Machine Learning, while a burgeoning technology, remains a novel concept for many businesses, particularly those contemplating a transition towards a more AI and ML-centric organizational structure.
The integration of artificial intelligence solutions into core business processes necessitates meticulous preparation and strategic planning.
This article aims to guide businesses in efficiently adopting artificial intelligence projects and planning for them effectively. The success of an AI project is largely contingent on thorough due diligence, strategic planning, and evaluation prior to the project’s commencement.
Infrastructure
The first aspect to consider is your company’s existing infrastructure, which includes technology infrastructure, platforms, and data infrastructure.
Your company’s technology infrastructure could be entirely cloud-based, on-premise, or a hybrid of the two. Depending on the specific problem, the AI solution for your use case might require minimal computational resources. However, some AI solutions necessitate more robust servers with numerous CPU cores, memory, and GPU/TPU power. Questions such as “What are the main programming languages used in your tech stack? What does the tech stack consist of? What type of API can you integrate with the ML API into the core technology? How much budget and resources can you allocate to the AI initiative?” are crucial. Your preference for certain platforms will influence the choice of technology, platforms, approaches, and solutions for your AI project.
Low-code AI platforms can address simpler data analytics or predictive analytics problems. More complex issues will require skills beyond the scope of low-code platforms, necessitating more sophisticated approaches and the involvement of actual data science professionals in planning, researching, experimenting, and implementing customized solutions.
The answers to these questions will help determine the type of solution you can develop.
Data
The second area is data. Your data infrastructure plays a huge role. Some AI solutions need big data, some only require small data. But the common denominator is Data Quality. Getting quality data is one of the biggest challenges in AI. Data Quality means the data should not be BIASED, it should contain predictive quality, actionable insights, it might need to be consistently formatted; because having unstructured data in heterogeneous formats (from pdfs, text files, SQL and NoSQL, image, logs, etc.) is not ideal.
Aim to have your data properly stored and processed (ideally in structured, at least searchable databases), have the data properly annotated if required; and sometimes, you might need to exceed the minimum data volume required for the AI models.
If you are not sure, try a POC project as the first step.
Human Resources
The third pillar is the human element. The right talent is crucial. Novice or merely competent “data scientists/data/AI engineers” can often result in false starts, deceptive models that seem effective, and a significant waste of time and resources. Beyond project managers and product personnel, you require AI engineers who are well-versed in all aspects of a data science project lifecycle: data engineering, data processing, ML model development, MLops, and the creation of production-grade applications. It’s imperative to onboard the right individuals or AI vendors for your project. The expertise of the right data scientist or AI architect can help circumvent a majority of incorrect approaches, which can potentially cost companies months or even years of investment. Remember, AI investment is a costly affair. It’s important to note that a person’s qualifications and degrees often have little correlation with their ability to deliver on an actual, commercial AI project. This is a lesson I learned from experience.
Planning and Strategy
The fourth pillar is the plan and strategy itself. Strategies are often formulated without a comprehensive understanding of AI capabilities vis-à-vis the organization’s existing infrastructure. This can lead to the selection of unfeasible initiatives that cannot be realized, or an overestimation of AI/ML potential can result in a haphazard approach, leading to vague business objectives, undefined success metrics, lack of clarity, and eventually, project abandonment due to lack of support from senior management. To avoid this, it’s beneficial to bridge the knowledge gap between tech and business personnel, aiming for greater clarity in business outcomes and their connection with product features and technical capacity. Expert review and supervision are invaluable.
Risk Management
The final consideration is Risk Management. Risks in AI projects encompass financial and timeline risks, which could result in budget overruns, delays, and extended time to market. There are also people and process risks, such as the absence of domain expert reviews and supervision, lack of technical skills, over-reliance on third-party platforms leading to a lack of real ownership, vendor lock-in, and lack of control over AI systems. Trust, Ethical, Legal & Compliance risks are also significant, pertaining to the accuracy of the model, potential bias against minority groups, the explainability of AI models, and the degradation of model performance over time or with unseen datasets.
Strategies for identifying and managing these risks will be discussed in subsequent videos and blog posts in this series. Stay tuned for our upcoming content. In the meantime, if you require assistance with AI projects, feel free to reach out to us at Aigenexpert.com.
