AI is no longer a future concept; it’s here, reshaping industries and driving innovation in businesses. At BetterEngineer, AI remodeled our approach. We had to embrace these transformations, adapt, and integrate them into our services. To lead this charge, we’ve brought on board Marc Boudria as our Chief Innovation Officer. With over 25 years of experience in digital strategy, innovation, and technology development, his work spans a diverse range of fields, with a particular focus on AI and custom solutions across various industries.
With a passion for exploring “the art of the possible”, Marc has held leadership positions at companies including Chaotic Moon, Accenture, HyperGiant, and NTT Data. In a recent Q&A for BetterEngineer, Marc shared rich, detailed content on AI, offering valuable insights into its potential and challenges. By reading this article, you’ll start questioning yourself: “Am I Using AI the right way?” For any business owner out there, we guarantee his singular perspective will make you rethink your AI strategy.
Marc Boudria: The best way to start is by examining your user experiences and identifying what their pain points are. Whether it's your employees interacting with a system or your customers, find out where the friction lies. Once these pain points are clear, you can objectively question how AI can address them. It's important to understand that AI might not always be the answer—and that’s okay. Sometimes, you can achieve what you need by simply thinking differently and questioning existing workflows and interactions. That can lead to innovative solutions.
Often, limitations in other areas create these pain points, and those are the spots where AI can have the most impact. It’s about looking beyond the obvious and recognizing that the real opportunity for AI might be in areas that aren’t immediately apparent.
Marc Boudria: Data is your first problem when you're trying to do AI initiatives. To be more precise, it’s the misunderstanding of what the word “data” means. Many businesses mistake data for human-readable information, but for AI, data needs to be much more than that. The quantity, structure, accessibility, and format of data are equally important. Most businesses don't have a centralized data lake or the necessary infrastructure to take full advantage of what AI services can do. Instead, they rely on fragmented data pockets, which is useful and helpful, but you're not industrializing that ability. To succeed, businesses need to move beyond these one-off experiments and work towards industrializing their data processes.
However, data is just one piece of the puzzle. AI implementation also requires substantial investment in talent and technology, which can be costly. Businesses must integrate AI systems with existing IT infrastructure, ensuring compatibility and alignment with overall business strategies. Additionally, managing the ethical and regulatory landscape, scaling projects from prototype to production, and overcoming organizational resistance are all significant challenges that require strategic planning, execution, and staffing. Successfully industrializing AI processes involves addressing all these facets to truly harness AI's potential.
Marc Boudria: Prioritizing AI projects should start with those aligned closely with strategic business goals and where data is readily available and manageable. Projects with high potential ROI, significant business impact, and lower barriers to entry due to existing data infrastructures, like abundant sensor data, are ideal candidates. It is also relevant to evaluate the feasibility, risks, and modifications of business processes as well. The technical viability of the projects and the support from the various stakeholders do have an impact on the success of the projects. Resource management is not only about having the right headcount but also about having the right skills and experience in the team as per the requirements of the project.
As for the team structure, I prefer lean and multidisciplinary teams rather than large, unwieldy ones, especially if the project scope doesn't demand extensive manpower. A good AI team is not only composed of data scientists but engineers that are capable of scaling and integrating the solutions into broader business systems. A smaller, well-rounded team can be more efficient and productive since people are not bogged down with complex internal dynamics while striving to deliver impactful solutions.
However, it’s essential to emphasize the industrialization of AI projects. Sometimes, a project that may take 15 weeks may only spend a small portion of the time actually doing data science. The rest is used to build the required infrastructure and integrate the solution into existing systems. This integration phase is actually where many businesses face significant challenges. Companies need to understand that a prototype developed in Python—or any other environment—is merely the first step; transforming it into a robust system capable of serving thousands requires considerable additional effort.
Marc Boudria: AI hiring machines often reduce candidates to a set of checkboxes, which can exclude qualified individuals who don't fit a standard profile. Many talented people I know never get jobs through a staffing organization because they don't fit into a box. However, their experience and track records are super powerful. Skills are important, but it's the secondary characteristics, such as creativity, adaptability, and problem-solving abilities, that truly matter. Skills are necessities that you have, they're not the reason that you hire somebody. But yet that's the way these systems are designed, like query filter mechanisms. I think it's the wrong way to think about the role that AI can play in staff augmentation. AI should support the hiring process, not dominate it.
Marc Boudria: The value of AI relies on those who are proficient in this technology. I was reading some statistics and according to the World Economic Forum, AI is predicted to generate 133 million new jobs by 2030. One thing I always talk about is: AI is not going to take your job. Somebody using AI is going to take your job. If you hire individuals who are not familiar with or resistant to AI, how can you expect this knowledge to be integrated into your workforce and workflows?
Now, as for the hiring process, AI should help us move beyond treating people like checkboxes. Traditional AI staffing systems often filter candidates based on rigid criteria, missing out on the nuances that make someone a good fit for a role. Companies should recognize that data is multidimensional. Using different algorithms, they can extract information from the natural language in articles, resumes, interview transcripts, or any other text that may be relevant. This provides a holistic picture of an individual, the factors that really matter when it comes to hiring. And remember, AI tools are just that—tools. They should help human decision-making, not replace it.
Marc Boudria: It’s unethical to reduce people to a set of checkboxes. AI systems that only consider qualifications without context can overlook individuals who don't fit the standard mold. Beyond this, companies should also be cautious about where their data comes from and how it’s used. There’s a fine line between leveraging data responsibly and engaging in practices like data laundering, where ethically questionable data sources are cleaned and used for business purposes. Some companies collect data from millions of websites and sell it to other companies. While the data itself might seem legitimate, the way it was obtained was unethical. I think businesses need to be vigilant about maintaining ethical standards in all aspects of AI implementation.
Marc Boudria: I think the future will bring success to those who are willing to change and failure to those who just keep doing the same. This technology stack demands that you think differently about how you use them in your business. In the past, technology was a digital version of traditional methods, like tablets versus papers. The information was the same, we just got a new way of accessing this. But AI is a game-changer, and you can't simply add it to your existing work methods. You need to rethink how you do things. So I think success in the future will depend on adaptability.
Also, privacy and ethics are going to be the most important topics in the future of AI. Before, people were more concerned with getting things done than being ethical. Now that AI is slowly becoming part of our lives, we should prioritize responsible data usage and ethical algorithm creation.
As Marc Boudria mentioned, AI is already here and companies need to adapt to it. Are you ready to discover the full potential of AI for your business?
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