CASE STUDY
Which industries are seeing the most successes with AI-driven transformation?
The most compelling AI use cases often come from sectors with complex processes and legacy systems, such as public sector, healthcare and financial services.
What unites the most successful transformations is not scale, but clarity of purpose. These organisations start with a well-defined problem, apply AI practically and reinvest the benefits to drive further value.
For example, we work with a leading US insurance provider that achieved a 10x ROI from its initial AI use cases. This unlocked funding for larger initiatives, such as deepening customer relationships and creating new sales opportunities.
A critical success factor is executive sponsorship. The most effective transformations are business-led, typically sponsored by the COO, CEO or CFO, and executed in partnership with the technology function. Fusion teams aligned to a shared vision and supported by expert coaches drive the best outcomes.
How do companies integrate AI within existing legacy systems?
Integrating AI into legacy systems is challenging, but absolutely possible. Many organisations wrongly assume AI requires a full system overhaul. In reality, you can start small.
We help clients identify high-value use cases where AI can complement existing systems. For instance, this might involve integrating AI models into ERP systems or layering conversational interfaces across internal systems and data to make them more accessible and actionable. One example is Alcora, our solution that lets users interact with enterprise data by simply asking questions. The AI interprets these queries and executes outcomes via agents.
More broadly, we are entering a phase where today’ s apps will rapidly become tomorrow’ s legacy. The consumerisation of AI, for example with tools like ChatGPT, is setting new expectations. Systems must evolve to become intelligent, integrated and AI-accelerated.
To stay relevant, organisations must adopt integrations such as Model Context Protocol( MCP) servers that bridge core systems with AI front-ends. Doing this securely and effectively demands deep expertise and a serious commitment to new skills.
What role do cloud platforms play in enabling AI-driven transformation, private or public?
Cloud is foundational to scalable, secure AI. Whether public, private or hybrid, cloud provides the compute power and flexibility needed to train, test and deploy AI models rapidly.
At Version 1, we work across Azure, AWS, Oracle and hybrid environments to help clients choose based on value, not vendor. Public cloud remains the main engine for AI innovation, but private or sovereign cloud is gaining traction where data privacy and regulation are paramount.
That said, in regulated sectors, or where performance and data sensitivity are critical, such as healthcare, Edge Computing and onpremises GPU deployments become essential. AI solutions must be deployed in the right place to meet the needs of the environment and end users. Design matters from the start.
How do you manage security concerns around AI models and data?
There are two key angles here. First, model and data security is critically important and currently under-discussed. Dozens of Generative AI and foundational model vulnerabilities have been flagged by security analysts. We encourage clients to follow OWASP recommendations and consider AI security from the outset, including risks like model drift or ageing datasets.
Second, security is about more than data protection, it is about trust. A solid intelligence foundation is key to AI success. That means setting up governance, policies, skilled teams and partnerships early on and ensuring production-ready platforms, data pipelines, model monitoring and observability are in place.
We also guide clients through emerging regulations such as the EU AI Act, which will shape how AI is built and used. Security and ethics must be treated as design principles, not afterthoughts. Responsible AI is not optional, it is essential for successful and sustainable transformation. x
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