How AI-ready is your organisation? Start with a baseline
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How AI-ready is your organisation? Start with a baseline
Attila Bernariusz
Published on 22 September 2026
8 min read
Attila Bernariusz
Published on 22 September 2026
8 min read
Discover why AI initiatives stall and learn how establishing an AI readiness baseline can help your organisation move from experimentation to measurable business value.
AI ambition is easy to find. A clear path from experimentation to measurable business value is much harder to achieve. For many organisations, the pressure to adopt AI is growing faster than their understanding of how to do it successfully. Teams are experimenting, leaders are setting ambitious adoption targets, and investment is rapidly increasing.
But before you can effectively scale AI – or credibly prove its ROI – you need to understand where you’re starting from. That means establishing a clear baseline of AI readiness.
Without that baseline, or if you default to a “we’re not ready yet” position, you won’t know whether the right foundations are in place to support your business ambitions, where investment will have the greatest impact, or which gaps could quietly undermine your AI efforts. An AI readiness baseline provides the starting point for making those decisions.
So how do you know if your organisation is ready for AI? The answer starts with understanding where you are today.
Without that baseline, or if you default to a “we’re not ready yet” position, you won’t know whether the right foundations are in place to support your business ambitions, where investment will have the greatest impact, or which gaps could quietly undermine your AI efforts. An AI readiness baseline provides the starting point for making those decisions.
So how do you know if your organisation is ready for AI? The answer starts with understanding where you are today.
Why do AI programmes stall?
Moving from ambition to action too quickly is one of the main reasons AI programmes run into roadblocks.
Often organisations begin with the technology. They identify a promising use case for AI, select a model or platform with all the bells and whistles, launch a pilot, and then focus on measuring adoption. But even when the technology is fit for purpose, initiatives can stall if the broader capabilities needed to support them are missing.
Often organisations begin with the technology. They identify a promising use case for AI, select a model or platform with all the bells and whistles, launch a pilot, and then focus on measuring adoption. But even when the technology is fit for purpose, initiatives can stall if the broader capabilities needed to support them are missing.
- Data might not be accessible, reliable, or governed well enough to support AI tools.
- Teams may lack the skills and confidence to use AI effectively—especially when experimentation and prompt iteration carry real cost.
- Governance may not have kept up with experimentation.
- Existing processes may not be designed to accommodate automation, let alone AI.
- Ownership of AI rollout may be unclear, with no named leader holding the mandate, budget, and targets to drive change.
Without understanding where you’re starting from, you end up with disconnected AI pilots rather than a scalable capability.
What is an AI readiness baseline?
An AI readiness baseline is a structured view of an organisation’s current capabilities and preparedness for AI adoption. In simple terms, it helps you assess your current state, define a realistic target state, and identify the gaps between them.
It is more useful than the simple score resulting from a readiness assessment. The value comes from understanding what that score means, where the gaps are, and what needs to happen next. The goal is not to achieve the highest possible score. The right level of readiness depends on your goals. If you want to introduce AI into a small number of internal workflows, your requirements will be different from those of an organisation aiming to embed AI across customer-facing products and operations.
To reach the right level of readiness, you need to ask:
It is more useful than the simple score resulting from a readiness assessment. The value comes from understanding what that score means, where the gaps are, and what needs to happen next. The goal is not to achieve the highest possible score. The right level of readiness depends on your goals. If you want to introduce AI into a small number of internal workflows, your requirements will be different from those of an organisation aiming to embed AI across customer-facing products and operations.
To reach the right level of readiness, you need to ask:
- What are we trying to achieve with AI?
- What capabilities do we already have?
- Which capabilities are missing or underdeveloped?
- What level of readiness do our business goals actually require?
- Which gaps should we address first?
By answering these questions, you can develop a much stronger foundation for deciding what to invest in, which use cases to prioritise, and how to build an AI roadmap.
How do you find out your AI readiness baseline?
An AI readiness assessment provides a structured way to answer those questions. It’s a useful tool, particularly early in your AI journey when there’s plenty of enthusiasm and experimentation but less clarity about overall readiness. It’s especially useful for teams who suspect something is blocking their AI ROI—not the technology itself, but something systemic they can’t yet identify.
At Adaptavist, we’ve invested significantly in R&D to develop an AI Readiness Assessment that goes beyond a generic score. Our approach is designed to help organisations understand not just whether they are ready for AI, but what is standing in the way of value realisation, which gaps matter most, and what to prioritise next.
Rather than focusing on individual tools or isolated use cases, a readiness assessment looks across the organisation to understand the capabilities that underpin successful AI adoption and value realisation.
Established maturity models also reinforce this approach. Gartner’s AI Maturity Model, for example, is designed to help organisations establish a baseline, identify gaps between current and desired maturity, and inform prioritisation and roadmaps.
Similarly, the AI Adoption Maturity Model from Accenture and Carnegie Mellon University’s Software Engineering Institute takes a multi-dimensional view of AI readiness. What these models highlight — and what we see in practice — is that AI readiness extends well beyond technology alone.
With that in mind, a useful AI readiness baseline should consider the following:
At Adaptavist, we’ve invested significantly in R&D to develop an AI Readiness Assessment that goes beyond a generic score. Our approach is designed to help organisations understand not just whether they are ready for AI, but what is standing in the way of value realisation, which gaps matter most, and what to prioritise next.
Rather than focusing on individual tools or isolated use cases, a readiness assessment looks across the organisation to understand the capabilities that underpin successful AI adoption and value realisation.
Established maturity models also reinforce this approach. Gartner’s AI Maturity Model, for example, is designed to help organisations establish a baseline, identify gaps between current and desired maturity, and inform prioritisation and roadmaps.
Similarly, the AI Adoption Maturity Model from Accenture and Carnegie Mellon University’s Software Engineering Institute takes a multi-dimensional view of AI readiness. What these models highlight — and what we see in practice — is that AI readiness extends well beyond technology alone.
With that in mind, a useful AI readiness baseline should consider the following:
- Strategy – is there a clear connection between AI investment and business objectives? Are priorities and desired outcomes understood?
- Governance – are there appropriate policies, responsibilities, and controls in place to manage AI risk, security, ethics, and compliance?
- Data – is your organisation’s data accessible, reliable, and governed well enough to support AI use cases?
- Operating model – do you have ownership, processes, and ways of working in place to move AI from experimentation into everyday operations?
- People and culture – do your people have the skills, confidence, and support they need to work effectively with AI in a cost-conscious environment?
- Technology – are your organisation’s platforms, infrastructure, and engineering practices capable of supporting the proposed AI use cases?
Why does readiness matter?
Looking across these dimensions matters because weakness in one area can limit progress in all the others. For example, you might have access to sophisticated AI technology but will still struggle to generate value if your employees don’t trust it, data isn’t fit for purpose, or governance prevents teams from using it effectively.
You do not need to solve every capability gap before you begin. The purpose of a readiness assessment is to identify the gaps that matter most in the context of your AI ambitions, so you can decide what to address first.
You do not need to solve every capability gap before you begin. The purpose of a readiness assessment is to identify the gaps that matter most in the context of your AI ambitions, so you can decide what to address first.
Establish your AI readiness baseline
At Adaptavist, we believe AI success depends on organisational readiness, not just technical capability. The future of AI in your organisation will not be defined by adoption rates alone, but by whether you can create an environment where people remain confident, capable, and in control of their work.
To do that, you need more than the right technology. You need the right systems, workflows, knowledge, and governance in place. Rather than focusing only on AI implementation, we look at the broader environment that enables AI to deliver meaningful business outcomes — and that starts with understanding your current position.
Our AI Readiness Assessment gives you an evidence-based view of your current level of readiness before you commit to larger transformation programmes, or to sense-check progress in existing initiatives. It assesses critical capabilities across core strategic pillars, giving you far more than a score. You gain clarity on where your organisation stands today, where it needs to get to, and the gaps between the two.
To do that, you need more than the right technology. You need the right systems, workflows, knowledge, and governance in place. Rather than focusing only on AI implementation, we look at the broader environment that enables AI to deliver meaningful business outcomes — and that starts with understanding your current position.
Our AI Readiness Assessment gives you an evidence-based view of your current level of readiness before you commit to larger transformation programmes, or to sense-check progress in existing initiatives. It assesses critical capabilities across core strategic pillars, giving you far more than a score. You gain clarity on where your organisation stands today, where it needs to get to, and the gaps between the two.
Frequently asked questions about AI readiness
How do you know if your organisation is ready for AI?
You can tell whether your organisation is ready for AI by assessing the foundations that support successful adoption. This includes whether you have a clear AI strategy, reliable and accessible data, appropriate governance and risk controls, the right technology environment, and people with the skills and confidence to use AI effectively. An AI readiness assessment helps identify strengths, gaps, and the areas that need attention before scaling investment.
How do you assess AI readiness?
AI readiness is assessed by reviewing the organisational capabilities needed to support AI adoption and value realisation. This usually includes strategy, governance, data, operating model, people and culture, and technology. The purpose of an AI readiness assessment is not just to generate a score, but to establish a baseline, identify gaps, and prioritise the actions needed to support your AI goals. Talk to Adaptavist about our AI Readiness Assessment.
Why is AI readiness important?
AI readiness is important because organisations often invest in AI before the right foundations are in place. Without clear readiness, teams may struggle with poor data quality, unclear governance, low employee confidence, fragmented ownership, or workflows not designed for AI. Understanding AI readiness helps organisations reduce risk, focus investment, and improve their chances of delivering measurable value from AI.
Written by
Senior Strategic Advisor
Attila Bernariusz is a Senior Strategic Advisor at Adaptavist, specialising in AI adoption strategy, agentic workflows, and portfolio management. With a business founder's background, he bridges executive strategy and team execution to design solutions that work as coherent systems.