From Planning to Execution — How to Make AI Adoption Work
- elenajfremova
- May 24
- 3 min read
As an engineering manager focused on digital transformation, I've seen how leveraging Artificial Intelligence (AI) can lead to meaningful change, streamlining workflows, enhancing customer experiences, and revealing new opportunities for innovation. You don't have to be a data scientist to leverage AI successfully. Clear strategic planning and thoughtful implementation matter most.
Here's what I've learned through real-world projects and research that can help guide your organisation on its AI journey.
Understanding the AI Landscape
My introduction to AI highlighted its impressive progress since its beginnings in the 1950s. This advancement can largely be attributed to the increasing computing power outlined by Moore's Law, which has opened up new possibilities for the technology. From practical experience, these are the key AI technologies that matter most in organisational contexts:
Machine Learning (ML) involves algorithms that learn patterns from data. I've used ML effectively for predictive analytics and customer segmentation.
Deep Learning leverages neural networks modelled after the human brain, which is essential for complex tasks such as autonomous vehicles and voice recognition systems.
Generative AI creates original content and insights, such as text or images, significantly enhancing creativity and productivity.
AI has become critical to many industries, from enhancing customer service to enabling accurate predictions in finance and healthcare.
One crucial insight I've gained is the importance of defining clear business objectives before adopting AI:
Automation of repetitive tasks significantly frees up human resources for strategic initiatives.
Enhanced customer experiences through personalised interactions and efficient, intelligent customer support systems.
Predictive analytics facilitate informed decision-making, improving forecasting accuracy and resource allocation.
Salesforce research backs my experience, noting that over two-thirds of IT leaders prioritise AI for efficiency and better customer interactions.
Assessing Your Current Capabilities and Infrastructure
Evaluating current capabilities is essential, and these factors greatly impact success:
High-quality, clean data is fundamental - poor data quality can quickly disrupt well-planned AI initiatives.
Cloud solutions have frequently offered scalable, cost-effective solutions.
Realistically evaluating your team's skills determines whether external support or additional training is needed.
The McKinsey Global AI Survey confirms that organisations that carefully manage data quality and talent are more successful.
Clearly defined roles are essential for successful AI implementation. Engineers are key in constructing and integrating robust technical solutions, while data analysts and data scientists are essential for maintaining model accuracy and ensuring data quality. Additionally, project managers are indispensable, as they enhance communication and ensure that teams remain aligned and focused every step of the way. Deloitte's AI and Workforce study emphasises the importance of clearly defined roles. When everyone knows what they're supposed to do, productivity boosts and project disruptions are avoided.
Choosing Between Homegrown and Third-Party Solutions
One of the most critical decisions in adopting AI is whether to build in-house solutions or partner with third-party providers. While I haven't personally implemented both approaches, my research and discussions with peers in the industry have shown that this is a crucial step in the AI journey.
Homegrown solutions provide flexibility and the opportunity to tailor systems precisely to your organisation's needs. However, they demand significant internal expertise and long-term investment to scale effectively. On the other hand, third-party platforms often offer mature, scalable tools with ongoing vendor support. They can accelerate your AI initiatives but may involve higher upfront costs and integration complexity.
Before making a choice, I recommend thoroughly evaluating your organisation's long-term scalability goals, internal technical capabilities, and available budget. Understanding these variables can guide you toward the most sustainable and strategic option.
Establishing an MLOps Framework
MLOps (Machine Learning Operations) has significantly streamlined the implementation and management of AI systems:
Tracking experiments systematically.
Managing model and data versions effectively.
Streamlining model deployment.
Continuously monitoring and refining performance.
Integrating AI into existing CI/CD workflows enables teams to adapt more smoothly. When AI is viewed as an enhancement to current systems rather than a separate initiative, it is crucial to prioritise ethics, privacy, and responsibility by sticking to regulations like GDPR and ensuring transparency and accountability in AI-driven decisions.
Continuous education plays a vital role in the success of AI initiatives. Regular, targeted training helps teams build confidence with new tools while fostering a culture of innovation, creating the psychological safety needed to explore, iterate, and grow alongside evolving technologies. Salesforce's Digital Skills Report aligns with my observations, highlighting that organisations investing in continuous learning consistently exceed their peers.
Integrating AI can vastly change your organisation's culture, operations, and strategy. Defining clear objectives, conducting accurate assessments, implementing structured processes, and engaging in continual improvement are fundamental to successful AI integration.
AI's true power lies in empowering your people to grow, collaborate, and create.
Adopt AI responsibly and strategically to enhance organisational success and contribute to a better future.
