The introduction of GenAI is at the top of the agenda for many companies – regardless of size. And many more – significantly larger companies – already have various AI technologies integrated into, among other things, product development and customer service.
However, generative AI is grabbing the headlines because GenAI is intuitive to understand and use and easy to implement – especially in so-called cognitive areas, where content with text, images, and video must be produced.
Therefore, it is also easy for individuals to subscribe—for example, to ChatGPT from Open AI—and start creating content. Similarly, Microsoft has also started with its Copilot after a somewhat hesitant start in Denmark.
The AI Act
However, this possibility of rapid adoption of GenAI is also a risk factor that needs to be assessed if GenAI is to be rolled out to all employees. The AI Act recently adopted by the EU aims to guide companies on the conditions that must be in place before AI is used.
It’s that, among other things, states that companies must have control over risk management and assessment, data governance, quality management, documentation of the AI system, robustness, and cybersecurity. So – while it is easy for an individual to subscribe to an AI tool, it is a different comprehensive project for a company to implement. Therefore, the rationales for introducing GenAI must also be well-covered.
A good first step is to study the organisation’s perception of, needs, and desires for AI. With this information uncovered, it is possible to identify prominent pilot projects and areas where AI insight needs strengthening.
This evaluates the discrepancies between an organisation’s current AI understanding and competencies. This ensures, among other things, that AI initiatives are aligned with overall strategic goals, optimizes return on investment and prevents incoherent efforts that can arise from uncoordinated initiatives.
By identifying gaps in skills, technology and processes, this assessment ensures the efficient allocation of resources, whether it involves training existing staff, recruiting new talent or upgrading technology infrastructure. It also plays a crucial role in managing risks associated with AI implementation, such as issues related to data protection, ethics and regulatory compliance.
As AI technology evolves rapidly, an ongoing review of an organisation’s proper adoption and use of AI is necessary to ensure value creation and maintain a competitive advantage. This process is not just a procedural checklist but a fundamental step in leveraging AI to create significant business value, ensuring that AI capabilities are aligned with the organisation’s needs and ambitions.
Identify use cases suitable for initial AI pilots
Identifying AI use cases that aim to increase efficiency creates immediate value and is a crucial step for organisations striving to leverage AI strategically. And here, all jobs with cognitive efforts are obvious candidates.
The goal is not to replace people with AI technology but to augment the same people with AI technology to be more effective for the same paycheck. This process begins with a comprehensive understanding of the organisation’s current processes, challenges, and strategic goals.
Analysing existing workflows, data flows, and performance metrics allows you to pinpoint areas where AI can have the most significant impact: automating repetitive tasks, improving decision-making with predictive data analytics, or optimising resource allocation.
Central to this effort is collaboration between cross-functional teams that bring together IT specialists, data analysts, and business unit leaders. This collaboration also facilitates the evaluation of potential ROI and helps prioritise use cases that offer the highest value for the cost and effort.
In addition, it’s crucial to consider the scalability and integration of AI solutions into existing systems. This involves assessing the technical infrastructure, data quality, and governance frameworks to support AI initiatives.
Data Architecture Inputs, AI Services, and AI Support
Implementing AI across an organisation depends on a well-structured data architecture that can effectively manage data volume, quality, variety, availability, and velocity.
A robust data architecture ensures that AI systems produce reliable and accurate results, essential for maintaining data integrity and security. Integrated data services can complement this architecture to facilitate seamless data flow and real-time interactions, which are critical for the responsiveness and adaptability of AI applications.
These services help AI systems quickly adapt to changing data and business needs.Equally important is a robust support system tailored to the technical and operational aspects of AI implementation.
This includes maintenance, updates, troubleshooting, and comprehensive training for users across the organisation, ensuring that AI tools are used effectively and contribute to business goals.
Overall, the foundation for broad AI implementation involves a combination of strategic data architecture, integrated services, and practical support systems, all supported by insightful decision-making.
This approach not only facilitates the smooth integration and operation of AI technologies but also enhances their ability to drive innovation and deliver significant business value.
Plan to build organisational buy-in, motivation, and ownership
Implementing AI broadly across an organisation requires more than just technological integration. It requires creating a culture of acceptance and enthusiasm.
The journey to widespread AI adoption is paved by securing organisational buy-in, driving motivation, and ensuring the initiative is deeply embedded in the organisation’s fabric.
The first step in this process is to articulate a clear vision that connects AI to the organisation’s overall purpose. This vision should highlight how AI can solve existing problems, increase efficiency, and drive innovation, making it relevant to all stakeholders.
It is also essential to engage with stakeholders early and often. This includes discussions with teams directly impacted by AI implementations and those whose work will indirectly change.
Training and development programs also play a critical role in building employee competency and confidence. These programs should be tailored to different organisational roles and ensure employees have the necessary skills and understanding to leverage AI.
Input to Decision Support and Communication Plan
An organisational AI analysis is a strategically important step for companies embarking on or expanding their use of artificial intelligence. The AI analysis provides vital insights that aid in decision support and formulating a communication plan, two essential components for successful AI integration.
Decision makers gain a clearer view of where investments and interventions are needed, whether in technology infrastructure, workforce training, or process change.
These insights are invaluable not only for strategic planning but also for developing an effective communication plan. Such a plan is crucial as it addresses how the changes brought about by AI will be communicated across the organisation.
The communication plan, based on an organisational AI analysis, ensures that all stakeholders understand the rationale behind AI initiatives, the expected benefits, and the role of the individual in this transformation. It helps set realistic expectations and mitigate resistance to change.
Overall, conducting an organisational AI analysis allows organisations to approach AI implementation with a well-informed strategy and straightforward communication tactics, increasing the likelihood of a successful implementation.
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© Lars Kirstein | lk@izara.com
