Looking Ahead: AI Trends for 2023

Looking Ahead: AI Trends for 2023

Over the last decade, Artificial intelligence (AI) has become deeply embedded in every aspect of our society and lives. From smart assistants to self-driving cars, automated healthcare management, virtual assistants, and smart industrial machinery, AI is changing the way business is run in major industries.

However, we firmly believe this is only the beginning. As technology evolves, we’ll see more AI use cases in our everyday lives, utilizing data more effectively and delivering an enhanced experience. IDC research predicts that worldwide spending by governments and businesses on AI technology will reach $500 billion in 2023. 

With this fast-growing market in mind, what advancements can we expect to see next year? This blog explores the key trends our AI experts at Wizeline believe will dominate the world of AI in business in 2023. Let’s dive right in! 

5 AI Trends to Look Out for in 2023

Generative AI (GAI)

Generative AI refers to machine learning algorithms that enable computers to use existing content like text and media files or even code to create brand-new content. For now, there are two major use cases for generative AI models: 

  • Generative Pre-Trained (GPT) language models using information collected on the web to create text content like blogs, press releases, or whitepapers.
  • Generative Adversarial Networks (GANs) which are used to create visual and multimedia artifacts from imagery and text data.

Generative AI technologies can build a 3D render from a pencil sketch or a process described in natural language. In 2023, I see GAI merging with no-code technologies to enable innovative things like a program automatically generated from a business process modeling (BPM) diagram, for example."

Heriberto Perez Peñuelas

Wizeline Technology Director

According to Gartner, the percentage of data generated by generative AI will amount to 10% of all generated data by 2025. In the coming year, we’ll likely see Generative AI applications across industries like logistics to accurately convert satellite images to map views or in marketing to auto-generate marketing messages. 

Accessible ML Models

With advancements in machine learning, we see more and more cloud-native solutions lowering the technical difficulty of developing and deploying ML models, making the potential of machine learning more accessible to all. 

In the coming year, we'll likely see more MLOps practices breaking the collaboration barriers between data scientists and other disciplines and increasing accessibility to machine learning."

Said Montiel

Wizeline Technology Director

With increased accessibility, teams are empowered to create ML models, deploy them for easy consumption, and monitor model health with much less engineering effort. Organizations that learn to leverage this can promote the next level of creativity in their data scientists. 

Ethical and Explainable AI

As AI continues to evolve rapidly and become part of more and more business processes, we see increased regulatory and customer pressures to ensure AI is ethical and explainable. Explainable AI describes an AI model, its expected impact, and potential biases, helping organizations build trust and confidence.

Advancements in explainable AI will help overcome the ‘black box’ problem of AI, making AI more transparent as model developers are held accountable for the decisions made by their models and the information used to arrive at such decisions.”

German Domínguez Montes

Wizeline Technology Director

Some benefits of explainable AI include minimized errors, reduced bias in AI models, and more informed decision-making. In the coming year, we anticipate increased use of explainable AI, helping to make AI more transparent, interpretable, and inclusive. 

Conversational Analytics

Powered by Natural Language Processing (NLP), conversational analytics uses AI to derive data from human conversations. The world already generates 2.5 quintillion bytes of data daily, and that number will only increase in this connected age. As AI technology advances, we expect to see more conversational analytics where text and voice will play a vital role in processing large datasets.

With improvements in NLP algorithms, computers will become more capable of understanding and resonating with human language, so the role of conversational analytics will increase."

German Domínguez Montes

Wizeline Technology Director

With applications across customer service, business decision-making, fraud detection, and more, AI-based analytics tools will track, identify, and analyze data to generate and deliver accurate insights quickly. 

Data as a Service (DaaS)

Data as a Service (DaaS) is a data management strategy that outsources most data storage, integration, and processing operations to the cloud, similar to how Software as a Service (SaaS) removes the need to install and manage software locally.

In addition to business benefits, DaaS will promote better data governance and intelligence."

Heriberto Perez Peñuelas

Wizeline Technology Director

DaaS enables organizations to start storing and processing data almost immediately. Since cloud infrastructure is less likely to fail, DaaS is less prone to downtime or disruptions, more scalable and flexible than on-premises alternatives, and promotes reduced data management and processing costs. According to Statista, the global analytics as a service market is estimated to reach approximately USD 59 billion. In the coming year, we anticipate increased DaaS adoption across major industries.

In Conclusion

As AI trends evolve, businesses must ensure the technology is not only enabling the success of their business but is also the foundation for competitive advantage. To stay ahead of the competition and consistently deliver best-in-class solutions, consider working with a partner like Wizeline. 

Wizeline helps companies mature data-driven capabilities by building next-generation data platforms and enabling machine learning and automation. The result is maximized business performance achieved through improved decision-making, reduced operational burden, and optimized resource utilization. 

Learn more about our Intelligence Everywhere practice, or email us at to get started.

Aisha Owolabi

Posted by Aisha Owolabi on November 29, 2022