For Luís Filipe Gonçalves, data and AI expert at Noesis, a solid foundation is the key to successful AI implementation, so take the time to get it right, he advises.
From predictive analytics to advanced data visualization, artificial intelligence (AI) promises to revolutionize the way businesses process and leverage information. Yet, before we rush headlong into an AI-driven future, we need to ask ourselves a crucial question: are we really ready?
The tools we have, such as Qlik, Power BI and Tableau, offer incredible capabilities when it comes to data analysis. But are our organizations, equipped with all the systems, infrastructure and people, ready to embrace AI? And even more for the rush towards generative AI?
AI hype and reality
AI is often touted as the ultimate solution to all data challenges. From automation to real-time insights, the promises of AI are vast. But implementing AI in data analysis is not as simple as flipping a switch.
Many companies embark on AI initiatives without adequate foundation, leading to frustrations, missed opportunities and disappointing results. Without true data-driven insight, AI will be another idea, not a business decision tool.
Before adopting AI, businesses must first carefully examine the state of their data ecosystems. AI thrives on large amounts of clean, structured data, but how prepared are most businesses to handle this flow of data? Without a proper foundation, AI cannot exploit its full potential.
Know the present before changing the future
Data visualization tools like Qlik, Power BI, and Tableau have already changed the way we interact with data. They help businesses visualize trends, drill down into details, and make informed decisions quickly. These platforms provide an essential foundation for any AI initiative, but their full potential can only be realized if businesses address the underlying data challenges.
Having the ability to explain the past is essential to predicting the future with AI. It is mandatory to have this capacity within our organizations because, if we do not know our past, we will not be able to look to the future. And we can only understand our past business decisions, past goals and targets if we have tools that can support this data mining.
There are some vital areas where preparation is essential.
Data Infrastructure
Before diving into AI, businesses should evaluate their existing data infrastructure to ensure it is scalable and flexible enough to integrate advanced AI algorithms. AI relies heavily on fast and reliable access to data. If your infrastructure can’t support a large-scale, real-time data stream, AI will struggle to deliver value. Scalability is the key driver of the organization’s data infrastructure.
Data quality
“Garbage in, garbage out” is a phrase often used in the data world. AI needs clean, high-quality data to work effectively. Although Qlik and Power BI have powerful data cleansing tools, the responsibility for ensuring data integrity ultimately lies with the business. If data is incomplete, inconsistent, or outdated, even the most sophisticated AI system will produce erroneous information.
Integration capabilities
Existing tools work best when integrated with other systems within the company. This begs the question: can your current ecosystem handle such integrations seamlessly?
AI can amplify insights when connected to CRM systems, marketing platforms, and other business software, but the integration capabilities of your current setup must be robust enough to support this interaction.
A non-compartmentalized strategy is mandatory. If AI is another set of tools, another application disconnected from the ecosystem, AI will never be a status quo strategy. It will just be another tool.
Talents and people
Even with the best tools in hand, the human element remains crucial. AI adoption requires data literacy across the entire organization. Reskilling and upskilling is necessary to ensure your employees can navigate this transition smoothly. Organizations can have the best strategy, the best toolset, a well-defined framework, but without trained and talented people, an organization will not get the most out of this data-driven insight.
Collaboration, not replacement
AI is not there to replace existing tools, it is there to improve them. These platforms have evolved to incorporate AI capabilities, such as predictive analytics and natural language processing, but that doesn’t mean they become completely autonomous.
Organizations should view AI as an enhancement to human decision-making. AI can quickly process large data sets, but human intuition and strategic thinking will always play a central role. In fact, many experts argue that AI works best when combined with human intelligence, especially in complex, context-specific decisions. The final call should always be made by business owners, not by algorithms or generative AI results.
What if you’re not ready for full AI?
Even though the benefits of AI are clear, not all companies are immediately ready to implement it at scale. But here’s the trick: you don’t need to announce that you’re not ready for AI. You can embark on “pre-AI” initiatives without the world knowing that you are still preparing for the big leap to AI.
Understanding the organization’s analytics maturity curve is a business differentiator. If we know where we are, we can define where we were and how we want to proceed as an organization. Conducting this type of assessment can map the organization’s status quo and reveal hidden capabilities already in place throughout the organization.
Launch an AI-FTF (AI Foundation Task Force). This is not a pre-AI project, but a strategic initiative that focuses on creating the data, infrastructure and skills needed for a successful AI-driven future.
Other initiatives that you can freely use include:
AI-DRP (AI Data Preparation Program)
This focuses on cleaning, structuring and optimizing your data so that when AI is fully deployed, the transition is smooth and efficient.
AI-SIP (AI Systems Integration Plan)
This ensures that your existing platforms can seamlessly integrate with AI tools and algorithms, preparing for a future where these systems work hand-in-hand.
AI-TSP (AI Talent Skills Program)
This is designed to upskill your team, ensuring they can maximize the potential of AI once deployed, and helping them understand how AI will reshape their roles and responsibilities.
By focusing on these initiatives, your organization can make real, impactful progress toward an AI future without compromising current operations. These are not just readiness projects, but crucial steps in laying the foundation for future AI success.
Is your business ready for AI?
While AI is undoubtedly the future of data analytics, it’s not about being first, it’s about being prepared. By ensuring your data, infrastructure and people are ready, businesses can get the most out of AI without the pitfalls of poor implementation.
Qlik, Power BI and Tableau will continue to play a crucial role in bridging today’s analytics needs with tomorrow’s AI-driven innovations. But as with any technological leap, success lies in preparation. Before adopting AI, look back and make sure your foundations are solid.
The future may be exciting, but preparation will always be key. Ensure your organization is truly at the forefront of AI transformation, whether today or tomorrow.
Luís Filipe Gonçalves is Director of Data Analytics and AI at Noesean international technology consulting firm. He has nearly 20 years of experience in data analytics, business intelligence, QlikView and SAS. Currently, he leads a team of over 100 professionals, driving data and AI-driven innovations at Noesis.
Don’t miss out on the knowledge you need to succeed. Register at Daily BriefSilicon Republic’s summary of essential science and technology news.
