Version 1 is a leader in Digital Transformation, partnering strategically with global organisations to adopt cutting-edge technologies and drive innovation responsibly. With a fast-growing team of 3,400+ professionals across four continents, Version 1 is committed to creating value through sustainable transformation. Brad Mallard, CTO at Version 1, discusses AI-driven Digital Transformation, focusing on its practical implementation and business impact.
How important is sustainable transformation to Version 1?
Sustainable transformation is a top priority at Version 1. We believe that digital progress and environmental responsibility must advance together. As organisations accelerate their adoption of AI, cloud and data, we work with them to ensure these innovations are aligned with their ESG [Environmental, Social and Governance] goals.
Version 1 is a signatory of the UN Global Compact and aligns with the UN Sustainable Development Goals, focusing particularly on areas such as inclusivity, accessibility and carbon or environmental considerations.
Sustainable transformation can be defined as ‘a mechanism to transform and deliver value’ in a broader societal sense, not just in financial or organisational terms. When done well, it addresses both.
Take the Children and Family Court Advisory Service, for example. We deliver tangible societal benefits by leveraging AI to support children and families navigating court services. Our solution personalises engagement using various media, languages and terminology tailored to the audience, reducing stress and accelerating the process.
Another initiative involves using Generative AI to modernise operations for a leading global school provider, giving time back to teachers and unlocking more effective learning methods. These projects deliver meaningful societal and sustainable benefits by applying powerful technologies like AI in a targeted way.
What’s the biggest challenge for businesses when it comes to Digital Transformation?
Technology isn’t usually the biggest hurdle, alignment is. Many digital programmes begin with a tool or trend instead of a clearly defined business problem. At Version 1, we challenge the status quo by first ensuring the right question is asked, then breaking down the problem into manageable steps to deliver tangible, iterative value.
Too often, Digital Transformation programmes are slow, costly and misaligned with the needs of frontline users or business owners. There’s a disproportionate focus on features, SaaS implementations and integrations, while people, change management, education and new ways of working are overlooked.
Another commonly underestimated area is data. Data must be high-quality and understandable to deliver meaningful and measurable outcomes. Success comes when transformation is treated as a strategic lever, not a technology project.
How has AI affected Digital Transformation?
Revisiting the idea of sustainable transformation, we can also think of it as continuous transformation and AI is the catalyst that enables this. On one level, AI has transformed business models, experiences and processes. On another, it empowers people and organisations to achieve far more than previously possible. It is redefining how we approach transformation and tackle historical challenges.
Our AI-enabled approach is geared toward sustainable, value-driven transformation. We equip people to enhance their roles with AI, delivering faster, iterative outcomes that prioritise the release of value.
Complex programmes are built incrementally by prioritising strategic and operational drivers, unlocking cost savings or creating new value to fund subsequent phases. This approach consistently delivers value where traditional transformation programmes often fall short.
How do you see AI developing in the near future?
The rapid pace of technological change makes it hard for any business to fully grasp or apply AI meaningfully. But that is not really the point. While I’m excited by AI, delivering value starts with understanding the business, its challenges and user needs, then working backwards from there.
Emerging technologies like multi-modal models and agentic AI are gaining traction. At the same time, considerations around security, regulation and compliance must be addressed from the outset, not as an afterthought.
With the right foundations and governance, combined with AI-empowered engineers and consultants, we can deliver tangible outcomes more effectively. I am particularly excited about the possibilities in audio and video recognition and generation. These could reinvent parts of the business in ways that traditional technologies, such as Robotic Process Automation, have not been able to.
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 on-premises 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.
