Organisations, no matter what industry or sector you are in, are struggling to find measurable value in their investments in AI. Pilot programs are stalling (or not even taking off), use cases are too isolated, and there is a real struggle to generate any momentum. What we are seeing is that even with powerful new technologies, tried-and-true approaches is what will unlock value.
Connect vision to execution, build effectively, and scale with confidence.
Strategy and vision
Identifying and defining business objectives
Understanding the overarching goals of the clients; ensuring to explicitly classify current pain points and desired outcomes of the implementation.
Tools such as Cisco’s AI Readiness Assessment Index can provide useful data for informing strategy application.
AI readiness assessment
Evaluating the client’s existing tech infrastructure and assessing data maturity and organisational readiness for AI adoption. Deloitte identifies six key areas for AI readiness assessment, including Strategy, People, Process, Ethics, Data and Technology and Platforms.
Stakeholder engagement
Ensuring that stakeholders are involved inter-departmentally to identify AI use cases that can deliver immediate impact as well as long-term value.
To capture AI’s potential to create value, organisations will need a plan to retool the relevant existing processes, upskill or hire key staff, refine approaches toward partnership, and develop the necessary data and technical infrastructure to deploy AI.
Strategy
AI is a transformative technology; alignment on direction and level of ambition is crucial. Define an AI vision and goals that align with organisational objectives, and then you can devise an approach for managing capability across the enterprise.
People
Agencies may face challenges around accessing and recruiting necessary technical skills, as well as helping existing employees develop and deploy AI skills. To address these areas, consider integrating AI with human workflows, redefining talent models, and getting stakeholder buy-in through effective communications and change management.
Processes
Establish, define, and design processes, controls, and governance systems to enable successful AI implementation. While AI pilots can provide proof of AI’s potential, its true value cannot be captured until it is integrated with the work and processes of the organisation.
Data
AI is only as good as the data upon which it is built, and its appetite for data is voracious. Design a data governance system that includes engineering and security. Data governance should include rules for sourcing, accessing, and quality management.
Technology and platforms
Procure and develop appropriate AI technology and platforms to operationalise AI assets, including those related to vendors, interoperability, and the computing environment. A variety of models for pursuing AI exist that vary in terms of platforms and ownership of technology (e.g., internal or in partnership), but, in all cases, AI requires a coherent approach that considers future requirements as AI scales within the organisation and its usage evolves.
Ethics
Establish mechanisms to understand and prevent AI bias, promote fairness and transparency, and ensure values and integrity are embedded in AI-driven initiatives. While any technology deployment should be ethical, AI brings issues such as transparency, privacy, and bias into particular focus.
Develop and test
Data and tools selection
Identify appropriate AI tools, platforms, and frameworks (e.g., machine learning, NLP, RPA) tailored to the client’s needs, infrastructure, and capabilities. Decide on developing in-house solutions or leveraging third-party tools based on cost, speed, and customisation requirements.
Prototype and iterate
Develop pilot AI models for top-priority use cases, using agile methods to refine based on real-time results.
Governance framework
Set up an ethical, legal, and compliance framework to govern AI usage, considering data privacy, transparency, and bias prevention.
Scale and sustain
Launch and integrate
Rollout AI solutions across your organisation, ensuring they are integrated smoothly into your current workflows.
Train and engage
Equip teams with the skills and knowledge to use and maintain AI effectively, fostering a culture of AI and adoption.
Optimise and scale
Continuously monitor performance, adapt based on feedback, and scale successful tests across other departments and use cases.
If this something you are exploring, or need to know, let’s start the conversation. NOW Digital can help you define and scale AI in a way that delivers real outcomes.