Menu
Close
2.9.24
A straightforward playbook offering practical advice and actionable steps for implementing AI into your organisation.
This roadmap provides a strategic blueprint detailing the necessary steps, key milestones, and approaches for effectively integrating AI technologies across different areas of an organisation.
We break down the practical steps needed to get your organisation AI-ready and how to assess your AI maturity.
Each chapter is designed to provide you with actionable steps, insights and detailed guidance, transforming the task of AI integration into a structured and manageable process.
You can download the full roadmap as a playbook to save or share with your teams or peers.
Use the contents menu below to choose, or save, what you’d like to read about first.
C1: Establishing a Robust Data Infrastructure.C2: Building a Skilled AI Team.C3: Choosing the Right AI Tools and Platforms.C4: Developing a Clear AI Strategy.C5: Responsible and Ethical AI Practices.C6: Prioritising AI Security and Risk Management.C7: Starting with Pilot Projects.C8: Choosing the Right Technology Partner - Key Criteria.C9: Technology Partner Resources.C10: Navigating the Path to AI Readiness.
The effectiveness of AI systems depends on the quality and accessibility of data.
Following AI data infrastructure best practices is essential to ensure AI initiatives have a solid foundation.
1. Data Audit
Conduct a comprehensive audit of your existing data sources to identify gaps, inconsistencies, and areas requiring enhancement.
2. Data Cleaning
Implement systematic processes to clean, standardise, and organise data, ensuring accuracy and reliability.
3. Data Accessibility
Develop a centralised data storage solution that ensures seamless access for stakeholders and AI applications.
Data Lakes - Utilise data lakes to store vast amounts of raw data. Examples include Amazon S3, Azure Data Lake, and Google Cloud Storage.
Data Warehouses - Use data warehouses for structured, query-optimised data. Examples include Snowflake, Amazon Redshift, and Google BigQuery.
Cloud Data Platforms - Leverage platforms like Microsoft Azure, AWS, or Google Cloud Platform (GCP) to manage and integrate these storage solutions efficiently.
ETL Tools - Deploy Extract, Transform, Load (ETL) tools to automate and streamline data processing workflows.
Data Governance Tools - Adopt comprehensive data governance frameworks and tools to maintain data integrity, privacy, and security.
Resources for Data and Data Services:
Data Services - Discover the value of solid data foundations.
Creating a Strong Data Foundation for AI (white paper download) - IBM.
Deploying and optimising AI systems requires specialised expertise. Building a skilled AI team is essential to negotiate the complexities of AI technologies and methodologies.
Data Experts - Focus on extracting insights and developing predictive models from data.
Machine Learning (ML) Engineers - Specialise in deploying, scaling and optimising machine learning models.
AI Specialists - Provide deep technical knowledge in specific AI domains, such as natural language processing (NLP) or computer vision.
Delivery Managers - Ensure AI projects are aligned with strategic business objectives and are executed within scope and on schedule.
Expert Technology Consultancies - Get access to permanent or freelance talent to fill the specific expertise needed for your project.
Specialised Recruitment Platforms - Utilise platforms dedicated to AI and tech talent to streamline the hiring process.
In-House Training Programs - Develop and implement comprehensive training programs for current and new employees covering AI fundamentals and advanced topics.
Workshops and Seminars - Organise regular workshops and invite industry experts to share insights and best practices.
Online Learning Platforms - Encourage continuous learning through reputable online courses and certifications.
Explore Outsourced Training - Partner with a technology partner for knowledge sharing and training either on the project or ahead of the project start date.
Cultivate an environment where experimentation, innovation, and continuous learning are encouraged and rewarded, fostering a dynamic and forward-thinking workforce.
Resources for building skilled AI teams:
Build Circle - Technology consultancy that scales your data, engineering, and AI capability. Provides entire teams or individual engineers integrated into your existing workforce to boost capability and culture while increasing skill sets and capacity.
Resource for hiring AI and Tech talent:
Arrows Group - Tech recruitment specialists - specialised skill set hiring.
Selecting the appropriate AI tools and platforms is critical to successful AI initiatives. The right technology stack can significantly enhance operational efficiency and decision-making capabilities.
1. Alignment with Business Objectives
Ensure the selected tools address specific business challenges and strategic goals.
2. Scalability and Flexibility
Choose tools that can scale with your business and adapt to evolving needs.
3. Security and Compliance
Prioritise platforms with robust security features and compliance certifications to protect sensitive data.
TensorFlow and PyTorch - Leading frameworks for developing machine learning models, offering extensive libraries and community support.
DataRobot and H2O.ai - Platforms specialising in automated machine learning, simplifying the process of model development and deployment.
Azure OpenAI, Google Gemini and AWS Bedrock - Generative AI capabilities from the main cloud platforms, providing access to a wide range of models.
Azure Document Intelligence, AWS Textract and Google Document AI - OCR services that make up a key part of the pipeline for many generative AI use cases.
Azure AI Search, AWS OpenSearch and Google Vertex AI Search - Cloud-based, AI-powered search services that enable you to find the most relevant data as part of RAG-based generative AI flows.
Azure Content Moderator, AWS Rekognition and Google Cloud Text Moderation - Cloud-based service that enables AI safety by moderating AI outputs.
Pilot Projects - Initiate small-scale pilot projects to validate the efficacy of the tools and gather initial insights.
Seamless Integration - Ensure the chosen tools integrate seamlessly with existing IT infrastructure and workflows.
Ongoing Support - Opt for tools that offer robust support and detailed documentation to facilitate smooth implementation and troubleshooting.
More resources on choosing the right AI tools and platforms:
How to choose the best AI platform - IBM.
The 21 Best Artificial Intelligence Platforms Of 2024 - The CTO Club.
A well-defined AI strategy is essential to align AI initiatives with overarching business goals. It provides a roadmap for integrating AI into business operations, ensuring coherence and strategic focus.
1. Objective Setting
Establish clear and specific goals for your AI initiatives, such as enhancing operational efficiency, improving customer experience, or innovating product offerings.
2. Roadmapping
Create a comprehensive plan that details the scope of AI projects, timelines, necessary resources, and key milestones to track progress.
3. Stakeholder Engagement
Engage key stakeholders in the planning process to ensure their perspectives are considered and to secure their commitment and support.
Introduce Small-Scale Projects - Initiate small-scale AI projects to test and demonstrate their potential value, allowing you to refine your approach based on real-world feedback.
Key Performance Indicators (KPIs) - Define specific metrics to evaluate the effectiveness and impact of your AI initiatives, ensuring that they contribute to your strategic objectives.
Iterative Review - Conduct regular reviews of your AI strategy, using feedback and performance data to make necessary adjustments and improvements.
Interdisciplinary Teams - Form cross-functional teams with members from various departments to encourage diverse perspectives and innovative solutions.
Regular Strategy Sessions - Conduct frequent strategy sessions to discuss progress, address challenges, and opportunities for improvement, and explore new opportunities for AI integration.
Unified Goals - Align departmental goals with the overall AI strategy, promoting a cohesive and unified effort towards AI adoption.
Resources for strategic Generative AI guidance:
Becoming AI-Ready - A Comprehensive Guide for CEOs and Founders - A ‘How to’.
Generative AI Services - Strategy, integration, use-cases and governance.
Ethical generative AI practices are vital for maintaining trust, complying with regulations, and protecting your business’s reputation. Implementing these practices helps mitigate risks, enhances transparency, and fosters accountability.
1. Fairness
Ensure that AI models and applications do not perpetuate bias or discrimination.
2. Transparency
Strive for transparency in AI decision-making processes, making them explainable and understandable.
3. Accountability
Establish clear accountability for the outcomes of AI applications, with mechanisms for addressing issues and concerns.
Data Privacy Measures - Implement robust data privacy measures to protect user data and follow relevant regulations.
Bias Audits - Perform routine evaluations of AI systems to identify and rectify any biases, ensuring fair and unbiased outcomes.
Explainability Tools - Utilise tools that enhance the explainability of AI models, helping stakeholders understand how decisions are made.
Data Protection Officer (DPO) - Appoint a DPO to oversee compliance with data protection regulations.
Regular Compliance Audits - Implement a schedule for frequent audits to verify that all processes and systems meet the required legal and regulatory standards.
User Consent Mechanisms - Develop and utilise systems to obtain explicit consent from users regarding their data, maintaining thorough records of consent and usage permissions.
To learn more about responsible and ethical AI integration read this SME article by Andrew Gibson - Principal AI Consultant at Build Circle:
More resources on implementing responsible and ethical AI:
AI Regulations Around the World - Mind Foundry.
As GenAI advances, Regulators and Risk Functions Rush to Keep Pace - McKinsey & Company.
Security is a critical aspect of AI deployment. AI systems often handle sensitive data and perform essential functions, necessitating robust security measures.
1. Secure Development Practices
Follow best practices in coding security and regularly review code to identify potential vulnerabilities and strengthen your systems.
2. Data Encryption
Use encryption methods to secure data both when it is stored (at rest) and when it is being transmitted, protecting it from unauthorised access and breaches.
3. Access Controls
Establish robust access control policies to ensure that only authorised personnel have access to AI systems and sensitive data.
Vulnerability Scanning - Periodically examine AI systems to detect vulnerabilities and swiftly address any issues that are discovered.
Penetration Testing - Conduct penetration testing to simulate attacks and evaluate the robustness of your AI security measures. Identify areas for improvement.
Incident Response Plan - Develop and continually update a comprehensive incident response plan to manage and mitigate the impact of security breaches.
Prioritising security not only protects your data and systems but also builds trust with your customers and partners, enhancing your business reputation and competitive edge.
By demonstrating a commitment to robust cybersecurity measures, you show stakeholders that you value their privacy and data protection. This trust can lead to increased customer loyalty, as clients feel more secure in their interactions with your business.
More reading on the importance of AI security:
Towards Secure AI (full publication download) - The Alan Turing Institute.
70% of Businesses Prioritise Innovation Over Security in Generative AI Projects - Infosecurity Magazine.
Starting with pilot projects allows you to test AI applications on a smaller scale, providing valuable insights and demonstrating value before a full-scale rollout.
These initial projects serve as a controlled environment where you can assess the effectiveness of AI solutions, identify potential issues, and gather stakeholder feedback.
By starting small, you can refine your approach, optimise performance, and ensure alignment with business objectives.
1. Strategic Selection
Choose pilot projects with clear, measurable benefits that align with your business objectives. Focus on initiatives that can deliver measurable outcomes and provide valuable insights for broader implementation.
2. Detailed Design
Create a comprehensive plan for each pilot project, outlining specific goals, criteria for success, and a detailed execution strategy. Design a clear path to achieving its objectives.
3. Stakeholder Involvement
Involve key stakeholders in both the planning and execution stages to ensure their perspectives are considered. Their support and alignment are crucial for the success and scalability of the pilot projects.
Key Performance Indicators (KPIs) - Establish and monitor specific metrics to evaluate the effectiveness and impact of your pilot projects. KPIs provide a clear measure of success and areas needing improvement.
Feedback Loops - Implement mechanisms to collect feedback from all relevant parties. This allows for real-time adjustments and ensures that any issues are promptly addressed.
Documentation - Keep detailed records of the processes, outcomes, and lessons learned from each pilot project. This documentation will be invaluable for scaling successful initiatives and refining future AI strategies.
Iterative Scaling - Use the insights and results gained from pilot projects to refine and scale AI applications iteratively. This approach allows for continuous improvement and minimises risks.
Resource Allocation - Distribute resources based on the demonstrated success and potential impact of the pilot projects. Prioritise initiatives that have shown clear benefits and scalability.
Continuous Improvement - Regularly update and enhance AI models and processes using feedback and performance data. This ensures that your AI initiatives remain effective and aligned with evolving business needs.
In-depth resource on scaling AI projects:
Moving past GenAI’s honeymoon phase: Seven hard truths for CIOs to get from pilot to scale. - McKinsey& Company.
Selecting the right technology partner is a pivotal step in your AI journey. The right partner can provide the expertise, resources, and support necessary to implement AI initiatives successfully, ensuring alignment with your business goals and maximising the return on your AI investments.
Proven Track Record - Select a technology partner who has a demonstrated history of success in AI development and deployment. A proven track record indicates their ability to deliver comprehensive, effective and reliable AI solutions.
Industry Understanding - Make sure the partner has a thorough understanding of the specific challenges and regulatory requirements of your industry. This knowledge is crucial for developing AI solutions that are both compliant and highly effective in addressing industry-specific issues.
Scalable Solutions - Look for partners who offer solutions that can grow with your business and adapt to evolving needs. Scalability ensures that as your company grows, AI solutions remain effective and efficient.
End-to-End Support - The ideal partner should provide comprehensive support, covering everything from initial consulting to implementation and ongoing maintenance. This ensures a smooth transition and long-term success.
Data Protection - Ensure that your technology partner adheres to stringent security standards and complies with relevant regulations. Data protection is crucial to maintain trust and avoid legal issues.
Regular Audits - The partner should conduct regular security audits and follow industry-specific regulations such as GDPR or HIPAA. This demonstrates their commitment to maintaining high security standards.
Detailed Request for Proposals (RFPs) - Develop detailed RFPs to systematically compare potential partners. A thorough RFP process helps in evaluating capabilities, pricing, and alignment with your business needs.
Review References - Assess case studies and request references to gauge the partner's reliability and past performance. This helps in understanding their expertise and success in similar projects.
Test Projects - Engage in pilot projects to evaluate the partner’s technical proficiency, project management skills, and cultural fit. Pilot collaborations provide a practical assessment before full-scale commitments.
Transparent Communication - Establish clear communication channels for regular updates and issue resolution. Transparent communication is critical to maintaining healthy and effective partnerships.
Define KPIs - Set mutual goals and key performance indicators (KPIs) to measure partnership success. This ensures that both parties are aligned strategically and can track progress effectively.
Collaborative Refinement - Commit to refining and enhancing AI solutions together over time. Continuous improvement ensures that AI applications remain relevant and effective as business needs evolve.
Training Programmes - Ensure the partner provides comprehensive training and knowledge transfer to empower your internal teams. Effective training programs help build in-house expertise and reduce dependency on recruitment.
Transform your business with Generative AI as a Service - strategy, governance and AI-driven innovation. Build scalable AI platforms for better customer experiences.
Transform your data into business value. Unlock key business-health metrics to discover new levels of efficiency and deliver maximum impact for your customers.
Engage the right people to build the right product. Fast. Design, develop and launch data and AI-powered solutions to increase the quality of your product. Accelerate development processes for innovation, scalability and market adaptability.
For personalised support and guidance on becoming AI-ready:
Contact Rory Seabrook - Managing Director, Build Circle.
Embarking on the journey to AI readiness is a transformative process that requires careful planning, strategic decision-making, and a commitment to ethical and responsible practices.
By following the roadmap outlined in this guide, CEOs and founders can effectively integrate AI into their core operations, harnessing its full potential to drive innovation, efficiency, and competitive advantage.
As you take these steps towards becoming AI-ready, remember that the journey is not just about technology but also about cultivating a culture of continuous learning, collaboration, and adaptability - lead your organisation into a future where artificial intelligence is a key enabler of growth and success.
For those ready to take the next step, download the complete roadmap and connect with our team for personalised support.
Let’s build a future where your business thrives with the power of AI.
© Copyright 2024 Build Circle Ltd
All Rights Reserved.
