The 2026 AI and Data Landscape: From Tools to Business Value

As we are entering 2026, let's have a look at the data market trends and why this year will be a crucial one for the business industry?

AI and Data Trends in 2026: From Experimentation to Enterprise Transformation

Firstly, AI is no longer in the experimental stage. Businesses are now actively investing in AI technologies to realise tangible value. Consequently, organisations are adopting a more pragmatic approach, integrating AI directly into their internal workflows to boost efficiency and deliver measurable results.

Additionally, organisations are demonstrating an increasing interest in investing in agentic AI, which facilitates the delegation of repetitive tasks to autonomous AI agents. This shift enables employees to focus on higher-value tasks. This shift is expected to transform operating models over time and accelerate the adoption of AI-driven processes across the enterprise.

The race to produce the best Large Language Model (LLM), which is at the core of generative AI technology, continues. Currently, Google and OpenAI are leading the competition. Google is now a serious contender to ChatGPT after releasing Gemini, a powerful multimodal model, while OpenAI is set to introduced the fifth version of ChatGPT in mid-2025.

Organisations have also become more sophisticated in their approach to using AI, particularly with regard to data sovereignty. They are increasingly aware that using prompting tools such as ChatGPT with sensitive or proprietary information could result in them losing control over their data. Consequently, they are implementing stricter governance and usage policies, and developing secure AI environments in partnership with AI companies.

As such, TotalEnergies has partnered with the French company Mistral AI to integrate its models into TotalEnergies' internal processes. This will enable TotalEnergies' employees to use advanced AI tools to improve efficiency and decision-making in their day-to-day operations.

Regulations have also evolved to introduce laws governing the use of AI across different territories, ensuring compliance and accountability and the responsible deployment of AI technologies. Notably, the European Union has released the AI Act, which regulates AI usage across its 27 member states.

Preparing for the AI Era: Data Governance as a Competitive Advantage

In 2026, data governance will continue to evolve to address the integration of AI across business operations. While maintaining a strong focus on data quality to produce high-performing AI models, organisations must also expand their governance frameworks to cover AI accountability, particularly the oversight and control of agentic AI systems.

This involves establishing transparent, responsible and compliant monitoring mechanisms, audit trails and human-in-the-loop controls for autonomous agents. Data governance is now increasingly recognised as a facilitator of AI innovation rather than a constraint. A well-established data governance will be a competitive advantage, enabling businesses to deploy AI with confidence, speed and long-term sustainability.

Additionally, companies are demonstrating a growing interest in the 'data mesh' concept as they seek to transfer accountability from a centralised IT bottleneck to the business domains specifically responsible for generating and understanding the data. At the same time, they are keen to adopt an agile approach to improving data as a product, which involves releasing new product versions consistently based on business needs.

In short, data governance is now a necessity for businesses looking to develop an effective data strategy in preparation for AI integration. It also enables the selection of the most suitable data architecture to meet management needs while ensuring compliance with regulations.

At last, the volume of data is expected to reach 180 zettabytes by 2025, which is 3x more than that generated in 2020. As the volume of data increases, the demand for data governance also increases. The market is projected to grow from USD 4.44 billion in 2024 to USD 18.07 billion by 2032.

Embracing AI in 2026: Workforce Transformation and Adoption Strategies

Ultimately, both employees and managers will be expected to embrace the use of data tools, particularly AI-powered solutions, as part of their daily work. This shift requires a strong focus on data literacy, AI awareness and responsible usage practices, as it goes beyond basic tool usage to ensure that AI-driven insights are properly understood and applied.

To support this transformation, organisations will need to invest in change management, training programmes and cultural initiatives that encourage trust in data and AI. Managers will play a pivotal role in promoting adoption, managing expectations, and integrating AI tools into decision-making processes. This will ensure that the adoption of technology translates into tangible business value, rather than remaining an isolated experiment.

In conclusion, 2026 will be a challenging year that will shape the future of data and AI across industries. Some speakers and leaders have predicted that AI will fail due to flawed premises. Particularly, Daniel Kokotajlo, a former OpenAI researcher, gained attention for his AI 2027 scenario predicting a rapid shift toward superintelligence with possible catastrophic consequences. While remaining conscious of the timeline, superintelligence refers to a hypothetical form of artificial intelligence that surpasses human intelligence in virtually all domains, not just specific tasks.