3 Keys to Success with AI

Note: This article was last updated September 2025. 

Key Takeaways

• In order to successfully implement and leverage AI capabilities, businesses will need to be able to define clear AI goals and strategy, set boundaries when working with AI to ensure accurate and relevant results, ensure data is ready for AI tools, and focus on AI security and governance.

• AI for the sake of AI will not be successful, but AI systems work effectively when there is a clear plan to use AI to automate business processes, interpret data, improve customer experiences, and more. Effective communication within teams and with AI systems enhances collaboration and decision-making.

Business leaders are all racing to get to the top with artificial intelligence (AI), but how can they make this journey successfully?

AI is now at the forefront of all business leaders’ minds. The rapid adoption of AI is transforming the business landscape, with every company seeking ways to integrate AI into their core business functions. From automating routine tasks to enhancing decision-making, companies are leveraging AI to create value and improve processes across the enterprise.

As AI becomes more widespread, many firms are successfully implementing AI technologies in real-world settings, demonstrating both practicality and cautious adoption of advanced AI tools. To ensure long-term success, companies must consider responsible AI development, including governance, safety, and ethical considerations, as they integrate AI into their workflows.

Introduction to Artificial Intelligence

Artificial intelligence (AI) is rapidly transforming the modern workplace, much like the steam engine revolutionized industries during the Industrial Revolution. Today, companies are embracing a wide range of AI tools and generative AI systems to enhance business processes and improve efficiency across every department. From automating routine tasks to generating new content and insights, AI systems are becoming essential tools for businesses seeking to stay competitive.

The adoption of AI is accelerating at an unprecedented pace. According to recent research from McKinsey, AI could unlock as much as $4.4 trillion in long-term business productivity growth.

In fact, 78% of surveyed companies report using AI in at least one business function, underscoring the widespread integration of artificial intelligence into daily operations. As AI adoption continues to climb, it’s crucial for companies to develop the competencies needed to seamlessly integrate AI systems into their workflows. By doing so, businesses can maximize the value of AI, streamline operations, and position themselves for long-term success.

As eager as your business may be to adopt and leverage AI tools, it’s not as simple as just using generative AI systems (GenAI) or implementing machine learning (ML) models in your operations. Like any technology, AI requires strategic and thoughtful execution in order to bring the most benefit to your organization. Soft skills, such as collaboration and problem-solving, are essential for successful AI adoption. Teams need to be skilled in both technical and interpersonal aspects to thrive in an AI-driven environment. Let’s slow down and really examine what factors will help make your AI strategy successful.

Building AI Foundations: Understanding AI Capabilities

To unlock the full potential of AI tools, it’s essential to first understand how AI systems work and what they can—and can’t—do. At their core, AI systems process vast amounts of data, recognize patterns, and make predictions or decisions based on that information. Generative AI systems take this a step further by creating new content, such as text, images, or videos, tailored to specific inputs.

Building a strong foundation in AI starts with understanding these capabilities and their limitations. Not every business challenge can be solved by AI, and knowing where AI excels is key to effective human-AI collaboration. This is where critical skills like data literacy, critical thinking, and problem-solving come into play. By developing these skills, individuals and teams can interpret data, ask the right questions, and work alongside AI tools to generate meaningful results. Embracing these foundational skills empowers businesses to leverage AI systems effectively, drive innovation, and create value in ways that were previously unimaginable.

Understanding AI Outputs

Interpreting AI outputs is a critical skill for anyone working with AI systems. While AI-generated insights can be powerful tools for informing business decisions, it’s essential to approach them with a discerning eye. This means recognizing potential biases, evaluating the quality and reliability of AI outputs, and applying critical thinking to make informed decisions.

Developing AI skills such as data literacy and a solid understanding of machine learning algorithms enables teams to assess AI-generated insights accurately. By doing so, companies can use these outputs to identify new ideas, improve efficiency, reduce costs, and stay ahead of the competition for years to come. Ultimately, understanding and leveraging AI outputs effectively empowers businesses to reach their full potential and achieve lasting success.

3 Keys to Successfully Implement AI Tools

Successfully implementing AI tools requires a strategic approach built on a few essential pillars. First, companies need to identify where AI can deliver the most value. Second, developing robust AI skills—such as data literacy, critical thinking, and problem-solving—is crucial for teams to work effectively with AI tools and interpret their outputs. Finally, responsible AI adoption is paramount. This means involving ethics specialists, addressing potential biases, and ensuring transparency and accountability throughout the AI lifecycle.

To unlock the full potential of AI, achieving benefits like improved efficiency, reduced costs, and enhanced customer experiences, let's dive deeper into these 3 essential elements for successful AI adoption.

1. Have a Clear AI Strategy and Goals

   This first point might seem self-explanatory, but don’t just adopt AI for the sake of jumping on the AI bandwagon. 

   Although it might seem necessary to adopt AI as quickly as possible to stay ahead of current market trends, being first isn’t always an advantage. AI projects tend to be complex and costly, and you’ll need to prove AI’s benefit to the other stakeholders in your business. But without careful consideration of objectives, goals, and requirements, you won’t be able to define AI’s outcomes or success. According to a recent Gartner survey, 37% of organizations are still looking to define their AI strategies, while 35% are struggling to identify suitable use cases.

   To start AI journey on the right food, answer these three questions: 
   • What are your AI objectives or business cases? 

     Defining a clear AI strategy is critical; otherwise, how will AI really help your business? Gartner found that only 9% of businesses have an AI vision statement in place, and more than one-third had no plans to draft an AI vision statement.

     Identify specific business areas where AI can create value and what business cases you want to use AI for. This could include automating repetitive tasks, improving customer service with chatbots, analyzing customer sentiment to better understand your customers, or drawing deeper insights from data. AI can also drive value in inventory management, marketing, and supporting sales teams with personalized engagement and automation.

   • What are the goals you want to accomplish with AI? 
     Again, AI should help your business achieve specific outcomes. Measuring those outcomes can help determine how effective your AI strategy is and provide feedback for fine-tuning and iteration. Choosing the right KPIs and metrics requires aligning those objectives with broader business goals. For example, if one of the business objectives you set is to improve operational efficiency, then a few KPIs to track to see if AI is streamlining business processes include:

     • Time to completion. Has there been a reduction in process time due to AI automations in place? 

     • Error rates. Have error rates decreased since AI has improved accuracy in processes such as data entry or calculations? 

     • Total downtime. Has AI been successful in predicting maintenance needs and alerting the team for critical fixes, ultimately reducing the total amount of downtime and ensuring the business stays live? 
        

   • What are the requirements needed to achieve these AI goals? 

     Fundamentally, AI will require substantial cultural change in your organization. Many employees may be scared or hesitant to embrace and leverage AI. Business leaders must acknowledge these changes and be proactive about mitigating these fears to show their teams the success and benefits AI can bring. Your organization should also be prepared to provide adequate education to employees surrounding your AI implementation. Cultural change will be difficult, but without a team that is fully onboard with AI, these initiatives will never truly take hold. 

     Not 100% sure what AI is? Learn about key AI concepts and how ML and GenAI can provide business value to enterprises in our blog post. 

2. Ensure Your Data is Ready for AI 

   Data is the foundation of AI. If garbage data goes in, then your AI models will only produce garbage back. 

   For most organizations, their existing data architectures are not made for the AI era. According to Accenture, only 1 in 5 companies excel at maximizing value from data. Additionally, data access is still very challenging for many organizations. 90% of business data is unstructured and may be separated in silos. All these factors make it difficult to make AI a reality. 

   But AI algorithms need a large quantity of data to help them learn, adapt, and make more effective decisions. All aspects of AI, including ML models, continuous learning, Gen AI, and descriptive analytics, are dependent on massive data sets. Not only do these algorithms need a lot of data, they need good data. Bad quality data will not only create bad outputs, but also train the model incorrectly for future computations and predictions. 

   Although AI relies heavily on data for its operation and evolution, the reverse is also true: data can benefit from AI in several ways. For example, AI can help automate data management tasks, making it easier to process, clean, and organize large datasets. AI can also help uncover patterns and insights in data, making recommendations from that dataset. 

   The first step is to get your data ready to be used for AI. Whether that means removing data silos, integrating disparate datasets, or future-proofing your data strategy, your data outcomes can only be as powerful as your data foundation allows for. 

   Mckinsey defines AI-ready data as known, understood, available, fit for purpose, and secure. Gartner adds that AI-ready data must be free of bias, accurate, and ethically sourced. 

   However, according to a recent Google survey, only 44% of respondents were confident in their organization’s data quality. 
   
   So what makes data AI-ready? 
   Data that is truly optimized for AI has a few key characteristics: 

   • The data is readily available and accessible. If AI models can’t access relevant data, then the recommendations or actions could be inaccurate because of the lack of information. 

   • The data is accurate and provides the right context with active metadata. Again, if data is inaccurate or not up to date, then the AI model’s accuracy gets compromised. AI can even be prone to “hallucinating,” inventing information, or providing incorrect information. 

   • The data is consistent. For example, if data has inconsistent formats or entries, this makes it difficult for AI models to aggregate and analyze effectively. 

   • The data lineage is properly mapped. Ontology, mapping data assets and their relationships, is critical to developing AI systems. Otherwise, AI models can only develop in a piecemeal, fragmented way. 

   • The data is secure. Ultimately, data must be secure and ethical in order to be leveraged. We’ll touch more on data security in the following section. 
      

3. Focus on AI Governance and Security 

   As AI becomes more integrated into your business landscape, your organization will need to ensure it is being used responsibly and safely.

   This involves: 

   • Creating an AI governance team that sets standards and regulations for the organization. This body should be up-to-date on the latest regulations and laws to ensure that the internal controls align with external laws. 

   • Proactively monitoring and managing risk. As AI becomes more widely used and sophisticated, ensuring its security is critical to prevent attacks and data leaks. Your organization will need to dedicate a team to not only protect the AI models themselves, but also the data used to train those models and the data that is processed by those systems. 

     In addition to monitoring for suspicious attacks, this team should also be responsible for enhancing your AI systems’ resilience against potential attacks and training those systems to improve their robustness. 

   • Training and educating employees. Using AI responsibly starts internally. Your employees may be hesitant to embrace AI, especially if they’re afraid it will take over their jobs. So gaining buy-in and alignment is critical to AI adoption. For example, you can share that AI can bring tangible benefits such as increasing productivity by 40%. When your team is on the same page, only then can AI truly be successful. 

     Your team, or the people who might be leveraging these AI systems, need to understand how to correctly and safely use them. Effective AI governance will take time, but it’s extremely critical to ensuring that your business’s AI strategy is compliant and minimizes potential harm.

Responsible AI Adoption

Adopting AI responsibly is essential for building trust and ensuring long-term business success. Responsible AI adoption means prioritizing transparency, accountability, and fairness in every aspect of AI decision-making. Companies must be vigilant about identifying and addressing potential biases in AI systems, ensuring regulatory compliance, and protecting sensitive customer data.

Investing in AI talent—such as AI engineers and data scientists—is critical for developing and deploying effective AI solutions. These professionals, along with ethics specialists, help organizations navigate complex ethical considerations and implement safeguards that prevent misuse of AI systems. By fostering a culture of responsibility and ethical awareness, businesses can minimize risks, maximize the benefits of AI adoption, and drive sustainable growth in an increasingly AI-driven world.

Improving AI Adoption and Implementation: Using AI Effectively

So, before you jump into the AI race, consider these three factors and how you can prepare your business to effectively take advantage of all AI can offer. The possibilities of AI are exciting, but if your business isn’t prepared to properly leverage AI, ultimately, you’ll still lag behind. Businesses should also plan for the years ahead by anticipating future skill requirements and workforce impacts of AI to ensure long-term success.

Ready to find out how you can implement AI in your digital solutions? See how AI is integrated all throughout the most flexible Digital Experience Platform, Liferay DXP.