Video
The AI Maturity Model: where is your organisation on its AI journey?
AI & Automation
- Duration
- 4:21
Overview
Where is your organisation on its AI journey?
Organisations are embracing AI at very different speeds. Some are still trying to identify where AI could deliver value, while others are already building agents, deploying solutions or trying to turn successful prototypes into secure, mission-critical applications. In this video, Valto CEO Hugh Valentine introduces Valto’s AI Maturity Model, a four-stage framework for understanding where your organisation is on its AI journey. Hugh explains the stages of Envision, Prototype, Deploy and Productionise, and the questions organisations should be asking at each point. Discover how Valto helps organisations identify high-value AI opportunities, prove their value, roll successful solutions out across the business and introduce the governance, security and cost control needed for production-ready AI. Hugh also explains why AI isn’t always the answer, and how technologies such as SharePoint and Power Platform can sometimes provide a better solution to the underlying business challenge.
What we cover
Chapters
- 1
Envision: Where can we use AI?
Understand how organisations can identify worthwhile AI use cases, prioritising opportunities based on potential return on investment and ease of implementation.
- 2
Prototype: Can we prove it delivers value?
Explore how small-scale agents, prompts and AI solutions can demonstrate value and help build the business case for further investment.
- 3
Deploy: How do we roll AI out?
Learn what comes next once an AI prototype has proven its value, from securing investment to planning a wider organisational rollout.
- 4
Productionise: How do we make AI mission-critical?
Discover how organisations can move beyond solutions that are "held together with sellotape" towards production-ready AI with the governance, security and cost control needed for wider business use.
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An AI Maturity Model helps organisations understand where they currently sit in their adoption of AI and what they need to do to progress. Valto’s model divides the journey into four stages: Envision, Prototype, Deploy and Productionise.
The four stages are Envision, Prototype, Deploy and Productionise. They move from identifying potential AI use cases through proving their value and deploying them more widely, before ultimately turning successful solutions into mission-critical applications.
Valto’s envisioning sessions look at how teams and departments currently work before identifying opportunities for improvement. Potential use cases can then be ranked based on factors including return on investment and ease of implementation, helping organisations find quick wins alongside larger strategic opportunities.
Once a prototype has demonstrated value and gained investment, the next step is planning how it can be rolled out more widely. Valto’s new app scoping helps organisations understand the existing prototype and build a programme of works for wider deployment.
Productionising AI means moving beyond an early-stage solution towards something that can operate reliably as a mission-critical application. This includes addressing areas such as governance, security, cost control and the processes surrounding the solution.
Valto can support organisations at different stages of their AI journey, from identifying and prioritising initial opportunities through to scoping deployments and helping turn existing AI solutions into secure, governed and production-ready applications.
Ready to take the next step on your AI journey?
Whether you're identifying your first AI use cases, proving the value of a prototype, planning a wider rollout or trying to make an existing solution mission-critical, Valto can help you understand where you are today and what needs to happen next. Our AI specialists can help you prioritise opportunities, plan your approach and build secure, governed solutions designed to deliver real business value.
Video transcript
Hugh Valentine, CEO, Valto:
One of the concepts that I’ve been talking a lot at Valto about is something that I’m coining as the AI Maturity Model.
We’re seeing loads of organisations now that are starting to embrace AI technology, and they’re all at different levels. I’ve tried to break that down into four levels.
The first one that I’ve got is called Envision, and that’s basically organisations trying to understand where they can use AI.
What are the use cases in my organisation where I can start to use tools like Copilot, OpenAI and Claude?
Obviously, at this point in time, they haven’t got the clear use cases. They’re just being told by the board, and there’s pressure to start to identify those.
We have an envisioning session. That’s basically where we come into organisations, sit down and start to understand that company’s ways of working and how each department utilises their toolsets.
What that enables us to do is come back with a report that makes recommendations for deploying and rolling out AI. That will include use cases ranked on return on investment and ease of implementation.
Obviously, what you want to do is find the quick wins with big returns on investment, but also plan in the big returns on investment that are more strategic.
That session basically helps you understand, with our years of experience, how you can leverage AI in your organisation.
We’ll be honest as well: not all of the answers are always AI. We do come across solutions using SharePoint, automation and Power Platform. It’s just that these AI conversations are triggering people to look at their ways of working and processes.
The second use case that we’re seeing a lot of is that people are in a prototype phase.
They’ve got people in their organisations who have started to use tools like Claude, OpenAI and ChatGPT. They’ve started to build things that are actually giving a little bit of return on investment.
They’re showing the organisation, “Hey, I’ve got a great use case here, and I know that if I can get some investment, I can start to roll that out.”
That’s probably where a lot of companies are now. They’re starting to build their own little agents or their own prompts that are helping them see value.
From there, we then see organisations that are trying to get those rolled out to more of a user base in the organisation.
They’ve now got some funding. They’ve got some investment. They’re looking at that next step: how do we roll this out to the company?
We have what we call new app scoping. That’s basically where we can understand how that prototype is working or your ambitions for rolling it out.
What we’ll do is put together a programme of works that will help you roll that AI out across the rest of the organisation.
Then the final one is where organisations have rolled out an AI solution, but it’s starting to, I kind of call it, feel like it’s held together with sellotape or zip ties.
It’s not really delivering the value that they need. It’s saving some time, but it’s not fully integrated into the workforce. People are still having to push it forward or maintain it, or it keeps breaking.
They know now, without a doubt, that this could be a really valuable tool, but they’re just struggling with that final hurdle of: how do we now get it rolled out properly across the organisation?
That’s getting it production-ready and mission-critical.
We spend a lot of time with organisations that have got to that point, but they’re really struggling with that final hurdle.
What we see is Azure AI Foundry is a brilliant place for those mission-critical AI solutions because what it enables us to do is move that AI using the same technology.
Whether it’s Claude or ChatGPT, we can move it using exactly the same models into Azure AI Foundry.
What that will immediately give us is governance, cost control and security. I can then actually start to build some processes around that.
It’s not held together with sellotape or zip ties anymore. It’s actually now become a mission-critical, valuable application, and it’s set out to achieve the objectives that we were looking to achieve, which is to replace or automate that function.
So, those are the typical stages that we see.
I’m going to be doing a lot more content over the coming weeks around the AI Maturity Model.
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