could become a market differentiator
Evaluating the build vs buy decision
Software is more flexible than ever before, so before going down the custom-built road, thoroughly evaluate all alternatives considering:
- Total cost of ownership: Custom development involves not just initial build costs but also ongoing maintenance and operational expenses. Compare these long-term costs against commercial alternatives.
- Time to value: Custom solutions typically take longer to develop and deploy. Weigh the opportunity cost of delayed implementation against the benefits of a perfectly tailored solution.
- Internal capabilities: Custom AI development demands specialised skills in data science, machine learning and your specific business domain. Assess whether you have – or can acquire – these capabilities.
- Risk management: Address inherent risks around project delivery, technical performance and ongoing support before committing to custom development.
Custom AI in practice: Real-world examples
At Nexon, we’ve developed several custom AI solutions for clients with unique requirements that off-the-shelf products couldn’t meet:
Advanced call management systems
Nexon has developed AI-powered solutions for intelligent communication management. Delphi, our locally hosted large language model (LLM), is fine-tuned on historic call data to intelligently predict how email-based service records should be triaged. It automates the process of creating incidents, requests, identifying spam, relating to existing records or flagging communications that require manual review – significantly reducing administrative overhead while improving response accuracy.
Developer productivity tools
We’ve built custom Azure AI tools that help development teams manage backlogs and tasks more efficiently. We use AI to prioritise work and identify potential bottlenecks before they impact project timelines.
Industry-specific predictive models
For organisations in specialised industries, we’ve created custom predictive models trained on proprietary client data that provide insights and forecasting capabilities generic solutions can’t match.
Best practices for custom AI development
When considering and implementing custom AI solutions, follow these key practices for success:
- Focus on strategic differentiation: Identify where custom AI creates genuine competitive advantage and define specific, measurable objectives before evaluating whether native capabilities or integrations could meet your needs.
- Ensure data readiness: Custom AI is only as good as the data that powers it. Before beginning development, verify that you can access high-quality data, clear governance policies and appropriate processing capabilities.
- Assemble the right expertise: Build a team with the specialised skills needed for success, including data science, machine learning engineering, and deep business domain knowledge, whether internal or through trusted partners.
- Start small and iterate: Begin with focused, minimum viable products that deliver tangible value. Use agile methods that adapt based on user feedback rather than ‘big bang’ deployments.
- Plan beyond development: Design for scalability, monitoring and maintenance from the beginning. Establish processes for updates, governance and ongoing optimisation.
Balancing your AI approach
As we’ve explored throughout this series, most organisations will need a thoughtful combination of all three approaches to AI implementation: leveraging native capabilities, strategic integrations and custom development.
The art is finding the right balance that works for your organisation – matching each approach to where it can deliver the most value while making the best use of your resources.
As a certified ServiceNow partner and recipient of two 2025 ServiceNow APAC Partner of the Year Awards categories, Nexon helps organisations implement all three approaches to AI adoption, delivering the optimal mix of solutions to maximise business value while managing cost and complexity.
To explore how Nexon can help you implement the optimal mix of AI approaches for your organisation’s needs, discover our comprehensive AI advisory services and solutions.
Join us at the ServiceNow Put AI to Work Summit in Brisbane on 29 April 2025 to explore AI-powered business transformation and see these capabilities in action.
Shayne Ray is a Lead Consultant, Data & AI at Nexon Asia Pacific. Get in touch with us for more information about strategic AI integration with ServiceNow.
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