Basic AI Maturity Assessment

Curious about what insights you’ll gain from our Data & AI Maturity Assessment? Complete our Basic AI Maturity Assessment to get a high-level indication of your current maturity level. The basic assessment briefly covers all six key pillars that the Data & AI Maturity Assessment is built on. Upon completion you will receive an initial quantitative metric of your maturity and an indication of which key areas are stronger than others.

Step 1 of 3

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About You

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What is the overall level of ambition with respect to AI in your organisation?

We aim to have a basic level of AI literacy within the organisation to meet the most essential business needs (e.g. keeping our business model risk-proof from developments in AI).

We are working towards building foundational AI knowledge and capabilities, e.g. using AI to automate routine tasks or improve data analytics.

We aim to be highly proficient in AI, using it extensively to drive business outcomes.

We aim to be a leader in AI, developing and commercialising our own AI solutions.

Strategy

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How aligned is your organisation's data and AI strategy with the organisation's overall strategy?

How aligned is your organisation's data and AI strategy with the organisation's overall strategy?(Required)

The organisation has neither a data and AI strategy or an overall strategy.

The organisation has an overall strategy but doesn’t have a data and AI strategy.

The data and AI strategy was developed independently from the business, with minimal alignment to the wider business strategy.

The data and AI strategy broadly fits into the organisation’s wider strategy, but the business has limited visibility of, or input into, specific initiatives.

The data and AI strategy is developed jointly by the technical and business teams, and is fully integrated into the organisation’s wider strategy.

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Answer 1

Data

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How would you rate the quality of data in your organisation considering the organisation's needs?

How would you rate the quality of data in your organisation considering the organisation's needs?(Required)

Data quality issues are highly prevalent and impact our work and delivery substantially (e.g. delays to analytics projects, rescoping, disproportional engineering resource allocation to resolving data quality issues, cost overrun, failure to deploy, performance degradation).

We have resolved a number of our data quality issues, though data remains a bottleneck.

We have a good handle on data quality and have minimised data quality issues.

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Answer 2

Infrastructure

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What is your organisation's approach to productionising AI use cases?

What is your organisation's approach to productionising AI use cases?(Required)

We primarily experiment and build non-productionised, proof-of-concept models.

We build data and AI solutions / MVPs that employ some software engineering best practices.

We build solutions that apply software engineering best practices and some DevOps and MLOps principles, and are partially automated.

We build fully productionised data and AI solutions. We follow software engineering best practices, facilitate efficiency and reusability, implement standards and protocols, and follow an automation-first approach. DevOps and MLOps is used throughout the lifecycle to automate the building, testing, and deployment of data and AI. We monitor model performance in an automated fashion. Our solutions run autonomously across the business and are integrated with other systems.

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How would you rate the maturity level of your organisation's AI technology?

How would you rate the maturity level of your organisation's AI technology?(Required)

Low: Our data and AI technology is dated and has limited flexibility, e.g., traditional non-cloud infrastructure set-up, disparate database management systems with separate relational analytical repositories.

Moderate: Some advancements in the data and AI technology have been made, e.g., a base data lake with analytics sandboxes, cloud computing.

High: Advanced data and AI technology is in use.

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Answer 3

Risk

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Is there clear ownership of, and responsibility for, data and AI regulatory compliance in your organisation?

Is there clear ownership of, and responsibility for, data and AI regulatory compliance in your organisation?(Required)

There is no clear ownership or responsibility for data and AI regulatory compliance.

There is some recognition of the need for compliance, but ownership is vague or scattered across various departments.

Clear ownership and accountability for data and AI regulatory compliance are established, with designated roles and responsibilities.

Data and AI regulatory compliance is fully integrated into the organisational structure and culture, with ongoing oversight and continuous improvement.

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Does your organisation have governance in place around the use of data and AI, in particular advanced analytics and generative AI tools?

Does your organisation have governance in place around the use of data and AI, in particular advanced analytics and generative AI tools?(Required)

There is no formal governance or policies for the use of data and AI tools. Awareness of potential risks is low, and use is largely unregulated.

Basic governance frameworks are being developed. Some policies may exist, but they are not consistently enforced or widely understood.

A comprehensive governance framework is in place for the use of data and AI tools. Policies are well-defined and communicated to employees.

Governance around AI is fully integrated into the organisation's culture and operational processes. Policies are continually evaluated and adapted.

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Answer 4

People

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How well does your organisation support employees in developing the skills needed to use data and AI solutions?

How well does your organisation support employees in developing the skills needed to use data and AI solutions?(Required)

There is no formal support in place.

There is some form of support in place, but it is not (yet) fully structured or complete.

We proactively provide consistent and structured support to users who are interested in improving their skills as users of data and AI solutions.

We strongly prioritise skill development in the use of data and AI solutions, and offer regular training programs, mentoring, and upskilling initiatives with clear pathways for employees to become proficient users.

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In your view, is the use / adoption level of AI in your company approproate considering the organisation's needs?

In your view, is the use / adoption level of AI in your company approproate considering the organisation's needs?(Required)

We are far from where we need to be with respect to adoption of data and AI considering our needs and ambitions.

Our adoption of data and AI is satisfactory but there is room for improvement.

Yes, considering our needs and ambitions we are exactly where we need to be.

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How suitable is the above skill level of your data and AI specialists considering your organisation's needs?

How suitable is the above skill level of your data and AI specialists considering your organisation's needs?(Required)

There are significant gaps between the current skill level and what our organisation needs.

We can meet most of our goals in a satisfactory way with the current skillset, though some upskilling and hiring would still be needed.

The skill level of our data and AI specialists is suitable considering our needs and ambitions.

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Answer 5

Operating

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What is your organisation's approach to standardising the AI lifecycle?

What is your organisation's approach to standardising the AI lifecycle?(Required)

We do not manage the data and AI lifecycle in a standardised way.

We standardise some parts of the data and AI lifecycle (e.g. data ingestion), but not others.

We are starting to standardise the entire data and AI lifecycle, from design to deployment, but this is not yet fully implemented across our use cases.

All data and AI use cases follow a standard design, development, and deployment pattern. We follow clearly defined protocols that outline how we approach new projects, ingest data, engineer features, build, test, deploy, and monitor models.

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Answer 6

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Score