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

Although personalization might have been the hot topic a few years ago, AI is now at the forefront of all business leaders’ minds. In 2023, 79% of corporate strategists said that technology like AI would be critical to their success in the next two years. 

As eager as your business may be to adopt and leverage AI, it’s not as simple as just using GenAI or implementing ML models in your operations. Like any technology, AI requires strategic and thoughtful execution in order to bring the most benefit to your organization. 

Let’s slow down and really examine what factors will help make your AI strategy successful. 

3 Keys to Successfully Implement AI 

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, or drawing deeper insights from data. 

   • 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.

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.

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.