Is Your Data Ready for AI? Lessons from Lingaro and Galderma’s Webinar

AI adoption is widespread in many industries and touches almost every part of modern business operations. It can handle routine tasks and deliver advanced analytics, making its potential seem endless. Yet, many companies still struggle to turn AI projects into real, measurable results, even with growing excitement and investment. The promise of AI often falls short because of organizational and technical challenges.

A recent Lingaro webinar on AI readiness tackled these challenges head-on. It explained common mistakes that block successful AI implementation and shared a clear roadmap for becoming AI-ready. Attendees also saw AI in action, learned how agentic commerce works, and discovered a proven pathway to AI-driven commerce excellence — featuring a real-world case study with Galderma and actionable insights for the future.

Krystian Jabloński Lingaro’s Polish Site Leader and Generative AI Expert and Murat de Picciotto, Global Head of E-commerce at Galderma shared useful tips from their work with AI. Their discussion showed that being ready for AI isn’t just about using new AI technologies. It also means building a complete environment with strategy, data integration, governance structures, and the right organizational culture.

Why AI Readiness Matters

The webinar showed that the road to AI success is full of obstacles. Many organizations face common barriers that slow progress and prevent them from reaching AI’s full potential. These challenges often appear in key areas:

• Poor data quality emerges as a primary concern, eroding trust in AI outputs and undermining decision-making processes. If the data used in AI models is wrong, missing, or inconsistent, the insights will be flawed. This can lead to poor strategies and costly mistakes.
• Weak user adoption is a significant challenge. Teams may resist or avoid using AI tools. This happens when they do not understand, value, or see these tools as useful for their daily tasks. To overcome this resistance, we need to educate users. Also, creating easy-to-use interfaces that fit well with current workflows is important.
• Unclear ROI makes leaders skeptical. They may wonder if AI investments are giving enough returns to cover the costs. Demonstrating the tangible business value of AI requires establishing clear metrics, tracking progress, and communicating successes effectively to stakeholders.

The Lingaro webinar shared deep industry insights. It showed a worrying trend: many companies do not have a clear AI strategy. This lack of clear strategy often causes scattered efforts, mismatched priorities, and a failure to use AI effectively.

On top of that, ongoing data quality issues make AI implementation even harder. Preparing data to be AI-ready isn’t just technical — it’s strategic. It enables faster, more accurate outcomes and supports confident, data-driven decisions.

The discussion emphasized that without robust data and AI governance frameworks, businesses inevitably encounter a range of problems. These include less accuracy, slower processing speeds, and less user alignment. All these issues weaken the impact and use of AI solutions.

The impact of data & AI governance is significant. Poor data reusability leads to problems with integration and consistency. This creates information silos and makes it hard to use data across different applications and business units. Organizational gaps in frameworks, skills, and KPIs (Key Performance Indicators) further impede effective execution and value realization.

Addressing these numerous challenges is crucial for achieving a meaningful return on investment (ROI) from AI initiatives. Organizations face several challenges. These include delayed delivery and trouble finding data. Also, a lack of asset reusability exists. Data quality problems are common, too. Many organizations lack government control. Accessing data can be difficult. Additionally, some data is outdated or insecure.

Three Stages of AI

The webinar provided a clear and helpful way to understand how AI has developed. It divided this process into three main stages:

• Wave 1: Generative AI – This first wave includes general-purpose AI tools like ChatGPT. These tools give wide-ranging answers and create content using publicly available data. This stage is often a starting point for organizations looking into AI. It lets them try out different uses and get to know the technology. Galderma's AI falls into this category.
• Wave 2: Synthesis AI – In this stage, we connect AI models to a company's own data. This helps provide more accurate and relevant insights. By training AI with their own data, organizations can better understand their customers, operations, and markets. This leads to better decision making.
• Wave 3: Agentic Systems – This is the most advanced stage of AI development. In this stage, AI systems can act on their own. They can use different tools and platforms, like CRM (Customer Relationship Management) and e-commerce systems. These AI agents can automate complex tasks, optimize processes, and personalize customer experiences in real-time.

To effectively navigate these stages and unlock the full potential of AI, leaders need to prioritize several key areas:

• Maintaining clean, high-quality data is paramount. 
  Organizations need to invest in data governance, data quality tools, and data cleansing methods. This will help them ensure that their AI models train on reliable and accurate information.
• Automating tasks can significantly enhance efficiency and productivity.
  By automating repetitive and manual processes, organizations can free up human employees to focus on more strategic and creative activities.
• Using chat interfaces can improve communication and collaboration. 
  Chatbots and conversational AI help people interact easily with AI systems. This makes it simpler for users to get information, ask for help, and give feedback.
• Upgrading systems is essential to support the evolving needs of AI. 
  Organizations must make sure their IT systems, data storage, and computing resources can handle AI workloads.

Strong guardrails are critical to ensure responsible and ethical AI deployment. Organizations must establish clear guidelines and policies to prevent bias, protect privacy, and mitigate the risks associated with AI.

The speakers emphasized the importance of better retrieval methods, suggesting the adoption of advanced tools like knowledge graphs. Knowledge graphs help AI models understand the context and relationships between data points. This leads to more accurate and relevant insights. 

As organizations use AI agents more in real applications, they need to create basic governance frameworks. This will help us ensure that people use AI responsibly and ethically.

A Skincare Company’s Success

Galderma is a well-known skincare brand. It started using AI to increase engagement and sales during live streams in Asia.

Galderma knew that speed and accuracy are crucial in this fast-changing area. They wanted to use AI to improve customer experience and boost business growth.

The solution was to use an AI assistant. This assistant could give product information and answer customer questions in seconds. This was drastically different from the minutes it took for human agents to respond. The impact of this AI-powered assistant was immediately apparent:

• Teams saved a lot of time because the AI assistant managed many customer questions. This allowed human agents to focus on more complex and important tasks.
• They cut the training time for new hires by fifteen times. The AI assistant gave easy access to information and guidance, speeding up the onboarding process.
• Sales in the Philippines saw a strong increase of ten percent. This shows a clear link between using AI and better business performance.

To measure progress and improve the AI solution, Galderma tracked key metrics. These included host readiness, answer accuracy, sales impact, and overall adoption rates. The company's main goals were to teach customers about the science of its products. They also wanted to create more interactive chances through content and live Q&A sessions.

Practical Tips for Commerce

Based on the ideas shared in the webinar, here are some steps businesses can take to improve their AI readiness:

• Leverage AI to summarize information for quicker and more informed decision-making. 
  AI-powered summary tools can turn large amounts of text into short and clear summaries. This helps decision-makers quickly understand the main points and make timely choices.
• Automate tasks to streamline operations and improve scalability.
  By automating repetitive and manual processes, organizations can reduce workload, minimize errors, and free up resources to focus on more strategic initiatives.
• Employ conversational analytics to identify pain points and improve adoption rates. 
  Conversational analytics tools can look at customer interactions and feedback. They help find where AI solutions are not working well. This allows organizations to improve their methods and boost user satisfaction. 

Strike a balance between AI-generated content and human-made content to foster trust and engagement. AI can help create content, but it is important to keep a human touch. This helps ensure authenticity and build trust with customers.

Ultimately, strong data foundations, clearly defined goals, and consistent measurement are the cornerstones of successful AI implementation. With a strong foundation, companies can create AI solutions that provide real value and lead to important business results. The discussion showed that using AI requires a new focus on trust, risk, rules, and security management. It underscores the need for a formal governance framework to ensure scalability and maximize return on investment (ROI).