Assurance management tool with artificial intelligence integration

Abstract

An apparatus 10 provides an assurance management tool 12 for implementing 10 an assurance review 14 of an initiative 16 or operation 18. The apparatus 10 comprises training means 20 for training an ar

Description

TECHNICAL FIELD The present disclosure relates generally to an assurance management tool, and more particularly to an assurance management tool with artificial intelligence (AI) integration. TECHNICAL BACKGROUND Assurance management involves evaluating and verifying the effectiveness, reliability, and integrity of systems, processes, controls, or information in relation to initiatives or operations to ensure that they align with established standards, regulations, or expectations. The primary goal of assurance management is to provide confidence to stakeholders, such as investors, government, regulators, customers, or management, that an organisation's initiatives or operations are conducted in a manner that is transparent, accountable, and in accordance with relevant standards and objectives. There are significant inconsistencies and inefficiencies of assurance reviews, which increases costs by eroding stakeholder confidence and jeopardising the success of initiatives or operations. This significantly burdens government bodies and enterprises, particularly affecting those involved in large-scale initiatives where inefficiencies lead to substantial financial and temporal losses they cannot afford. There is, therefore, a need for improved assurance management tools. This is particularly relevant for large enterprises and government organisations which often deal with vast amounts of data, complex workflows, and industry-wide compliance requirements. SUMMARY OF THE DISCLOSURE According to a first aspect of the present disclosure, there is provided an apparatus, wherein the apparatus is an assurance management tool for implementing an assurance review of an initiative or operation, wherein the apparatus comprises: training means for training an artificial neural network based on a data set, wherein the data set includes at least metrics and / or performance indicators in relation to the initiative or operation; and assistance means for providing assistance by the trained artificial neural network in relation to the initiative or operation, or in relation to the assurance review. The apparatus provides an improved assurance management tool by virtue of the assistance means providing assistance by the trained artificial neural network in relation to an initiative (which may be, or relate to, a project, program, portfolio, or organisation) or operation (for example, a business operation), or in relation to an assurance review for such an initiative or operation. Accordingly, assurance reviews and processes are more efficient and insightful for users because any inconsistencies and inefficiencies are removed. This is particularly the case for large enterprises and government organisations which often deal with vast amounts of data, complex workflows, and industry-wide compliance requirements. The apparatus streamlines the assurance review process of initiatives or operations, and in relation to such initiatives or operations, drives productivity and operational efficiency, fosters collaboration, and facilitates compliance with industry requirements. Furthermore, operational costs are reduced, and decision-making capabilities are improved. This provides enterprises the ability to stay competitive in a rapidly evolving market. Possibly, the assistance means provides assistance by the trained artificial neural network by providing recommendations and / or guidance. Possibly, the assistance means provides assistance by the trained artificial neural network by providing recommendations and / or guidance in relation to any steps of a process flow of a framework process for developing an assurance review of an initiative or operation, and / or in relation to any implementation steps of the process flow. Possibly, a recommendation by the assistance means is a suggestion that something would be good or suitable for a particular job or purpose, or a suggestion that a particular action should be done or not done. Possibly, the assistance means provides assistance by the trained artificial neural network by recommending how to improve an initiative or operation, or by recommending how to improve an assurance review. Possibly, the assistance means provide assistance by the trained artificial neural network by recommending a particular framework, where the framework recommended depends on user actions, responses to questions, and / or depends on type of industry in relation to the initiative or operation. Possibly, the assistance means provides assistance by the trained artificial neural network by recommending an assurance review strategy and plan based on inputs from the user and historical datasets, e.g., what type of review is needed, when is the review needed, and which individuals will need to be involved. Possibly, guidance by the assistance means is help and advice about how to do something or about how to deal with a problem identified by a user or the assistance means. Possibly, the assistance means provides assistance by the trained artificial neural network by providing guidance based on identifying where there is scope creep and / or projected delays in meeting milestones. Possibly, the assistance means provides assistance by the trained artificial neural network by generating documents based on user actions, responses to questions, or type of industry in relation to the initiative or operation. Documents generated may be templates comprising different categories and measures. Documents generated may include an executive summary, lessons learned, findings, and action items using content from interviews, review measures, documents and observations inputted by a user. The assistance means may provide assistance by the trained artificial neural network by capturing experiences. The assistance means may provide assistance by the trained artificial neural network by presenting information. The assistance means may provide assistance by the trained artificial neural network by action tracking. The assistance means may provide assistance by the trained artificial neural network by file sharing. The assistance means may provide assistance by the trained artificial neural network by automating tasks. The assistance means may provide assistance by the trained artificial neural network by providing analytics. The analytics may be predictive analytics. The assistance means may provide assistance by the trained artificial neural network by providing identification, including identifying trends, anomalies, and patterns in data. Possibly, the assistance means provides assistance by the trained artificial neural network by optimising resource allocation, wherein resource allocation is optimised by suggesting reallocation of machinery. This reduces project duration and improves machinery utilisation, for example in relation to road construction. Possibly, the assistance means provides assistance by the trained artificial neural network based on continuous process improvement, wherein continuous process improvement is by identifying adjustments to machine calibration schedules based on performance trends. This improves efficiency and reduces waste. The assistance means may provide assistance by the trained artificial neural network in relation to the initiative or operation and in relation to the assurance review. According to a second aspect of the present disclosure, there is provided a method for implementing an assurance review of an initiative or operation by an assurance management tool, the method comprising: training an artificial neural network by training means based on a data set, wherein the data set includes at least metrics and / or performance indicators in relation to the initiative or operation; and providing assistance by assistance means based on the trained artificial neural network in relation to the initiative or operation, or in relation to the assurance review. According to a third aspect of the present disclosure, there is provided a computer program for implementing an assurance review of an initiative or operation by an assurance management tool, the computer program comprising instructions which, when executed by an apparatus, cause the apparatus to perform at least: training an artificial neural network by training means based on a data set, wherein the data set includes at least metrics and / or performance indicators in relation to the initiative or operation; and providing assistance by assistance means based on the trained artificial neural network in relation to the initiative or operation, or in relation to the assurance review. BRIEF DESCRIPTION OF THE DRAWINGS Some examples will now be described with reference to the accompanying drawing in which: Figure lisa diagrammatic view of an apparatus according to examples of the disclosure; and Figure 2 is a diagrammatic view of another apparatus according to examples of the disclosure. DETAILED DESCRIPTION The present disclosure will now be described by way of example only and with reference to the accompanying drawings. Referring initially to Figure 1, examples of the disclosure provide an apparatus 10. The apparatus 10 is an assurance management tool 12 for implementing an assurance review 14 of an initiative 16 or operation 18. Accordingly, the assurance management tool 12 can be used to carry out or accomplishing an assurance review 14 of an initiative 16 or operation 18. Assurance management involves evaluating and verifying the effectiveness, reliability, and integrity of systems, processes, controls, or information in relation to initiatives 16 or operations 18 to ensure that they align with established standards, regulations, or expectations. The primary goal of assurance management is to provide confidence to stakeholders, such as investors, government, regulators, customers, or management, that an organisation's initiatives 16 or operations 18 are conducted in a manner that is transparent, accountable, and in accordance with relevant standards and objectives. Initiatives 16 are typically unique and temporary (with a definitive beginning and ending), while operations 18 are typically ongoing and permanent. In more detail, an initiative 16 is a temporary endeavour undertaken to create a unique product, service, or result, for example in relation to a project, program, portfolio, or organisation. Operations 18 are ongoing executions of activities that produce the same output or repetitive service. Operations 18 are typically used to run regular business models, achieve the goals, and support the business, i.e., business operations. The apparatus 10 comprises training means 20 for training an artificial neural network 22 based on a data set 24. The data set 24 includes at least metrics 26 and / or performance indicators 28 in relation to the initiative 16 or operation 18. An example training data set 24 is described in further detail below. The trained artificial neural network 22 is based on machine learning (ML). Machine learning is a subset of artificial intelligence (AI) that automatically enables a machine or system to learn and improve from experience. Instead of explicit programming, machine learning uses algorithms to analyse large amounts of data from the data set 24, learn from insights, and then make informed decisions. Accordingly, the apparatus 10 according to examples of the disclosure provides an artificial intelligence (AI) system or model based on the artificial neural network 22. The artificial neural network 22 is trained using relevant data sets 24 and its performance is validated through rigorous testing ensuring models are robust, accurate, and capable of handling real-world scenarios. The artificial neural network 22 is trained using learning approaches such as supervised learning, unsupervised learning, and reinforcement learning or using conditioning approaches such as fine-tuning or prompting. In some examples, the artificial neural network 22 is further trained by the training means 20 based on information inputted by a user of the apparatus 10, in addition to the data set 24. Training of the artificial neural network 22 can therefore be a continual process impacted by users of the apparatus 10 and the information inputted by such users in relation to an initiative 16 or operation 18, or in relation to an assurance review 14 or such an initiative 16 or operation 18. The apparatus 10 therefore provides an assurance management tool 12 comprising integrated artificial intelligence. Metrics 26 are quantitative measurements used to track specific activities in relation to an initiative 16 or operation 18. Examples of metrics 26 include (or relate to) client, scope, governance, risks, supply chain, workflow efficiency, bottleneck identification, communication effectiveness, and stakeholder satisfaction etc. Anything that is measurable, quantifiable, and comparable to previous measurements can be a metric 26. Performance indicators 28 are targets of performance or progress based on specific longer-term goals and objectives in relation to an initiative 16 or operation 18. Examples of performance indicators 28 include (or relate to) time to completion, budget adherence, resource utilisation, risk management effectiveness, and quality of deliverables. Performance indicators 28 can also be used to track progress towards other goals and objectives. A performance indicator 28 typically has a defined target or specific goal that can be worked towards. The apparatus 10 further comprises assistance means 30 for providing assistance 32 by the trained artificial neural network 22 in relation to the initiative 16 or operation 18, or in relation to the assurance review 14. The apparatus 10 provides an improved assurance management tool 12 by virtue of the assistance means 30 providing assistance 32 by the trained artificial neural network 22 in relation to an initiative 16 (which may be, or relate to, a project, program, portfolio, or organisation) or operation 18 (for example, a business operation), or in relation to an assurance review 14 for such an initiative 16 or operation 18. In some examples, the assistance means 30 provides assistance 32 by the trained artificial neural network 22 both in relation to the initiative 16 or operation 18 and in relation to the assurance review 14. Accordingly, assurance reviews 14 and processes are more efficient and insightful for users because any inconsistencies and inefficiencies are removed. This is particularly the case for large enterprises and government organisations which often deal with vast amounts of data, complex workflows, and industry-wide compliance requirements, simplifies and enhances. Accordingly, the apparatus 10 improves the delivery success rates of initiatives such as projects, and thus addresses the problem that globally projects fail more often than not. The apparatus 10 streamlines the assurance review process of initiatives 16 or operations 18, and in relation to such initiatives 16 or operations 18, drives productivity and operational efficiency, fosters collaboration, and facilitates compliance with industry requirements. Furthermore, operational costs are reduced, and decision-making capabilities are improved. This provides enterprises the ability to stay competitive in a rapidly evolving market. The trained artificial neural network 22 can assist a user by evaluating and verifying the effectiveness, reliability, and integrity of systems, processes, controls, or information in relation to initiatives 16 or operations 18 to ensure that they align with established standards, regulations, or expectations. Referring to Figure 2, in some examples the assurance management tool 12 comprises a user interface 34 that a user sees and interacts with. The user interface 34 includes features that allow the user to input data and includes areas where the user will see on-screen output. Accordingly, the user interface 34 is the front-end application view to which the user interacts to use the assurance management tool 12. The user interface 34 comprises a number of navigation components 36, i.e., tabs or navigation panels, some of which act as the highest-order sections or categories, for example: ‘Home’, ‘Initiatives’, ‘Reviews’, ‘Templates’ 62, and ‘Frameworks’ 60. Such navigation components 36 are purposefully broad so that a user can gradually channel into the user interface 34 to access more specific content without having to scan all of the available content at once. Other navigation components 36 are more specific, such as ‘Dashboard’, ‘Interviews’, ‘Measures’, ‘Observations’, ‘Findings’, and ‘Action Items’. The user interface 34 further comprises a number of informational components 38. The informational components 38 may be accessed via one or more of the navigational components 36. The informational components 38 may include helpful hints and tips on good initiative or operation management. For example, a high-level guide to a standard initiative or operation management approach may be provided noting elements that make for a good assurance review 14 of an initiative 16 or operation 18. An example standard initiative or operation management approach may include the following elements: 1. Governance 2. Scope and Objectives 3. Business Case 4. Stakeholders 5. Charter 6. Funding 7. Planning and Review 8. Implement 9. Performance 10. Risk 11. Communication 12. Document In some examples, the assurance management tool 12 comprises a framework process 40 for developing an assurance review 14 of an initiative 16 or operation 18. The framework process 40 may be accessed via a navigational component 36. An example framework process 40 may have the following process flow 42: 1. Review Frameworks 60 2. Customise Templates 62 3. Create Initiative 4. Conduct Review 5. Report and Take Action A process flow 42 may be defined by the following implementation steps 44: 1. Look through Frameworks 60 2. Create your initiative and begin review 3. Custom make template 62 with added methodologies 4. Add URLs to documents (Documents Tab) 5. Set when your interviews will be and with whom (Interviews Tab) 6. Review all the noted observations (Observations tab) 7. Change the score of your chosen framework template 62 (Measures Tab) 8. Formal view of your observations (Findings Tab) 9. All actions need to be derived from a finding (Actions Tab) 10. Look at your review stats (Review Dashboard) The assistance means 30 can provide assistance 32 by the trained artificial neural network 22 by providing recommendations 46 and / or guidance 48. For example, the assistance means 30 can provide assistance 32 by the trained artificial neural network 22 by providing recommendations 46 and / or guidance 48 in relation to any of the steps in a process flow 42 (1 to 5 above) of a framework process 40 for developing an assurance review 14 of an initiative 16 or operation 18, and / or in relation to any of the implementation steps 44 underpinning the process flow 42 (1 to 10 above). The recommendations 46 and / or guidance 48 provided can be personalised. The recommendations 46 and / or guidance 48 provided is targeted and actionable. A recommendation 46 by the assistance means 30 is a suggestion that something would be good or suitable for a particular job or purpose, or a suggestion that a particular action should be done or not done, i.e., advocating a particular item or course of action. For example, the assistance means 30 can provide assistance 32 by the trained artificial neural network 22 by recommending 46 how to improve an initiative 16 or operation 18, or by recommending 46 how to improve an assurance review 14. Furthermore, the assistance means 30 can provide assistance 32 by the trained artificial neural network 22 by recommending 46 a particular framework process 40 depending on industry, e.g., Governance for critical infrastructure organisations, or Gov policies for Gov Departments. In some examples, the assistance means 30 provides assistance 32 by the trained artificial neural network 22 by recommending 46 an assurance review strategy and plan based on inputs from a user and historical datasets, e.g. what type of review is needed, when and which individuals need to be involved. Guidance 48 by the assistance means 30 is help and advice about how to do something or about how to deal with a problem identified by the user or the assistance means 30. For example, the assistance means 30 can provide assistance by the trained artificial neural network 22 by providing guidance 48 based on pinpointing, i.e., identifying, where there is scope creep and / or projected delays in meeting milestones. Scope creep (also called requirement creep, or kitchen sink syndrome) is continuous or uncontrolled growth in an initiative's scope or an operation’s scope. The assistance means 30 may also show a T-minus indicator, e.g., “you have an initiative milestone coming up in 6 months, you should plan your review now and complete it by Month X”. By providing targeted and actionable recommendations 46 and / or guidance 48, the apparatus 10 helps users to implement effective solutions and improve initiative 16 or operation 18 outcomes. The assistance means 30 can provide assistance by the trained artificial neural network 22 by generating documents 50, such as reports, tailored to what is important to a user. The assistance means 30 can provide assistance by the trained artificial neural network 22 by action tracking 52 (for example by indicating, “do now”, “do later”, or “consider doing” in relation to tasks). The assistance means 30 can provide assistance by the trained artificial neural network 22 by action tracking 52 based on presenting, i.e., showing, what type of actions are being held up, e.g., process changes vs technology updates vs people change. The assistance means 30 can provide assistance by the trained artificial neural network 22 by capturing experiences 54, i.e., knowledge or understanding gained by experiences such as lessons learned and actions generated, for ongoing and future initiatives 16 or operations 18. Another of the navigational components 36 may provide a dashboard 56. The dashboard 56 can display or present information, for example, an overview of initiatives 16 or operations 18 and the schedule of reviews 14. Information displayed or presented may include initiative reviews undertaken and a score for each overtime, a wholistic view of the initiative performance, and relevant information such as milestones. The dashboard 56 may also display the status of actions and provide an indication of action completion, including: displays on time, in progress, overtime, and status of actions. The dashboard 56 may also display the total documents to review and / or the total interviews to complete, including displaying those done and those to do. The assistance means 30 can provide assistance by the trained artificial neural network 22 by presenting, i.e., showing, information 58, such as document types and file sizes. Information 58 presented is typically pertinent, i.e., pertains or relates directly and significantly to a matter at hand providing relevant pertinent details. The assistance means 30 can provide assistance by the trained artificial neural network 22 by interpreting, summarising and sharing key points of the information 58 presented. The dashboard 56 may also display framework scores and scores for categories, which may be on the basis of month-to-date (MTD) and / or year-to-date (YTD) measures. The assistance means 30 can provide assistance by the trained artificial neural network 22 by recommending 46 actions to be taken, for example, based on framework scores. Another of the navigational components 36 may provide frameworks 60 that can be used to create templates 62. The assistance means 30 can provide assistance by the trained artificial neural network 22 by recommending 46 which framework 60 is best to use given user responses to key questions. Framework categories can be evaluated by a user. The assistance means 30 can provide assistance by the trained artificial neural network 22 by recommending best categories given user responses to key questions. Templates 62 can be reviewed in terms of categories and measures. The assistance means 30 can provide assistance by the trained artificial neural network 22 by recommending best templates 62 given user responses to key questions. New, i.e., custom templates 64 can be created. The assistance means 30 can provide assistance by the trained artificial neural network 22 by generating documents 50, such as new templates 64 which include categories and measures according to user responses to key questions. The apparatus 10 offers the flexibility for users to create new, i.e., custom templates 64, enabling them to combine criteria from various frameworks 60 like APM and Axelos Prince2. This customisation facilitates a more tailored benchmarking process, aligning with specific initiative 16 or operation 18 needs and organisational standards. Another of the navigational components 36 may provide a new initiatives 66 tab for creating new initiatives. A user may create a new initiative 66 by clicking on the ‘New Initiative’ 66 tab, completing fields and clicking on ‘Create Initiative’. The dashboard 56 displays initiative progress, assigned tasks, and team activities. Gantt charts can be viewed for a timeline perspective of the initiative, with milestones being called out with deadlines. Another of the navigational components 36 may provide a ‘Create Review’ 68 tab for creating new reviews. A user may create a review by completing fields as required, such as: General, Team, Scope, Context, and Objective, and clicking on the ‘Create Review’ tab. To complete an assurance review 14 fields such as: “Documents”, “Interviews”, “Measures”, “Observations”, “Finding”, and “Action Items”, “Summary”, “Report”, and “Lessons Learned”, are completed and the report viewed. Once fields such as documents, interviews, measures and observations are completed the assistance means 30 can provide assistance by the trained artificial neural network 22 by generating documents 50 which include an executive summary, lessons learned, findings, and action items using the content from the above, e.g., from the interviews, documents, review measures, and observations that a user will input. The apparatus 10 further comprises collaboration tools for assigning and scheduling interviews, for example. The assistance means 30 can provide assistance by the trained artificial neural network 22 by action tracking 52, such as assigning action items, and making recommendations 46. In some examples, the assistance means 30 can provide assistance by the trained artificial neural network 22 by carrying out interviews (i.e., Al-led interviews) via a voice call. Accordingly, instead of a human conducting an interview, the interview is conducted by an AI consultant, i.e., by the assistance means 30. The assistance means 30 can provide assistance 32 by the trained artificial neural network 22 by file sharing 70 e.g., by uploading files and attaching files to initiatives and tasks. The assurance management tool 12 further comprises reporting and analytics 76 and data visualisation, for instance, by the use of charts and graphs to visualise progress which may be presented on the dashboard 56. Apparatus 10 according to examples of the disclosure provide automated data collection and integration. Data aggregation is provided by collecting data from various sources such as historical reviews, databases, and spreadsheets, as well as user inputted specific information. The assistance means 30 can provide assistance 32 by the trained artificial neural network 22 by automating tasks 74, such as data collection and integration. Apparatus 10 according to examples of the disclosure provide advanced analytics 76 and reporting. The assistance means 30 can provide assistance 32 by the trained artificial neural network 22 by providing analytics 76, such as predictive analytics 78. Predictive analytics 78 provide predictive insights to anticipate potential issues, areas of concern and generate findings and recommendations. The assistance means 30 can provide assistance 32 by the trained artificial neural network 22 by providing identification 72, for example, identifying trends, anomalies, and patterns in data (scored measures, observations, findings, and overall initiative context). Apparatus 10 according to examples of the disclosure provide customisable dashboards 56 which provide real-time insights and visualisations with customisable options for different users. Apparatus 10 according to examples of the disclosure provide risk assessment and management. Risk scoring automatically scores and prioritises risks based on predefined criteria and AI analysis (i.e., analysis by the trained artificial neural network 22 via the assistance means 30). Scenario analysis simulates different scenarios to understand potential impacts and risk mitigations. Compliance checks ensure compliance with industry standards. Apparatus 10 according to examples of the disclosure provide document and workflow management. Version control maintains and manages multiple versions of documents. Collaboration tools enable team collaboration with features like commenting, task assignments, and change tracking. Workflow automation streamlines review processes with automated workflows, reminders, and approval processes. Apparatus 10 according to examples of the disclosure provide audit trail and transparency. Detailed audit logs keep a comprehensive audit trail of all actions taken within the tool. Transparency provides clear, traceable evidence of how conclusions were reached, and data was processed. Apparatus 10 according to examples of the disclosure provide Natural Language Processing (NLP) in relation the trained artificial neural network 22 via the assistance means 30. Text analysis analyses and extracts insights from unstructured data such as reports, and documents. Automated summaries of large documents are provided by the trained artificial neural network 22 via the assistance means 30 to quickly understand key points. Apparatus 10 according to examples of the disclosure provide integration with assurance standards. Predefined templates 62 include templates 62 and checklists that align with common assurance standards and practices. Customisation / recommendations enable customisation of templates 62 to fit specific organisational needs and standards. Apparatus 10 according to examples of the disclosure provide standards updates. Automatic updates keep the tool 12 updated with the latest industry changes and standards. An alert system notifies users of significant changes that may impact their review processes. Apparatus 10 according to examples of the disclosure provide custom report generation as described above. As described above, reports (i.e., documents 50) tailored to specific needs can be generated, whether for internal review or external stakeholders. Export options provide options to export reports in various formats (PDF, Excel, Word). Apparatus 10 according to examples of the disclosure offer advanced Al-driven functionalities based on the trained artificial neural network 22 via the assistance means 30 that automate routine tasks, provide real-time analytics, and ensure data accuracy. For large enterprises, this means reduced operational costs, improved decision-making capabilities, and the ability to stay competitive in a rapidly evolving market. In some examples of the disclosure, the assurance management tool 12 is provided as Software as a Service (SaaS). The apparatus 10 is built using a robust stack that includes JavaScript for client-side scripting and interactive elements, Ruby on Rails for server-side logic and API integrations, and AWS (Amazon Web Services) for scalable cloud infrastructure, ensuring high availability and reliability. The architecture is designed to handle varying loads, making it suitable for organisations of all sizes. The scalable design allows growth and adaptation to increasing demands of users without compromising on performance. The apparatus 10 includes load balancing, auto-scaling groups, and distributed databases to manage high traffic and large datasets efficiently. The apparatus 10 is built with accessibility at its core, adhering to WCAG 2.1 accessibility guidelines. The platform features a responsive design that ensures seamless use across different devices and screen sizes. It provides alternative text descriptions for visual content, enables keyboard navigation, supports assistive technologies, and undergoes thorough accessibility testing to eliminate any barriers for users with disabilities. The Figures also illustrate a method for implementing an assurance review 14 of an initiative 16 or operation 18 by the assurance management tool 12. The method comprises training the artificial neural network 22 by the training means 20 based on the data set 24. The data set 24 includes at least metrics 26 and / or performance indicators 28 in relation to the initiative 16 or operation 18. The method further comprises providing assistance 32 by the assistance means 30 based on the trained artificial neural network 22 in relation to an initiative 16 or operation 18, or in relation to the assurance review 14. The Figures also illustrate a computer program for implementing an assurance review 14 of an initiative 16 or operation 18 by the assurance management tool 12. The computer program comprises instructions which, when executed by an apparatus 10, cause the apparatus 10 to perform at least the method described above. Examples Example 1: Strategic direction Company A has experienced significant changes over the past few years, including policy shifts, technological advancement, and evolving public expectations. As a result, there is a need to ensure that the organisational structure, processes, and culture are adaptable and aligned with the new strategic direction. The leadership team recognises the importance of a thorough assessment to identify areas that require improvement and to leverage existing strengths effectively. In a previous performance review an overall score of 4.5 was obtained, indicating an overall poor performance. The overall score is based on individual scores in a number of different categories, for example: ‘Governance of initiative delivery’, ‘Finance and commercial’, ‘Leadership and capability’, ‘Planning and control’, ‘Portfolio management’, ‘Programme and initiative management’, ‘Solution delivery’ etc. The assistance means 30 can provide assistance 32 by the trained artificial neural network 22 by recommending 46 actions that need to be taken due to poor performance, i.e., to improve performance in relation to the operation of Company A. The user interface 34 of the assurance management tool 12 comprises a number of navigation components 36, i.e., tabs including ‘Dashboard’, ‘Summary’ (i.e., Executive Summary), ‘Report, and ‘Lessons learned’. The ‘Summary’ tab includes a number of more specific tabs, including: ‘Overview’, ‘Scope’, ‘Objectives’, ‘Team’, and ‘Context’. The ‘Overview’, ‘Scope’, ‘Objectives’, ‘Team’, and ‘Context’ can be completed by a user. The assistance means 30 can provide assistance 32 by the trained artificial neural network 22 by generating documents 50 which include an ‘Executive summary’ and ‘Lessons learned’, ‘Findings’ and ‘Action items’ from the interviews, documents, review measures, and observations inputted by a user. The results will be presented on the dashboard 56. Example 2: Program for Building a New Train Line between London and Manchester Introduction The proposed program aims to develop a high-speed rail line connecting London and Manchester, enhancing the transportation infrastructure between these two major cities in the UK. The project is expected to reduce travel time, increase capacity, and provide a sustainable alternative to road and air travel. The new train line will play a crucial role in boosting economic growth, improving regional connectivity, and contributing to the UK’s long-term transportation strategy. Key Objectives 1. Travel Time Reduction: The new line will reduce the travel time between London and Manchester significantly, enhancing convenience for passengers and promoting economic activities between the two cities. 2. Increased Capacity: The new rail line will provide additional capacity to accommodate growing passenger numbers, alleviating congestion on existing routes. 3. Sustainability: The project is designed with a strong focus on environmental sustainability, incorporating green technologies and minimising carbon emissions throughout construction and operation. 4. Economic Growth: By improving connectivity, the project aims to stimulate economic development in the regions along the route, supporting job creation and investment. 5. Integration with Existing Networks: The new line will be integrated with the existing rail network, ensuring seamless connections, and enhancing the overall transportation system in the UK. Project Scope The program will involve the following key components: • Route Planning and Design: Detailed planning and design of the rail line, including the selection of the optimal route, station locations, and infrastructure requirements. • Land Acquisition and Permissions: Securing the necessary land and permissions, including environmental assessments, community consultations, and negotiations with stakeholders. • Construction: Building the rail infrastructure, including tracks, bridges, tunnels, and stations, with a focus on minimising disruption to existing services and communities. • Technology Integration: Incorporating advanced technology for train control, signaling, and passenger services to ensure the line operates efficiently and safely. • Testing and Commissioning: Rigorous testing of the infrastructure and systems before the line becomes operational, ensuring it meets all safety and performance standards. Timeline and Budget The project is expected to be completed within a 10-year period, with phased milestones for design, construction, and commissioning. The budget for the program will be established based on detailed cost estimates, with provisions for contingencies to address any unforeseen challenges. Assurance Review 14 Overview An assurance review 14 is a critical evaluation process that assesses various aspects of the project to ensure it is on track to meet its objectives. This review typically involves independent experts who examine the project’s progress, risks, management processes, and compliance with regulatory and quality standards. How Assurance Review 14 Improves Productivity • Risk Mitigation: Assurance reviews 14 help identify potential risks early in the project, allowing the project team to address them proactively. This reduces the likelihood of delays and cost overruns, thereby improving overall productivity. • Quality Assurance: The review ensures that the project adheres to quality standards, preventing the need for costly rework or corrections later in the project. Maintaining high quality throughout the project lifecycle contributes to timely and efficient project delivery. • Process Optimisation: By evaluating the efficiency of the project management processes, an assurance review 14 can identify areas for improvement. Streamlining these processes can lead to faster decision-making, better resource allocation, and reduced bottlenecks. • Accountability and Governance: Assurance reviews 14 lenhance transparency and accountability within the project. Regular assessments ensure that project goals are aligned with strategic objectives, and any deviations are promptly addressed, leading to more effective governance. • Stakeholder Confidence: Conducting assurance reviews 14 can boost stakeholder confidence by demonstrating that the project is being managed effectively. This can lead to better support from investors, government bodies, and the public, contributing to smoother project execution. • Innovation and Best Practices: Assurance reviews 13 often bring in external perspectives, which can introduce innovative solutions and best practices. This can lead to the adoption of innovative technologies or methods that enhance productivity and efficiency. How AI, i.e., the assistance means 30 for providing assistance 32 by the trained artificial neural network 22, improves the assurance review 14: Examples of how the AI integrated tool, i.e., the assistance means 30 for providing assistance 32 by the trained artificial neural network 22, improves the assurance review process are outlined below: 1. Predictive Analytics for Risk Mitigation: Analyse vast amounts of project data, including historical project outcomes, to predict potential risks. By identifying patterns and correlations that may not be immediately evident to human reviewers, AI can forecast issues such as cost overruns, delays, or safety concerns before they materialise. This proactive risk mitigation allows the project team to take preventive actions, reducing disruptions and improving productivity. 2. Automated Quality Assurance: Automatically monitor the quality of construction processes in real-time. For example, ML algorithms can analyse images from construction sites to detect deviations from design specifications or identify potential safety hazards. This continuous quality monitoring ensures that problems are identified and rectified immediately, preventing costly rework, and maintaining the project timeline. 3. Process Optimisation through Machine Learning: Assess and optimise project management processes by analysing data on resource allocation, workflow efficiency, and task durations. Machine learning algorithms can suggest adjustments to resource deployment or process steps to enhance efficiency. This optimisation leads to faster decision-making, better resource utilisation, and the elimination of bottlenecks, thereby boosting overall productivity. 4. Enhanced Accountability with Al-driven Audits: Assist in conducting more thorough and frequent audits by automatically tracking project milestones, budget adherence, and compliance with regulatory standards. By comparing real-time data against predefined benchmarks, deviations can be flagged and detailed reports can be generated for the project management team. This increased transparency and accountability help ensure that the project remains aligned with its strategic goals. 5. Improved Stakeholder Communication: Facilitate better communication with stakeholders by providing real-time updates and predictive insights. For instance, Al-driven dashboards can offer a comprehensive view of project progress, risks, and expected outcomes, allowing stakeholders to make informed decisions quickly. This fosters greater confidence and support from investors, government bodies, and the public. 6. Innovation through Al-driven Insights: Introduce innovative solutions by analysing global best practices and emerging technologies in similar infrastructure projects. By leveraging Al's ability to process and learn from vast amounts of data, the project can adopt innovative methods and tools that enhance productivity and efficiency, such as advanced construction techniques or new sustainability practices. Conclusion The AI integrated tool, i.e., the assistance means 30 for providing assistance 32 by the trained artificial neural network 22, significantly improves the assurance review process for large infrastructure projects like the London-Manchester high-speed rail line by enhancing risk mitigation, quality assurance, and process optimisation. Predictive analytics help foresee and prevent potential issues, while Al-driven tools automate quality checks and optimise management processes. Furthermore, AI enhances accountability through continuous monitoring and audits, and it improves stakeholder communication by providing real-time insights. By introducing innovative practices and technologies, AI ensures that the project is delivered on time, within budget, and to the highest quality standards, thereby boosting overall productivity and success. Example 3: Organisation Implementing an ERP System Introduction An Enterprise Resource Planning (ERP) system is a comprehensive software platform designed to integrate various business processes across an organisation into a single, unified system. The implementation of an ERP system is a strategic initiative aimed at optimising operations, improving data visibility, and enhancing overall efficiency. This organisation is undertaking the ERP implementation to replace fragmented legacy systems, streamline workflows, and achieve long-term profitability through better resource management and decision-making. Key Objectives • Process Integration: The ERP system will integrate critical business functions such as finance, supply chain, human resources, manufacturing, and customer relationship management (CRM), eliminating silos and fostering greater collaboration across departments. • Cost Efficiency: By automating routine tasks, reducing redundancy, and improving resource allocation, the ERP system will lead to significant cost savings, directly contributing to improved profitability. • Enhanced Data Visibility: The centralised data provided by the ERP system will enable real-time insights into business performance, leading to more informed and timely decision-making, which is crucial for maintaining a competitive edge and driving profitability. • Operational Optimisation: The ERP system will standardise processes across the organisation, leading to increased efficiency, reduced errors, and faster execution of tasks, all of which contribute to higher productivity and profitability. • Scalability and Growth: The ERP system is designed to support the organisation’s growth, allowing it to scale operations efficiently and manage increased complexity without proportional increases in cost. Project Scope The ERP implementation project includes the following phases: • Requirements Analysis: Conducting a thorough analysis of the organisation’s current processes and identifying specific needs that the ERP system must address to maximise its impact on profitability. • ERP System Selection: Choosing an ERP solution that aligns with the organisation’s strategic goals, offers the necessary functionality, and provides a strong return on investment (ROI). • System Design and Customisation: Customising the ERP system to fit the organisation’s unique processes while ensuring that it supports best practices that enhance profitability. • Data Migration: Transferring data from legacy systems to the new ERP system with a focus on data integrity and accuracy to avoid disruptions in operations. • Implementation and Testing: Rigorous testing of the ERP system to ensure it meets performance standards and supports the organisation’s profitability goals before going live. • Training and Change Management: Ensuring that staff are fully trained and prepared to use the ERP system effectively, with change management strategies in place to facilitate a smooth transition. • Go-Live and Support: Deploying the ERP system across the organisation and providing ongoing support to address any issues and ensure continuous operation. Timeline and Budget The ERP implementation is typically planned over a period of 12 to 24 months (about 2 years), depending on the size of the organisation and the complexity of its processes. The budget will include costs for software acquisition, customisation, data migration, implementation services, training, and ongoing support. A contingency budget is also set aside to manage unexpected challenges that may arise during the implementation. Assurance Review 14 Overview An assurance review 14 is a thorough evaluation process that assesses the ERP implementation project to ensure it is progressing as planned and aligned with the organisation’s profitability goals. This review involves independent experts who examine the project’s risk management, governance, quality of deliverables, and adherence to budget and timelines. How Assurance Review 14 Improves Profitability 1. Cost Control and Budget Adherence: Assurance reviews 14 help identify potential cost overruns early in the project. By recommending corrective actions, the review ensures that the project remains within budget, preventing unnecessary expenses that could erode profitability. 2. Maximising ROI: The review ensures that the ERP system is configured and used in a way that maximises its return on investment (ROI). This includes verifying that the system’s features are fully leveraged to reduce costs, increase efficiency, and drive revenue growth. 3. Risk Mitigation: Early identification of risks, such as data migration issues, system integration challenges, or user adoption problems, allows the organisation to address these challenges before they impact profitability. Effective risk management ensures that the ERP system delivers its intended financial benefits. 4. Process Efficiency: By evaluating the efficiency of the implementation process, assurance reviews 14 can identify opportunities to streamline workflows, reduce downtime, and ensure faster deployment. Efficient processes translate into cost savings and quicker realisation of profitability gains. 5. Quality Assurance: Ensuring the ERP system meets high-quality standards is crucial to avoid post-implementation issues that could disrupt operations and negatively impact profitability. The review ensures that the system functions as intended, supporting the organisation’s financial goals. 6. Improved Decision-Making: Assurance reviews 14 assess the effectiveness of the ERP system in providing accurate and timely data. Improved data visibility and real-time reporting enable better decision-making, leading to more strategic investments, optimised resource allocation, and enhanced profitability. 7. Enhanced Stakeholder Confidence: Regular assurance reviews 14 demonstrate to stakeholders, including investors and management, that the ERP project is being managed effectively with a clear focus on profitability. This confidence can lead to better support and potentially more favorable financial terms or investments. 8. Change Management Success: Assurance reviews 14 evaluate the effectiveness of change management strategies, ensuring that employees are fully engaged and making the most of the ERP system. Successful change management leads to higher productivity and quicker realisation of profitability benefits. How AI, i.e., the assistance means 30 for providing assistance 32 by the trained artificial neural network 22, improves the assurance review 14: The AI integrated tool, i.e., the assistance means 30 for providing assistance 32 by the trained artificial neural network 22, can significantly enhance the assurance process of an ERP implementation by automating and optimising various aspects of the project, leading to better risk management, cost control, and overall success. Examples of how the AI integrated tool, i.e., the assistance means 30 for providing assistance 32 by the trained artificial neural network 22, improves the assurance review 14 process are outlined below: 1. Predictive Analytics for Cost Control and Budget Adherence: Analyse historical data and project trends to predict potential cost overruns before they occur. By identifying patterns that suggest budget deviations, AI tools can alert project managers to take preemptive corrective actions. This helps ensure that the ERP implementation stays within budget, thereby protecting profitability. 2. Automated Quality Assurance: Continuously monitor the quality of the ERP implementation by analysing data integrity, system performance, and user feedback in real-time. Machine learning algorithms can detect anomalies or issues during data migration, system integration, or customisation, ensuring that problems are addressed before they escalate, reducing postimplementation disruptions. 3. Process Optimisation through AI: Optimise the ERP implementation process by analysing workflow data and identifying inefficiencies. Machine learning algorithms can suggest improvements in resource allocation, task prioritisation, and process execution, leading to faster deployment and reduced downtime. This streamlining of processes translates directly into cost savings and quicker profitability gains. 4. Enhanced Risk Mitigation: Assess and predict risks associated with the ERP implementation by analysing data from similar projects and real-time operational data. For example, we can foresee potential integration issues, user adoption challenges, or data migration risks, allowing the project team to implement preventive measures. Effective risk management ensures that the ERP system delivers its intended financial benefits without unexpected setbacks. 5. Improved Decision-Making with AI-Driven Insights: Enhances the decision-making process by providing advanced analytics and real-time insights from the centralised data in the ERP system. By leveraging AI, the organisation can identify trends, forecast outcomes, and make data-driven decisions that align with its profitability goals. This leads to more strategic investments, optimised resource use, and increased revenue. 6. Al-Powered Stakeholder Communication: Facilitate better communication and transparency with stakeholders by providing real-time updates and predictive insights on project progress and potential outcomes. Al-driven dashboards and reports can help stakeholders understand the financial implications of the ERP implementation, increasing their confidence and support for the project. 7. Automated Change Management Support: Enhance change management efforts by analysing user behavior and engagement with the ERP system. We can identify areas where additional training or support is needed, ensuring that employees are effectively utilising the system. Successful change management, driven by AI insights, can lead to higher productivity and faster realisation of profitability benefits. 8. Innovation through AI: AI can introduce innovative practices during ERP implementation by analysing best practices from other successful projects and applying them to the current context. This could include automating routine tasks, implementing advanced analytics for better decision-making, or optimising workflows, all of which contribute to enhanced profitability. Conclusion The AI integrated tool, i.e., the assistance means 30 for providing assistance 32 by the trained artificial neural network 22, enhances the assurance process in ERP implementations by improving cost control, quality assurance, and process efficiency. Predictive analytics help forecast and mitigate potential risks, while Al-driven tools optimise workflows and ensure high standards of quality. AI also improves decision-making by providing real-time insights and facilitates better stakeholder communication through automated reporting. Additionally, AI supports change management by ensuring that employees are effectively engaged with the new system. Overall, AI plays a crucial role in ensuring that the ERP implementation meets its profitability goals, delivering maximum return on investment and driving the organisation’s long-term success. Training Data for the ANN As described above, the apparatus 10 comprises training means 20 for training an artificial neural network 22 based on a data set 24. The data set 24 includes at least metrics 26 and / or performance indicators 28 in relation to an initiative 16 or operation 18. Further information in relation to the data set 24, i.e., training data for the artificial neural network 22, is provided below. Source: Years of annotated project management information derived from actual reviews previously conducted by Firewood &Co. Scope: Data covers projects, programmes, and portfolios across various industries, including Construction, IT, Healthcare, and Finance. Categories of Data: 1. Primary Source: Data from assurance reviews 14 conducted by Firewood and its associates throughout their history. Extensive data across various industries, including Construction, IT, Healthcare, and Finance 2. Secondary Source: Reports and articles generated based on assurance reviews 14 previously conducted. Literary works produced for the Association for Project Management (APM) and other project management associations. These works are derived from real-world project management experiences and insights gathered. 3. Tertiary Source: Public datasets and literary works related to project management available in the industry. These sources provide additional context and supplement the primary and secondary data. Data Types: Performance Indicators 28, including: Time to completion Budget adherence Resource utilisation Risk management effectiveness Quality of deliverables Process Metrics 26, including: Workflow efficiency Bottleneck identification Communication effectiveness Stakeholder satisfaction Technical Specifications 80, including: Industry-specific details, such as code quality, system uptime in IT, and machine operation metrics in construction Historical Trends and Anomalies 82, including: Success and failure patterns across different projects Annotated cases highlighting technical challenges and operational efficiencies Further examples demonstrating technical impact of apparatus 10 beyond the assurance review process Optimised Resource Allocation: The assistance means 30 for providing assistance 32 by the trained artificial neural network 22 predicts resource bottlenecks and suggests optimal resource distribution across projects. Accordingly, the assistance means 30 provides assistance 32 by the trained artificial neural network 22 by optimising resource allocation 84. Example: In road construction, the assistance means 30 for providing assistance 32 by the trained artificial neural network 22 suggests reallocating machinery to reduce project duration and improve machinery utilisation. Enhanced Risk Management: The assistance means 30 for providing assistance 32 by the trained artificial neural network 22 identifies latent risks by cross-referencing historical data from similar projects. Example: In IT infrastructure projects, the assistance means 30 for providing assistance 32 by the trained artificial neural network 22 detects hardware configurations prone to failure, allowing pre-emptive risk mitigation. Continuous Process Improvement: The assistance means 30 for providing assistance 32 by the trained artificial neural network 22 continuously learns and refines its recommendations, improving technical processes over time. Accordingly, the assistance means 30 provides assistance 32 by the trained artificial neural network 22 based on continuous process improvement 86. Example: In manufacturing, the assistance means 30 for providing assistance 32 by the trained artificial neural network 22 identifies adjustments to machine calibration schedules based on performance trends, improving efficiency and reducing waste. Technical Standards Compliance: The assistance means 30 for providing assistance 32 by the trained artificial neural network 22 ensures adherence to industry-specific technical standards through its analysis of technical metrics. Example: In regulated industries like healthcare and finance, the assistance means 30 for providing assistance 32 by the trained artificial neural network 22 helps ensure compliance with critical technical standards. Example of using the apparatus 10 to set up an assurance review 14 in relation to an initiative 16, From the user interface 34 a user can select the navigation component 36 relating to ‘Frameworks’ 60. The available frameworks 60 are presented to the user, e.g., ‘government gateway’. The assistance means 30 for providing assistance 32 by the trained artificial neural network 22 can recommend a particular framework 60 based on an assessment of the user’s requirements. A template 62 (which may be standardised or bespoke) can then be provided based on the selected framework 60. The template 62 includes a number of measures in relation to the framework 60 selected. A bespoke template 64 can also be created by selecting appropriate measures, which may be based on the framework 60 selected. The assistance means 30 for providing assistance 32 by the trained artificial neural network 22 can recommend measures based on an assessment of the user’s requirements and create the template 62. A user can then create an initiative 16 by selecting the ‘Create Initiative’ tab. The initiative 16 may be a project, to which a user allocates a name, budget, and initiative value. A user can populate fields relating to a ‘Description’, ‘Objectives’, ‘Industry type (e.g., commerce)’, and ‘Life cycle (e.g., hybrid)’. The assistance means 30 for providing assistance 32 by the trained artificial neural network 22 may recommend who should be interviewed, along with any documents to be provided. Such attributes can also be user selected. A user can then create a review based on user selections and / or recommendations by the assistance means 30 for providing assistance 32 by the trained artificial neural network 22. In relation to the review created, a user may complete fields relating to ‘Overview’, ‘Context’, ‘Scope’, ‘Objectives’, and ‘Team’. The assistance means 30 for providing assistance 32 by the trained artificial neural network 22 can provide an ‘Executive summary’ and ‘Lessons learned’. Measures such as ‘Organisational capabilities’, ‘Key roles’, and ‘Clear and unambiguous objectives’, etc, will be scored, indicating the performance of each respective measure. The assistance means 30 for providing assistance 32 by the trained artificial neural network 22 can provide guidance in terms of providing feedback or findings on the basis of scores allocated for each measure, along with proposed actions to take. All the information relating to the review is presented in a report. There is thus described an apparatus 10, method, and computer program with a number of advantages as described above and below. Examples of the disclosure leverage advanced technology and industry best practices. The use of the trained artificial neural network 22 by the assistance means 30, i.e., AI integration, further adds value, for example, by improving customer service with chatbots, enhancing data analytics, and automating routine tasks. The apparatus 10 offers efficiency through AI automation and real-time analysis, enhanced accuracy by reducing human errors and employing advanced analytics, and cost savings from reduced labour and operational costs. The term ‘comprise’ is used in this document with an inclusive not an exclusive meaning. That is any reference to X comprising Y indicates that X may comprise only one Y or may comprise more than one Y. If it is intended to use ‘comprise’ with an exclusive meaning, then it will be made clear in the context by referring to “comprising only one..” or by using “consisting”. In this description, reference has been made to various examples. The description of features or functions in relation to an example indicates that those features or functions are present in that example. The use of the term ‘example’ or ‘for example’ or ‘can’ or ‘may’ in the text denotes, whether explicitly stated or not, that such features or functions are present in at least the described example, whether described as an example or not, and that they can be, but are not necessarily, present in some of or all other examples. Thus ‘example’, ‘for example’, ‘can’ or ‘may’ refers to a particular instance in a class of examples. A property of the instance can be a property of only that instance or a property of the class or a property of a sub-class of the class that includes some but not all of the instances in the class. It is therefore implicitly disclosed that a feature described with reference to one example but not with reference to another example, can where possible be used in that other example as part of a working combination but does not necessarily have to be used in that other example. Although examples have been described in the preceding paragraphs with reference to various examples, it should be appreciated that modifications to the examples given can be made without departing from the scope of the claims. Features described in the preceding description may be used in combinations other than the combinations explicitly described above. Although functions have been described with reference to certain features, those functions may be performable by other features whether described or not. Although features have been described with reference to certain examples, those features may also be present in other examples whether described or not. The term ‘a’ or ‘the’ is used in this document with an inclusive not an exclusive meaning. That is any reference to X comprising a / the Y indicates that X may comprise only one Y or may comprise more than one Y unless the context clearly indicates the contrary. If it is intended to use ‘a’ or ‘the’ with an exclusive meaning, then it will be made clear in the context. In some circumstances the use of ‘at least one’ or ‘one or more’ may be used to emphasis an inclusive meaning but the absence of these terms should not be taken to infer any exclusive meaning. The presence of a feature (or combination of features) in a claim is a reference to that feature or (combination of features) itself and also to features that achieve substantially the same technical effect (equivalent features). The equivalent features include, for example, features that are variants and achieve substantially the same result in substantially the same way. The equivalent features include, for example, features that perform substantially the same function, in substantially the same way to achieve substantially the same result. In this description, reference has been made to various examples using adjectives or adjectival phrases to describe characteristics of the examples. Such a description of a characteristic in relation to an example indicates that the characteristic is present in some examples exactly as described and is present in other examples substantially as described. Whilst endeavoring in the foregoing specification to draw attention to those features believed to be of importance it should be understood that the Applicant may seek protection via the claims in respect of any patentable feature or combination of features hereinbefore referred to and / or shown in the drawings whether or not emphasis has been placed thereon. I / we claim:

Claims

1. An apparatus (10), wherein the apparatus (10) is an assurance management tool (12) for implementing an assurance review (14) of an initiative (16) or operation (18), wherein the apparatus (10) comprises:training means (20) for training an artificial neural network (22) based on a data set (24), wherein the data set (24) includes at least metrics (26) and / or performance indicators (28) in relation to the initiative (16) or operation (18); andassistance means (30) for providing assistance (32) by the trained artificial neural network (22) in relation to the initiative (16) or operation (18), or in relation to the assurance review (14).

2. An apparatus according to claim 1, wherein the assistance means (30) provides assistance (32) by the trained artificial neural network (22) by providing recommendations (46) and / or guidance (48).

3. An apparatus according to claim 2, wherein the assistance means (30) provides assistance (32) by the trained artificial neural network (22) by providing recommendations (46) and / or guidance (48) in relation to any step of a process flow (42) of a framework process (40) for developing an assurance review (14) of an initiative (16) or operation (18), and / or in relation to any implementation steps (44) of the process flow (42).

4. An apparatus according to claim 2 or 3, wherein a recommendation (46) by the assistance means (30) is a suggestion that something would be good or suitable for a particular job or purpose, or a suggestion that a particular action should be done or not done.

5. An apparatus according to any of claims 2 to 4, wherein the assistance means (30) provides assistance (32) by the trained artificial neural network (22) by recommending (46) how to improve an initiative (16) or operation (18), or by recommending (46) how to improve an assurance review (14).

6. An apparatus according to any of claims 2 to 5, wherein the assistance means (30) provides assistance (32) by the trained artificial neural network (22) by recommending (46) a particular framework (60), where the framework (60) recommended depends on user actions, responsesto questions, and / or depends on type of industry in relation to the initiative (16) or operation (18).

7. An apparatus according to any of claims 2 to 6, wherein guidance (48) by the assistance means (30) is help and advice about how to do something or about how to deal with a problem identified by a user or the assistance means (30).

8. An apparatus according to any of claims 2 to 7, wherein the assistance means (30) provides assistance (32) by the trained artificial neural network (22) by providing guidance (48) based on identifying where there is scope creep and / or projected delays in meeting milestones.

9. An apparatus according to any of the preceding claims, wherein the assistance means (30) provides assistance (32) by the trained artificial neural network (22) by generating documents (50) based on user actions, responses to questions, or type of industry in relation to the initiative (16) or operation (18).

10. An apparatus according to claim 9, wherein documents (50) generated are templates (62) comprising different categories and measures.

11. An apparatus according to claim 9 or 10, wherein documents (50) generated include an executive summary, lessons learned, findings, and action items using content from interviews, review measures, documents and observations inputted by a user.

12. An apparatus according to any of the preceding claims, wherein the assistance means (30) provides assistance (32) by the trained artificial neural network (22) by capturing experiences (54).

13. An apparatus according to any of the preceding claims, wherein the assistance means (30) provides assistance (32) by the trained artificial neural network (22) by presenting information (58).

14. An apparatus according to any of the preceding claims, wherein the assistance means (30) provides assistance (32) by the trained artificial neural network (22) by action tracking (52).

15. An apparatus according to any of the preceding claims, wherein the assistance means (30) provides assistance (32) by the trained artificial neural network (22) by file sharing (70).

16. An apparatus according to any of the preceding claims, wherein the assistance means (30) provides assistance (32) by the trained artificial neural network (22) by automating tasks (74).

17. An apparatus according to any of the preceding claims, wherein the assistance means (30) provides assistance (32) by the trained artificial neural network (22) by providing analytics (76).

18. An apparatus according to claim 17, wherein the analytics (76) are predictive analytics. (78)19. An apparatus according to any of the preceding claims, wherein the assistance means (30) provides assistance (32) by the trained artificial neural network (22) by providing identification (72), including identifying trends, anomalies, and patterns in data.

20. An apparatus according to claim 2 or any claim dependent on claim 2, wherein the assistance means (30) provides assistance (32) by the trained artificial neural network (22) by recommending (46) an assurance review strategy and plan based on inputs from a user and historical datasets.

21. An apparatus according to any of the preceding claims, wherein the assistance means (30) provides assistance (32) by the trained artificial neural network (22) by optimising resource allocation (84), wherein resource allocation (84) is optimised by suggesting reallocation of machinery.

22. An apparatus according to any of the preceding claims, wherein the assistance means (30) provides assistance (32) by the trained artificial neural network (22) based on continuous process improvement (86), wherein continuous process improvement (86) is by identifying adjustments to machine calibration schedules based on performance trends.

23. An apparatus according to any of the preceding claims, wherein the assistance means (30) provides assistance (32) by the trained artificial neural network (22) in relation to the initiative (16) or operation (18) and in relation to the assurance review (14).

24. A method for implementing an assurance review (14) of an initiative (16) or operation (18) by an assurance management tool (12), the method comprising:training an artificial neural network (22) by training means (20) based on a data set (24), wherein the data set includes at least metrics (26) and / or performance indicators (28) in relation to the initiative (16) or operation (18); andproviding assistance (32) by assistance means (30) based on the trained artificial neural network (22) in relation to the initiative (16) or operation (18), or in relation to the assurance review (14).

25. A computer program for implementing an assurance review (14) of an initiative (16) or operation (18) by an assurance management tool (12), the computer program comprising instructions which, when executed by an apparatus (10), cause the apparatus (10) to perform at least:training an artificial neural network (22) by training means (20) based on a data set (24), wherein the data set (24) includes at least metrics (26) and / or performance indicators (28) in relation to the initiative (16) or operation (18); andproviding assistance (32) by assistance means (30) based on the trained artificial neural network (22) in relation to the initiative (16) or operation (18), or in relation to the assurance review (14).

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