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  • Writer's pictureAgnė Jaraminaitė

Trends and Challenges in Business AI and Data Strategies

trends and challenges in business ai and data strategies

In the constantly evolving business environment, AI and data are at the forefront of technological advancement. Julija Surniene, our Data Platforms Service Lead, likens the significance of these developments to the transformative impact of the iPhone and the World Wide Web. As we look towards 2026, with an anticipated data generation rate of 7 petabytes per second globally, businesses are gearing up to invest approximately $200 billion in data and analytics technologies. This article, informed by Julija's expertise, aims to delve into the current trends and challenges in AI and data strategy.

The AI-First Shift in Business Strategy

As Julija points out, businesses are increasingly embracing an AI-first approach, integrating AI into their core strategies. This shift is not just about adopting new technologies; it's about rethinking the entire business model to leverage AI's potential fully. Organizations across various sectors are realizing that AI can offer more than just operational efficiencies – it can drive innovation and create new opportunities.

Overcoming Security Challenges and Data Democratization

According to Julija, as businesses integrate more AI and data-driven strategies, security concerns inevitably rise. Protecting sensitive information while making data accessible across the organization is a delicate balance. Data democratization, or the process of making data available to a wider range of people within the organization, is becoming increasingly important. It empowers employees at all levels to make data-driven decisions, fostering a culture of informed innovation.

The Talent and Technology Equation

The successful implementation of AI and data strategies hinges on two critical factors: talent and technology. Julija emphasizes the importance of building a skilled workforce in AI and data science through targeted training and hiring. Simultaneously, they are focusing on selecting and developing the right technological tools and platforms to support their AI and data initiatives.

Data Strategy: The Core of AI Success

Julija cites a study from MIT, stating that over 70% of CEOs expect AI to be a critical part of business functions by 2025. However, the journey from concept to implementation of AI is fraught with challenges, primarily centered around data strategy. Companies often mistake AI as a standalone solution, but its effectiveness is deeply rooted in the quality and strategy of the underlying data. Any successful AI implementation vitally requires a cohesive and well-thought-out data strategy.

Data Platforms: The Backbone of AI and Data Strategy

By 2026, it is projected that 82% of organizations will have integrated data platforms that support the entire data and AI workflow. A modern data platform must be democratic, scalable, and flexible, designed to grow with evolving technologies and needs. Julija explains that these platforms play a critical role in data acquisition, storage, preparation, and analysis, enabling users to make informed decisions.

Real-World Applications and Use Cases

Julija shares examples about businesses across industries are leveraging unified data platforms to gain a competitive edge:

  • Retail: Retailers are consolidating data into single platforms for deeper customer insights, enhancing personalization and improving conversion rates across channels.

  • Manufacturing: Manufacturers are integrating data from disparate systems, streamlining operations, and improving supply chain efficiencies.

  • Financial Services: Banks and insurance firms are using data solutions to offer personalized services and improve direct-to-customer experiences.

Best Practices for an AI-First Future

To navigate the AI and data landscape effectively, Julija Surniene advises businesses to:

  • Establish Clear Ownership and Build a Data Strategy: Assigning a chief data officer to develop and oversee a data strategy and governance metrics is becoming increasingly essential.

  • Invest in Talent and Partnerships: Building internal expertise and forming strategic partnerships is crucial for AI/ML implementation.

  • Foster Trust and User Adoption: Building trust in AI systems and promoting a culture of AI adoption across the organization is vital.

  • Focus on Business Problems, Not Just Technology: AI should be a tool to solve specific business challenges, not a solution in search of a problem.

  • Contextualize AI Use: Understanding the regulatory and practical constraints of data is key to effective AI implementation.

Stepping Into an AI-Enabled Future

As businesses step into an AI-enabled future, understanding and adapting to these trends and challenges is crucial. The journey towards AI and data maturity is a gradual but transformative process, requiring commitment, coordination, and a forward-thinking strategy. By focusing on these aspects, businesses can harness the power of AI and data to drive innovation, efficiency, and growth.

If you're considering implementing a data platform but have numerous questions, don't hesitate to contact us. Our Data Platforms Service Lead Julija Surniene and her team are ready to assist you in navigating these complex but exciting waters.


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