Your Journey to AI starts here.

The journey to ensuring data readiness for AI implementation is multifaceted, involving meticulous preparation, seamless integration, and stringent governance. By investing in data readiness, organisations can unlock the full potential of AI, driving innovation and achieving strategic goals while maintaining data integrity and compliance.

Unifying your business’ data is a fundamental step in your data maturity journey. AI models rely on high-quality data to function effectively; by seamlessly connecting disparate data sources, you ensure the foundations for robust and scalable AI deployments.  

• Data Availability: Centralising data sources ensures AI has comprehensive datasets, enhancing learning and performance.
• Data Consistency: Unified data improves AI reliability and training.
• Real-Time Insights: Integrated data systems enable real-time processing, delivering timely and relevant insights.
• Enhanced Predictive Capabilities: A unified data foundation helps AI analyse historical trends, improving predictive accuracy and enabling proactive decisions.

Data preparation and integration is the foundational step in ensuring data readiness for AI. It involves cleaning, transforming, and organising data to make it suitable for analysis. Key activities in this phase include:

• Ingesting Data: Clean data by removing duplicates, correcting errors, and handling missing values.
• Data Transformation: Normalise, encode, and scale data for AI models using Azure Data Factory.
• Data Warehousing: Consolidate data from multiple sources using Microsoft Fabric for advanced querying and analysis.
• Data Lake: Store and process large volumes of diverse data types with Azure Data Lake Storage.

Data governance ensures that data is managed securely and compliantly throughout its lifecycle. Setting policies, procedures, and standards to maintain data quality and integrity is crucial in AI to maintain fairness, avoid bias, enhance privacy and comply with ethical standards. 

• Data Governance: Implement a data governance strategy using tools to ensure data is protected from unauthorised access and breaches.
• Data Compliance: Azure Policy and Microsoft Compliance Manager help organisations adhere to regulatory requirements and industry standards. They provide templates and assessments to ensure data practices are compliant with laws such as GDPR and CCPA.
• Data Lineage: Understanding the origin and flow of data is crucial for trust and transparency. Azure Purview offers comprehensive data lineage tracking, which helps organisations understand how data moves through their systems and where it originates.

The journey to the public cloud is a strategic pathway to unlocking the full potential of AI. By carefully planning and executing each step—from assessment and migration to governance and AI utilisation—businesses can create a robust, responsible, scalable, and secure environment for AI innovation and adoption. The proximity to advanced cloud, data and security tools allows organisations to fully leverage AI capabilities, driving transformative business outcomes and achieving strategic objectives.

Migrating from legacy infrastructure allows your business to  innovate by consolidating and scaling data and applications while embedding additional platform native security. Leveraging Microsoft Azure supports the on-demand resources as needed, as well as providing proximity to foundational tooling required later in your journey to implementing AI at scale. 

• Assessment and Planning: Identify your business needs and develop a comprehensive migration plan. Include timelines, resource allocation, and necessary integrations.
• Infrastructure Design: Design a robust system architecture that ensures seamless integration with your existing infrastructure and supports advanced AI capabilities when needed.
• Deployment and Configuration: Deploy your cloud infrastructure and configure it to meet organisational requirements. Ensure all integrations are correctly set up and functioning.
• Testing and Validation: Conduct thorough testing to ensure that the system operates as expected. Validate the security measures and compliance with industry standards.

Modernising your public cloud infrastructure provides unparalleled scalability and flexibility. Businesses can easily scale their resources up or down based on demand, increased security, optimised running costs and accelerated use of AI functionality and models. Modernising your cloud landscape also allows you to leverage on going investments into the Microsoft Azure platform without the overhead of doing this yourself. Modernisation typically starts with the following:

• Planning & Design: Identify your business needs and conduct a thorough risk analysis to develop a comprehensive modernisation plan. Design a robust system architecture that meets business requirements and future proofs infrastructure to enhance your business’s journey towards AI innovation.

• Cloud Deployment: Deploy your cloud infrastructure in a phased manner to minimise disruptions. Configure it to meet organisational requirements, ensuring all integrations are correctly set up and functioning.
• Automation: Utilise automation tools to streamline deployment and configuration processes, reducing the risk of human error.
• Testing and Validation: Conduct thorough testing to ensure that the system operates as expected. Validate security measures and compliance with industry standards.
• Monitor and Evaluate: Continuously monitor the performance of infrastructure and gather feedback. Evaluate the impact on business processes and adjust as needed to drive the modernisation agenda.

Organisations are increasingly leveraging intelligent apps as part of their public cloud infrastructure. These applications not only optimise operational efficiency but also play a crucial role in an organisation’s journey toward utilising Artificial Intelligence. Many orgnisation are consolidating applications into containerised cross-platform supported packages that are future ready and capable of supporting AI.

• Business Needs and Futureproofing: Assess current infrastructure, define business requirements, and ensure a scalable, adaptable design for future advancements.
• Deployment and Configuration: Implement phased deployment of intelligent apps to minimise disruptions and ensure all integrations are functional, utilising automation tools to streamline processes and reduce human errors.
• Testing and Validation: Conduct extensive testing to ensure applications operate as expected and meet security standards and establish centres of excellence to support new applications.
• Performance Monitoring: Implement continuous monitoring to promptly identify and address performance issues, ensuring optimal functionality and efficiency of the intelligent apps.

Utilising low code platforms enables organisations to rapidly develop and deploy AI solutions with minimal coding expertise, thereby accelerating the integration of AI into their business processes. This streamlined approach not only reduces development time and costs but also empowers non-technical staff to contribute to AI projects, fostering innovation and enhancing overall business agility.

Guardrails in low-code platforms like Microsoft Power Platform play a crucial role in balancing agility and control. By implementing technical, operational, and organisational guardrails, companies can empower their users to innovate while maintaining security, compliance, and governance.

• Data Loss Prevention (DLP) Policies: Protect sensitive data by controlling access and sharing.
• Environment Strategies: Use separate environments for development, testing, and production.
• Role-Based Access Control (RBAC): Assign permissions based on user roles to limit unauthorised access or modification of critical resources.
• Power Platform Centre of Excellence (CoE): Centralise governance, best practices, and templates.

By utilising low-code platforms, organisations can swiftly develop and deploy AI solutions, even with minimal coding expertise. This approach not only reduces development time and costs but also empowers non-technical staff to actively contribute to AI projects, fostering innovation and enhancing overall business agility.

• Building Solutions with Power Platform: Use Power Platform’s tools like Power Apps and Power Automate to design tailored solutions with minimal coding.
• Deployment of Solutions: Deploy solutions across your organisation using different environments for development, testing, and production.
• Monitoring and Optimisation: Continuously monitor and adjust solutions using Power Platform’s analytics features to optimise performance and effectiveness.

Incorporating AI into business operations presents numerous opportunities for innovation and efficiency; however, it also introduces potential security risks. To safeguard data and infrastructure, a robust security strategy is essential. 

• Assess Current Security Posture: Evaluate the current framework to identify vulnerabilities.
• Implement Data Protection Measures: Use Azure Defender for Cloud for encryption, access controls, and DLP policies.
• Establish Compliance and Governance: Adhere to industry standards with Azure Policy and Compliance Manager.
• Develop Role-Based Access Control: Enforce RBAC to limit access to critical resources.
• Monitor and Optimise Continuously: Regularly adjust security measures using analytics from Azure Purview and Defender for Cloud.

Integrating AI with Microsoft Dynamics 365 offerings can significantly enhance business productivity and decision-making processes by automating routine tasks and providing intelligent insights. Careful planning, considered configuration, robust integration, and thorough training are essential to harness the full potential of AI within Dynamics 365 to drive innovation in your operations.

To successfully implement a CRM in Microsoft Dynamics 365, create architecture blueprints, confirm data tabling designs, and ensure all functionalities are thoroughly tested. Establish robust support mechanisms, develop comprehensive training programmes, and prepare for bi-annual functionality releases to maintain ongoing success.

• Plan and Design: Create architecture blueprints and confirm data tabling designs to build the foundational plan for configuration of the Dynamics 365 platform.
• Test and validate all functionalities and security posture through comprehensive testing.
• Monitoring and Support: Set up monitoring and support mechanisms to ensure smooth operation and evergreen updates.
• Train and Onboard Users: Develop comprehensive training programmes to ensure users are adopting the new system.
• Prepare for the next wave: Dynamics 365 releases new functionality bi-annually and planning for and embracing these waves of changes is critical for ongoing success.

Consumable AI in relation to Dynamics 365 refers to the integration of Artificial Intelligence features within the Dynamics 365 platform to enhance various business processes. These AI features are designed to be easily accessible, contextual to the user and business process, and usable without requiring extensive technical expertise.

Steps an organisation needs to take to utilise Consumable AI in Dynamics 365:

• Review native features: several preconfigured AI elements are available with minimal configuration. These target common scenarios and should always be evaluated first.
• Identify Needs and Enable Features: Determine the specific areas where AI can add value and activate relevant features in Dynamics. Map these needs to native features where possible. This may involve configuring settings and permissions to ensure the AI tools are accessible to the appropriate users. Train Users: Provide training to employees on how to use the AI features effectively and safely.
• Prepare for the next wave: Dynamics 365 releases new functionality bi-annually and planning for and embracing these waves of changes is critical for ongoing success.

By leveraging consumable AI capabilities in Dynamics 365, the platform assists users in a wide range of tasks, from automating routine processes to providing intelligent insights and recommendations. With AI extensibility, businesses can tailor these capabilities to meet their specific needs.

• Responsibly Monitor and Evaluate: Develop and implement Responsible AI guardrails to continuously monitor the performance of AI features and gather feedback. 
• Extend where needed: several AI extensibility options are available where the identified organisational needs exceed the default functionality provided and these can be configured around desired business processes and outcomes. 
• Test and Validate: Conduct thorough testing to ensure the system operates as expected. Validate security measures and compliance with industry standards. 
• Train and Onboard Users: Develop comprehensive training programmes for all users to encourage user adoption.