AI Assessment - Relentica

AI Maturity Assessment

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Strategic Direction and Leadership

Artificial intelligence is not a technology initiative. It is a business capability that requires clear leadership, strategic alignment and executive sponsorship. Organisations that achieve the greatest value from AI have a clear vision, defined ownership and a strong connection between AI investment and business outcomes.

This section contains 5 of the assessment's 28 questions and evaluates how effectively AI is embedded within your strategy, how engaged your leadership team is, and whether the governance and decision-making structures are in place to support successful adoption.

How clearly is AI linked to your business strategy?(Required)

AI is not currently considered within business strategy discussions.

AI is discussed occasionally but without clear objectives or ownership.

AI has been identified as supporting specific business priorities or initiatives.

AI is formally integrated into strategic planning and investment decisions.

AI is a core enabler of growth, competitiveness, efficiency and innovation across the business.

How engaged is your executive leadership team in AI adoption?(Required)

The executive team rarely discusses AI.

Individual leaders show interest, but there is no coordinated approach.

AI is regularly discussed at leadership level and opportunities are being explored.

Executive leaders actively sponsor AI initiatives and monitor progress.

The executive team visibly champions AI and drives adoption across the organisation.

Who owns AI strategy and governance within your organisation?(Required)

No individual or team is accountable for AI.

Ownership exists informally but responsibilities are unclear.

A named leader or function has responsibility for AI.

Formal governance structures and accountability are established.

AI ownership and governance are embedded across leadership, risk and operational functions.

How are AI investments prioritised?(Required)

AI spending occurs without business justification or governance.

Investment decisions are largely opportunistic or vendor driven.

Most AI initiatives require a business case and approval process.

AI investments are prioritised against strategic objectives and expected benefits.

AI investments are actively managed as a portfolio with ongoing value tracking.

How well does your organisation measure AI success?(Required)

No measures exist to assess AI performance or value.

Success is measured through activity, pilots or experimentation only.

Operational improvements such as productivity and efficiency are tracked.

Business outcomes such as revenue, margin, customer experience or risk reduction are measured.

AI performance is directly linked to strategic outcomes and board-level reporting.

Data Foundations

The quality of AI outcomes depends heavily on the quality of the data that powers them. Even the most advanced AI tools will struggle to deliver value if data is fragmented, inaccessible or poorly governed.

This section evaluates the readiness of your data landscape, including quality, accessibility, ownership and the organisation's ability to use data as a trusted asset for AI and automation initiatives.

How confident are you in the quality of the data used by your organisation?(Required)

Data quality issues are common and frequently impact decision-making.

Some data quality controls exist, but accuracy and consistency remain a challenge.

Key business data is generally reliable, with occasional quality issues.

Data quality is actively monitored and managed across most business functions.

High-quality, trusted data is consistently available and supports confident decision-making.

How accessible is data across business functions?(Required)

Data is largely siloed and difficult to access.

Some data sharing exists, but significant barriers remain.

Most key business data is accessible to relevant teams.

Data is readily available through common platforms and reporting tools.

Data is easily discoverable, accessible and leveraged across the organisation.

How well are data ownership and accountability defined?(Required)

No formal ownership exists for key business data.

Some ownership exists but responsibilities are unclear.

Key data sets have identified owners and responsibilities.

Data ownership is well understood and actively managed.

Data ownership, stewardship and accountability are embedded throughout the organisation.

How prepared is your data estate for AI and automation initiatives?(Required)

Data is fragmented, inconsistent and unsuitable for AI use.

Significant data preparation would be required before AI adoption.

Some data sources are suitable for AI initiatives.

Technology Readiness

Modern technology platforms provide the foundation for scalable AI adoption. Organisations with flexible, integrated and well-managed technology environments are better positioned to deploy AI solutions quickly and effectively.

This section examines the maturity of your technology estate, including infrastructure, integration capability, automation and the ability to support future AI innovation.

How modern and scalable is your technology landscape?(Required)

Legacy systems significantly constrain business agility.

Some modernisation has occurred, but key limitations remain.

Core platforms support current business needs adequately.

Technology platforms are modern, scalable and regularly updated.

The technology landscape is highly scalable, resilient and supports rapid innovation.

How easily can AI tools integrate with your existing platforms?(Required)

Existing systems make AI integration extremely difficult.

AI integration is possible but requires significant effort.

Integration is achievable for selected use cases.

Most platforms support straightforward AI integration.

The technology architecture is designed to enable rapid AI integration and scaling.

How automated are your core business processes today?(Required)

Most processes remain manual.

Limited automation exists within isolated functions.

Several key processes are partially automated.

Automation is widely deployed across business operations.

Automation is embedded throughout the organisation and continuously optimised.

Automation is embedded throughout the organisation and continuously optimised.(Required)

Infrastructure is largely on-premise and difficult to scale.

Early cloud adoption has begun.

Core workloads operate on modern cloud platforms.

Cloud capabilities are well established and actively managed.

Cloud infrastructure is highly mature, scalable and supports advanced AI workloads.

Governance, Risk and Trust

As AI adoption increases, so does the importance of governance, security, privacy and ethical oversight. Organisations must ensure that AI is deployed responsibly, transparently and in line with regulatory expectations.

This section assesses how effectively your organisation manages AI-related risks and whether appropriate governance structures, controls and assurance mechanisms are in place.

How clearly are AI risks identified and managed?(Required)

AI risks have not been formally assessed.

Risks are recognised but managed inconsistently.

AI risks are assessed for major initiatives.

Formal risk assessment processes exist for AI projects

AI risk management is embedded within enterprise risk processes.

How mature are your AI governance policies and controls?(Required)

No AI policies or governance controls exist.

Initial policies are being considered or drafted.

Basic governance controls are in place.

Comprehensive governance policies guide AI adoption.

Governance is actively monitored, reviewed and continuously improved.

How well do you address privacy, security and compliance requirements for AI?(Required)

Privacy, security and compliance considerations are largely absent.

Consideration occurs inconsistently.

Key requirements are addressed for major initiatives.

Security, privacy and compliance reviews are standard practice.

Robust controls and assurance processes are embedded across all AI activity.

How confident are you that AI use is ethical and transparent?(Required)

Ethical implications are not considered.

Ethical concerns are discussed informally.

Ethical principles have been defined.

Ethical review forms part of AI decision-making.

Ethical and transparent AI practices are embedded across the organisation.

People and Capability

Technology alone does not deliver transformation. Success depends on people understanding, adopting and applying AI effectively within their roles. Building confidence, skills and organisational capability is critical to achieving sustainable outcomes.

This section evaluates workforce readiness, leadership engagement, skills development and the organisation's ability to build and maintain AI capability.

How well does your workforce understand AI opportunities and risks?(Required)

Most employees have little awareness of AI.

Awareness is growing but understanding remains limited.

Employees have a basic understanding of AI opportunities and risks.

AI literacy is well established across most teams.

AI understanding is embedded throughout the organisation.

How strong are your AI and data skills across the organisation?(Required)

Significant skills gaps exist.

Limited specialist capability is available.

Some AI and data skills are present within key teams.

Strong capability exists across multiple functions.

AI and data expertise are recognised organisational strengths.

How actively are leaders promoting AI adoption and learning?(Required)

Leaders rarely discuss AI.

Support varies significantly between leaders.

Leaders encourage exploration and learning.

Leaders actively promote AI adoption and capability building.

Leadership consistently drives AI learning, innovation and change.

How effective are your training and upskilling programmes for AI?(Required)

No AI training programmes exist.

Limited or ad-hoc training is available.

Structured training exists for selected groups.

AI learning programmes are widely available.

Continuous AI learning and development are embedded in the organisation.

Execution and Adoption

Many organisations generate excitement around AI but struggle to convert ideas into practical outcomes. Successful AI adoption requires effective delivery, strong collaboration and the ability to scale proven solutions.

This section explores how effectively your organisation delivers AI initiatives, engages business stakeholders and embeds successful solutions into day-to-day operations.

How effectively does your organisation move AI ideas into production?(Required)

Most AI ideas never progress beyond discussion.

Pilot projects occur but rarely scale.

Some AI solutions reach production successfully.

AI initiatives are routinely delivered into production.

The organisation consistently turns AI opportunities into measurable business outcomes.

How consistently are AI initiatives delivered and adopted?(Required)

AI projects frequently stall or fail.

Delivery success is inconsistent.

Many initiatives achieve their intended outcomes.

AI projects are delivered consistently with strong adoption.

AI delivery and adoption are repeatable organisational capabilities.

How well are business teams involved in AI projects?(Required)

AI projects are primarily technology-led.

Business involvement is limited.

Business stakeholders participate in key projects.

Business and technology teams collaborate closely.

Cross-functional ownership drives AI initiatives from idea through value realisation.

How quickly can your organisation scale successful AI solutions?(Required)

Scaling AI solutions is extremely difficult.

Scaling requires significant effort and time.

Successful solutions can be expanded selectively.

AI capabilities can be scaled across multiple functions.

The organisation rapidly scales successful AI solutions enterprise-wide.

Business Value and Outcomes

AI should deliver measurable business benefits, not simply create activity. Organisations that achieve lasting success focus on outcomes such as growth, efficiency, customer experience and risk reduction.

This section assesses how effectively your organisation defines, measures and realises value from AI investments and whether those benefits are linked to strategic objectives.

How clearly are AI benefits defined before investment?(Required)

Benefits are rarely defined.

Benefits are discussed informally.

Most initiatives have defined objectives and expected outcomes.

Benefits are quantified and included within business cases.

Benefit targets are clearly linked to strategic and financial outcomes.

How consistently are AI outcomes measured after implementation?(Required)

Outcomes are not measured.

Measurement is inconsistent.

Major initiatives have defined success metrics.

Benefits tracking is standard practice.

Outcomes are continuously monitored and used to improve investment decisions.

How much business value has AI delivered so far?(Required)

No measurable value has been delivered.

Limited value has been realised through isolated initiatives.

AI has delivered measurable benefits in selected areas.

AI is generating meaningful business value across multiple functions.

AI is delivering significant, measurable impact on growth, efficiency, customer experience or risk reduction.

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Your report will highlight your organisation's strengths, identify priority areas for improvement and provide guidance on the next steps to accelerate AI adoption and business value.

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