Ibrahim Halfaoui on how consultants can find lasting advantages with AI
A recognised consultant in artificial intelligence and digital transformation, Ibrahim Halfaoui helps organisations translate complex technologies into measurable business value. Speaking with the Cyber Security Speakers Agency, he offers valuable insight for consultants and business leaders seeking to turn AI potential into lasting competitive advantage.
Many companies are eager to adopt AI but often stumble along the way. From your perspective, what are the biggest misconceptions about integrating AI into business operations?
So, businesses often assume that AI integration is a quick-fix solution that immediately yields results without proper planning. The misconception lies in the underestimation of the complexity and the time required for successful AI implementation.
In general, there is always a kind of tendency from people to overlook critical aspects such as data quality, infrastructure quality, and the monitoring and maintenance of the model over time when it is in production. All of this leads to unrealistic expectations about the immediate potential and impact of AI on operations.
The main problem businesses face is the proper conceptualisation of an AI project. Before anything starts, businesses and developers need to sit down and put things in context, including critical aspects such as the goals of the AI project, the stakeholders involved, and the data sources to be considered.
According to Gartner estimations, 80% of AI projects fail because these elements are not put into context in advance. Businesses often realise mid-project that they lack essential data or resources, which halts progress entirely.
Having such a high rate of failure underlines the importance of proper planning so that, when a project starts, businesses are well prepared for the risks that could arise spontaneously. This preparation allows them to mitigate those risks in time, cost, and quality.
Beyond the hype, where do you see AI and machine learning delivering the most tangible value for organisations today?
AI has truly transformative potential for businesses across industries and departments. A lot of research and publications have shown how AI is transforming markets both across different industries and within companies themselves.
There are countless techniques and tools through which AI can transform workflows — from predictive analytics and automation of processes to enhancing customer experience and personalising recommendations, products, and services. These all help improve efficiency, save costs, save time, and improve quality.
When businesses consider AI and its related technologies, they gain valuable insights. Even by analysing the data they already have, they can improve decision-making, create innovative products and services, and uncover opportunities that were previously invisible.
How can organisations align AI projects with their strategic objectives to achieve measurable business impact?
Aligning AI initiatives with a company’s strategic goals is key. Before implementation, businesses need to identify specific use cases where AI can improve operations, enhance efficiency, or create new revenue streams. Once that is done, they can invest in implementing robust AI systems, analyse data effectively, and ensure model quality when pushed into production. This is what we call AI quality management.
Many industries, such as medical and health product manufacturing, already understand the importance of quality management in their operations. Recently, such businesses have realised that AI is entering their territory and are asking how they can manage the quality of their AI components.
A proper AI quality management system helps businesses identify and define quality goals for AI components and prepare mitigation actions that ensure those goals are met.
Successful AI adoption depends on people as much as technology. How can businesses build the right skills and culture to make the most of AI?
Indeed, there is a learning curve. As assessors and auditors of AI, we often rely on defining the maturity of businesses in AI adoption. There are generally five stages of maturity:
- Novice – when a company has just heard of AI and begins exploring possibilities.
- Experimental – when people start to play around with AI and explore its use.
- Operationalisation – when AI starts being implemented and brought into production.
- Systemisation – when proper controls, workflows, and pipelines are set up for models in production.
- Transformation – when AI becomes fully integrated, as in the case of companies like Google or Meta.
For people, this learning curve begins with raising awareness and building knowledge of the fundamentals of AI usage and development. This can be achieved through training programmes, workshops, online courses, e-learning resources, AI toolkits, and access to experts and mentors.
Over time, companies need a clear plan to maintain this learning process — encouraging employees to experiment, test, and innovate. Creating a culture that values continuous learning and curiosity ensures people align their personal development with the company’s strategic goals.
