This maturity matrix has been devised for Adult Social Care AI maturity in Councils in the United Kingdom.
This grid has been produced to enable you to help define where you sit against these key areas of AI maturity for Adult Social Care in the UK as a Council provider.
This aid can help you to determine interventions you may need next to help you move your AI maturity forward for your team, organisation or Council.
The suggested approach is to download the full matrix and have a conversation with your team on where you are, where you need to be and what you need to do to get to the next level of maturity that you are aiming for.
There is a link to download the file at the end of the section for you to use.
Here is a one-page precis, followed by the whole matrix in longer form.
NW ADASS Maturity Matrix for AI in Adult Social Care Please see the matrix here and downloadable Excel and pdf files further below.
| Dimensions | Exploration | Experimentation | Incorporation | Optimisation | Continuous Business Agility |
|---|---|---|---|---|---|
| Leadership | Leadership in the council is starting to recognise AI's potential to improve Adult Social Care, but there is minimal understanding of how it can be practically applied. | Council leaders support small-scale AI projects in Adult Social Care, but these are experimental and not connected to wider strategies or outcomes. | Leaders provide clear direction for AI initiatives, linking AI to strategic Adult Social Care goals, with sponsorship and commitment from senior management. | Leadership actively promotes AI-driven projects to enhance Adult Social Care, making AI a cornerstone of the council's strategy for service improvement and resource efficiency. | Leadership sees AI as essential for ongoing transformation in Adult Social Care, adapting quickly to new AI opportunities to improve outcomes for vulnerable populations. |
| Culture | Staff in Adult Social Care are aware of AI but are cautious about adopting it, with many viewing it as a distant possibility rather than an immediate tool for service delivery. | Teams within Adult Social Care are becoming more open to AI-driven experiments, but there is uneven acceptance across departments, and many still rely on traditional methods. | A growing number of staff embrace AI's potential to improve services, with cross-department collaboration to integrate AI into care assessments and decision-making processes. | The council's Adult Social Care workforce increasingly values AI for streamlining operations and improving care outcomes, integrating it into routine decision-making and service delivery. | AI is embedded into the culture of Adult Social Care, with staff continuously exploring and adopting AI tools to enhance service responsiveness and adapt to changing care needs. |
| Governance | There are no formal AI governance structures in Adult Social Care. Concerns about ethics, data privacy, and AI's impact on care are acknowledged but not addressed in policy. | Basic governance structures are starting to form, guiding AI experiments within Adult Social Care. Ethical and regulatory discussions are beginning, but they lack formal oversight. | Formal governance policies are in place to regulate the use of AI in Adult Social Care, ensuring compliance with ethical standards, data privacy laws, and safeguarding policies. | Adult Social Care AI models are regularly audited for fairness, transparency, and compliance with care regulations. Governance structures ensure that AI aligns with ethical care principles. | Governance frameworks in Adult Social Care are dynamic, continuously adapting to new ethical, legal, and technological developments in AI, ensuring robust oversight of AI-driven care. |
| Strategy | There is no formal AI strategy for Adult Social Care. AI is seen as a potential future enhancement, but there are no concrete plans or defined goals. | An initial AI strategy is emerging, focusing on isolated use cases in Adult Social Care, such as predictive analytics for care planning or resource allocation. | AI is integrated into the broader Adult Social Care strategy, aligned with long-term goals such as improving care outcomes, increasing efficiency, and addressing resource gaps. | AI is central to the Adult Social Care strategy, driving innovation and improving service delivery. AI tools are used to support predictive care planning, optimise resources, and improve the quality of life for people with lived experience. | The AI strategy in Adult Social Care is continuously updated, with the council regularly refining its approach to maintain agility and respond to changing care needs and technology advancements. |
| Insight | Insight capabilities are minimal, and decision-making is largely reactive, based on historical data. There is little systematic use of data for driving improvements in Adult Social Care services. | The council begins to experiment with structured data analysis and basic insights through isolated projects. Predictive analytics may be tested, but data and insights remain fragmented. | Insights from data are integrated into decision-making processes across Adult Social Care. Predictive analytics inform care planning, resource allocation, and risk assessments. | Advanced AI and analytics are used to generate insights that improve service delivery and personalisation. Real-time data enables dynamic insights for proactive care management. | Insight generation is fully embedded, driving continuous improvements in care delivery. Advanced AI models help anticipate care needs, with insights shared across departments and partners to create a responsive and adaptive Adult Social Care system. |
| Data Management | Data relevant to Adult Social Care is siloed and often incomplete, making it difficult to use for AI initiatives. Data governance and quality control are minimal. | Efforts to clean and organise Adult Social Care data have begun. Data is more structured, allowing for initial AI projects, but integration across departments is limited. | Data management practices are well established, with structured, clean data accessible across Adult Social Care services. This enables AI projects to scale effectively, with improvements in care outcomes and service planning. | Advanced data management systems allow for real-time data sharing across Adult Social Care services. Data quality and accessibility are optimised to support AI-driven insights and efficient service delivery. | Data management is highly agile, with seamless data sharing across departments and external partners. Data pipelines adapt to new AI demands, enabling real-time insights for care delivery and planning. |
| People Capability | There is limited understanding or expertise in AI within Adult Social Care teams. Staff rely on external advisors or experts for guidance on AI-related initiatives. | Some staff within Adult Social Care have begun to develop AI skills through experimentation, but these capabilities are not widespread or standardised across the council. | The council provides structured training to Adult Social Care staff to build AI skills, with dedicated teams or roles emerging to oversee AI initiatives and their integration into care services. | AI skills are prevalent across Adult Social Care teams, with staff trained to use AI tools to improve care delivery, identify risks, and optimise resources. Centres of AI excellence may be established within the council. | The Adult Social Care workforce is AI-savvy, with continuous professional development programmes in place to keep staff at the forefront of AI advancements. AI capability is seen as a core skill for all staff. |
| Processes | AI is not considered in Adult Social Care processes, which remain manual and siloed, relying heavily on traditional methods for case management and care delivery. | Early AI-driven pilots are being integrated into a few Adult Social Care processes, such as automating routine tasks or using AI for assessments, but these are isolated. | AI is increasingly embedded into core Adult Social Care processes, automating tasks such as risk assessments, care planning, and resource management to improve efficiency and outcomes. | AI-driven automation is optimising Adult Social Care processes across departments. Predictive and prescriptive analytics are used to streamline workflows, enhance care personalisation, and better allocate resources. | Processes in Adult Social Care are continuously redesigned and optimised based on AI-driven insights. The council adapts quickly to changing care demands through process automation and AI-enabled decision-making. |
| Technology Infrastructure | Legacy systems dominate Adult Social Care IT infrastructure, with little support for AI initiatives. There is no integration between data systems and emerging AI technologies. | The council introduces basic AI tools and platforms, such as cloud-based analytics for Adult Social Care, but these are still isolated and not integrated into a cohesive infrastructure. | Scalable and secure AI infrastructure is established, with cloud computing, data storage, and AI platforms integrated to support AI-driven solutions across Adult Social Care services. | Advanced AI tools and platforms are optimised for speed, scalability, and security, enabling seamless deployment of AI solutions that improve care delivery and resource management in Adult Social Care. | The AI infrastructure supporting Adult Social Care is highly adaptive, with real-time data processing, advanced analytics, and continuous scalability to support future innovations and challenges in care delivery. |
To download the Maturity Matrix to use for your team, please click below:
NW ADASS Maturity Matrix for AI in Adult Social Care.pdf
NW ADASS Maturity Matrix for AI in Adult Social Care.xlsx
NW ADASS High-Level Maturity Matrix for AI in Adult Social Care.pdf