In today’s data-driven world, businesses are constantly seeking ways to harness the power of information for strategic advantage. While Traditional Business Intelligence (BI) has long been the cornerstone of data analysis, the emergence of Decision Intelligence is challenging its limitations. As organizations grapple with increasingly complex data landscapes, the shortcomings of traditional BI tools are becoming more apparent.

Data quality and availability issues often plague traditional BI setups, where inconsistent and siloed data limit the effectiveness of decision intelligence models. The integration of cutting-edge tools with existing systems can lead to complex technical debts, stifling innovation and efficiency. Moreover, rigid semantic models designed for specific, short-term applications lack the flexibility needed for dynamic, self-service intelligence models. This inflexibility can result in interpretability and trust issues, as stakeholders struggle to rely on opaque or overly complex machine learning outputs without clear insight into their underlying operations. Organizational resistance to change further compounds these challenges, as cultural inertia often impedes the adoption of advanced analytics tools.

However, the emergence of AI-powered Business Intelligence is revolutionizing this landscape, transforming challenges into opportunities. By enhancing data integration, automating pattern recognition, and providing real-time insights, AI technologies are bridging the gap between data abundance and actionable intelligence.

Let’s take a look at how AI has helped businesses scale and enhance their decision-making capabilities.

Traditional BI systems have served businesses well for decades, but they’re increasingly showing their limitations in the face of modern data challenges. Let’s take a look at the key issues that are pushing organizations to seek more advanced solutions:

• Data Timeliness and Accessibility: Traditional BI solutions often operate with significant lag time, providing insights based on historical data that may be days or weeks old. This delay can severely impact decision-making in today’s fast-paced business environment where real-time insights are increasingly crucial for maintaining competitive advantage.
• Platform Investment and Implementation Costs: The expense of traditional BI platforms presents significant financial barriers, particularly when considering the total cost of ownership. Organizations face substantial upfront licensing fees, ongoing maintenance costs, and infrastructure expenses for self-hosted solutions. The situation becomes even more complex when organizations need to upgrade or migrate to newer versions to access modern features. 
• Interpretability and Trust Barriers: Traditional BI systems often struggle with transparency in their data processing and analysis methods. Users frequently encounter “black box” scenarios where they cannot easily validate the logic behind generated insights or verify data lineage. This opacity creates significant trust issues, particularly when critical business decisions hang in the balance
• Cultural Resistance and Change Management:  According to a study, 92.2% of companies report that the challenge of creating a data-driven organization is as much about people and processes as it is about technology. Cultural resistance often stems from employees’ comfort with existing processes, fear of job displacement, and reluctance to learn new tools. The transition from familiar spreadsheet-based analysis to more sophisticated BI platforms can be particularly challenging, requiring careful change management strategies and comprehensive training programs. 
• Technical Complexity and Resource Dependencies: The operation of traditional BI tools typically requires specialized technical expertise, creating bottlenecks in the analytics process. Organizations frequently find themselves dependent on IT departments or data specialists for even routine analysis tasks, leading to delayed insights and reduced operational efficiency.
• Integration and Scalability Constraints: As data volumes and sources continue to multiply, traditional BI systems struggle with seamless integration and scalability. Organizations face significant challenges in connecting disparate data sources and maintaining performance as data quantities grow, often requiring costly infrastructure upgrades or complex workarounds.
• Complexity and Technical Barriers: Many traditional BI tools require significant technical expertise to operate effectively. This complexity often leads to a bottleneck where IT departments become overwhelmed with requests, slowing down the entire decision-making process.
• Manual Processes: Despite advancements in automation, many BI tasks still require manual intervention. Data preparation, for instance, can consume up to 80% of an analyst’s time, according to Gartner.
• Struggles with Big Data: The explosion of big data has overwhelmed traditional BI systems. Traditional BI tools simply weren’t designed to handle this scale of information.

These challenges are not just inconveniences; they represent significant barriers to business agility and competitiveness.

Moreover, the siloed nature of traditional BI systems often leads to inconsistent views of data across different departments. This can result in conflicting reports and a lack of a single source of truth, further complicating decision-making processes. The rigidity of many legacy BI systems also means they struggle to adapt to new data sources or changing business requirements, leaving organizations unable to capitalize on new opportunities or respond to rising threats in a timely manner.

Another critical issue is the lack of predictive capabilities in traditional BI. While these systems excel at telling you what has happened, they fall short in forecasting what might happen next. Companies need the ability to anticipate trends, predict outcomes, and proactively shape their strategies.

Artificial Intelligence is not just enhancing business intelligence; it’s fundamentally reimagining what’s possible in data analysis and decision-making. The integration of AI into BI tools is addressing many of the pain points associated with traditional systems, while also opening up new frontiers of analytical capability. Here’s how AI is changing the BI landscape:

• Personalized Insights: Conversational agents can tailor insights to individual user needs and preferences, providing a more personalized and relevant experience. This personalization leads to better understanding and utilization of data.
• Innovation and New Applications: The evolving capabilities of conversational agents open up new possibilities for innovation and applications within the enterprise. Agents can be used for data discovery, detailed insights, predictive analytics, and even generating creative content based on data, such as dashboards or presentations. BI Agents are also extensible, meaning they can be integrated within existing applications easily, bringing the modern agentic experience to the tools consumers are familiar with.
• Improved Decision-Making: By providing quick and easy access to insights, conversational agents enable faster and more informed decision-making. Users can ask questions in natural language and receive immediate answers, leading to more agile responses to business challenges.
• Enhanced Data Accessibility and Democratization: Conversational agents make data accessible to a wider audience within the enterprise, increasing user adoption with less resistance regardless of their technical skills. This democratization of data empowers more users to make data-driven decisions.
• Supporting Structured Data Analysis in Multi-Agent Architectures:  The major value proposition of using an analytics platform to support agenticBI initiatives is that there is already integrated support for a semantic architecture, data security, governance, and access. In multi-agent architectures, appropriate workloads/requests can be routed to these BI Agents to handle structured data analysis with significantly more accuracy than custom capabilities in the market currently are capable of.
• Insight Summarization and Prescriptive Action Planning: Help organizations bridge the gap between insights and business outcome by generating action plans for business users dynamically based on data insights and real time business metrics performance. Additionally, leveraging AI agents to identify gaps and anomalies for further analysis.

The impact of these capabilities is profound. According to a report by PwC, AI could contribute up to $15.7 trillion to the global economy by 2030, with much of this value coming from increased productivity and personalization in business processes, including data analysis and decision-making.

AI is also enhancing data discovery capabilities. Traditional BI systems require users to know what questions to ask of their data. Agentic AI systems, on the other hand, can autonomously explore data sets, identifying patterns, anomalies, and correlations that humans might miss. This can lead to unexpected insights and new avenues for business optimization.

Furthermore, AI is bringing a new level of personalization to BI. By learning from user interactions and preferences, AI-powered BI tools can tailor dashboards, reports, and insights to individual users or roles within an organization. This ensures that each stakeholder receives the most relevant information in the most digestible format, improving decision-making across all levels of the company.

While the benefits of AI-powered BI are clear, implementing these advanced systems requires careful planning and execution. Here are some best practices and key considerations for organizations looking to utilise AI in their BI processes:

• Data Quality and Governance: The effectiveness of AI algorithms is heavily dependent on the quality of data they’re trained on. Establish robust data governance policies to ensure data accuracy, consistency, and completeness. According to Gartner, poor data quality costs organizations an average of $12.9 million annually.
• Skill Development: AI-powered BI requires a new set of skills. Invest in training programs to upskill existing staff and consider hiring data scientists and AI specialists. 
• Change Management: Implementing AI-powered BI often requires significant changes to existing processes and workflows. Develop a comprehensive change management strategy to ensure smooth adoption across the organization.
• Ethical Considerations: As AI becomes more integral to decision-making, it’s crucial to address ethical concerns such as bias in AI algorithms and data privacy. Implement frameworks for responsible AI use and ensure transparency in AI-driven insights.
• Scalability and Integration: Choose AI-BI solutions that can scale with your business needs and integrate seamlessly with existing systems and data sources.
• Start Small, Scale Fast: Begin with pilot projects in specific departments or for particular use cases. Use the learnings from these pilots to refine your approach before rolling out AI-powered BI across the entire organization.

Another important consideration is the explainability of AI-driven insights. While AI can uncover complex patterns and make sophisticated predictions, it’s crucial that decision-makers can understand and trust these insights. Look for AI-BI solutions that provide clear explanations of their reasoning and allow for human oversight and intervention when necessary.

Lastly, consider the long-term implications of AI-powered BI on your workforce. While AI can automate many tasks, it also creates new opportunities for employees to focus on higher-value activities. Develop strategies to help your workforce adapt to these changes and utilise AI as a tool to enhance their capabilities rather than replace them.

As AI continues to evolve, so too does its application in business intelligence. Here are some key trends and technologies that are shaping the future of AI-powered BI:

• Large Language Models (LLMs) and Conversational Analytics: LLMs are making BI more accessible by allowing users to interact with data using natural language queries. Gartner predicts that by 2025, 50% of analytics queries will be generated via search, natural language processing or voice, or will be automatically generated.
• Edge Computing: As IoT devices proliferate, edge computing will enable real-time data processing and analysis at the source, reducing latency and bandwidth usage. Gartner forecasts that by 2025, 75% of enterprise-generated data will be processed at the edge.
• Augmented Analytics: This combines AI and machine learning to automate data preparation, insight discovery, and insight sharing. 
• Explainable AI: As AI becomes more complex, there’s a growing need for “explainable AI” that can provide clear reasoning for its decisions and predictions. This is crucial for building trust and ensuring accountability in AI-driven decision-making.

These technologies are set to further enhance the capabilities of AI-powered BI, enabling even more sophisticated analysis and decision-making support. For instance, the combination of NLP and augmented analytics could lead to BI systems that can engage in natural language dialogues with users, proactively suggesting insights and answering follow-up questions in real-time.

The rise of explainable AI is particularly important as businesses increasingly rely on AI for critical decisions. 

However, with these advancements come new challenges. As AI-powered BI systems become more sophisticated, issues around data privacy, algorithmic bias, and the ethical use of AI will become increasingly important. Businesses will need to navigate these challenges carefully, balancing the desire for ever-more powerful analytics with the need for responsible and ethical use of technology.

For more insights into what to expect for 2025 with regards to AI Trends, check out the comprehensive e-book on AI Business Trends 2025 by 66degrees.

With human-centric innovation and a collaborative approach to digital transformation, 66degrees is helping organizations across industries to optimise the full potential of AI in their BI processes.

Our offerings demonstrate how businesses can enhance out-of-the-box BI capabilities with tailored AI solutions. From individual workshops to enterprise-level implementations, our roadmaps guide businesses in introducing AgenticBI – a game-changing approach that combines the power of AI agents with traditional BI tools.

66degrees excels in integrating BI agents into multi-agent architectures. By leveraging platform APIs, we enable structured data analysis that goes beyond traditional BI capabilities, allowing for more complex queries and deeper insights. We also offer comprehensive strategies for AI accountability, including model calibration and performance monitoring

The future of business intelligence lies in AI. By overcoming the limitations of traditional BI, organizations can unlock new levels of data-driven insights and achieve unprecedented business success. This can also allow them to experience faster insights, gain a competitive edge, and unlock the full potential of data to drive innovation and achieve unprecedented business agility. 

At 66degrees, we empower organizations to make the right choices when it comes to cloud modernization and setting the stage for AI integration. Our strategic Google Cloud consulting services help align your technology needs with your business objectives, ensuring a robust, future-proof AI infrastructure. By making use of our deep expertise in cloud, data and AI engineering, we guide you through every step of your transformation journey. Connect with us to learn how we can help.