We have arrived at the inflection point that was once only theorized about — artificial intelligence (AI) has become ubiquitous. As we dive deeper into the intersection of AI and business operations, it’s crucial to acknowledge the pivotal role that data plays in the success of AI. Data is the lifeblood that powers the engine of AI. Without sufficient and high-quality data, AI cannot function.
5 Ways to Set Your Business Intelligence (BI) Up for AI Success
- Migrate your BI operations to the cloud.
- Adopt a modular approach to your BI strategy.
- Solve immediate problems with data.
- Align BI projects with AI goals.
- Build AI with a feedback loop to business stakeholders.
Considering its importance, companies should prioritize data management to improve their AI initiatives. However, adopting free and paid AI tools is not enough on its own, especially as the industry continues to evolve and take shape. Companies must also ensure that they can use AI effectively to get ahead of the competition. This requires a thoughtful approach to integration and implementation, which is where business intelligence (BI) comes in as a key enabler.
How Business Intelligence Can Boost Your AI Success
Business intelligence (BI) is the function of a company responsible for developing insights that contribute directly to solving business problems. For some companies, it’s a separate business unit, and for others, it’s integrated into each function. Whether they’re in charge of engineering the data, creating the right infrastructure for the data or just dashboarding to drive business outcomes, BI is the closest link that a company’s data can have to their bottom line.
Therefore, it’s crucial that any kind of data management strategy involves outcomes that contribute to BI in the immediate term even if the long-term view is to create AI. By focusing on enhancing their BI capabilities now, the company can better understand their data and its potential value before investing in more complex and resource-intensive AI projects.
This can also help ensure that any AI projects are built on a solid foundation of accurate and relevant data, which is essential for success. In addition, the company will develop an advanced understanding of the underlying data used to power the AI models and can fine-tune it for their specific requirements.
5 Tips to Set Your Business Intelligence Up for AI Success
As AI companies continue to make significant advancements in developing cutting-edge algorithms such as Bard, GPT-4, AlphaGo, and DALL-E 2, it’s becoming increasingly important for non-AI businesses to embrace and incorporate these emerging technologies in order to remain competitive and achieve sustained growth. When everyone uses AI, what will differentiate a business is its ability to experiment and innovate with its own data collected from business intelligence operations.
Here are some steps you can take to improve your BI strategy to set your company up for AI success:
1. Migrate Your BI Operations to the Cloud
Migrating BI operations to a cloud-based platform is vital for companies to establish a single source of truth from which teams can work together and create streamlined workflows. Unlike hardware storage and on-premise data systems, the cloud is instantly accessible anytime, anywhere, and can provide real-time data insights.
With data easily accessible to multiple parties, using a cloud-based platform prevents fragmented digital ecosystems from slowing business productivity and elevates business intelligence. Having real-time data also allows businesses to construct action plans with a higher rate of success and shape their decision-making processes and AI strategies with analytics.
It also makes business sense. According to McKinsey, such cloud-based advantages can add a potential $1 trillion to Fortune 500 companies by 2030, and the report acknowledges that early adopters would see the most value added.
2. Adopt a Modular Approach to Your BI Strategy
AI is frequently integrated into various business functions without a long-term strategic vision, resulting in a myopic and shortsighted approach. Nevertheless, adopting a modular approach to building systems and prioritizing BI before AI can prevent this. Incremental building of data pipelines enables the creation of data sets in the cloud that will eventually enhance AI capabilities, while offering immediate value in other areas.
This approach empowers companies to differentiate themselves based on their cloud infrastructure and data, rather than relying exclusively on the latest AI tools. Considering the flexibility of the cloud, companies can always experiment and innovate with the latest and greatest without having to overhaul their entire system architecture.
Start by creating a data strategy that allows you to solve immediate problems in a data-driven way, and leverage these valuable data points to construct powerful AI solutions that deliver real business value. Remember, with a solid data strategy in place, the possibilities for innovation and growth are endless.
3. Solve Immediate Problems With Data
The various departments on the business side of the fence are the ones generally closest to the most critical problems that need to be solved with the highest ROI. Generally these solutions fall under the umbrella of business intelligence rather than AI and therefore would follow relevant frameworks.
To begin with, it is essential to identify a particular problem, stakeholders, success indicators (both leading and lagging), and a clear definition of done. Based on these, the business intelligence teams can gather the necessary data points, clean and transform the data, develop dashboards, implement feedback from users and eventually automate the underlying pipelines.
4. Align BI Projects With AI Goals
Data teams have a unique opportunity to add value by staying strategic even as they execute tactically on business intelligence (BI) projects. In this way, they can prioritize their data engineering activities based on both the immediate requirements of the ongoing BI projects and the anticipated needs of their long-term AI capabilities on their roadmap.
They can do so by building scalable and flexible data pipelines and developing machine learning-friendly data modeling and analysis techniques. This could also involve building data pipelines that are scalable, flexible and adaptable to new data sources. By aligning data engineering activities with the broader organizational goals and vision for AI capabilities, the data teams can set BI projects up for success in the short term while paving the way for future AI projects. This helps ensure the organization remains competitive and innovative.
5. Build AI With a Feedback Loop to Business Stakeholders
By maintaining a dual focus on BI and AI, companies gain a technical advantage and enable their business counterparts to gradually get more accustomed to the data products that are necessary for their daily activities. This approach can help build a culture of data-driven decision-making within the organization, which is essential for success in today’s business environment.
Building explainable AI requires a strong feedback loop between the data teams and the business teams. Since they have been working together from the outset of BI initiatives, both teams can better understand each other’s needs and constraints, leading to more effective communication and collaboration. This can help ensure that the AI models developed are aligned with the business objectives and are easily explainable to all stakeholders.
Why Business Intelligence Is Integral to AI Success
Finally, it is important to note that the most successful AI implementations are those that are closely tied to business problems. While technology will continue to advance at a rapid pace, the business problems that AI is used to solve will evolve independently of that.
Executing on BI projects without losing sight of the long-term AI vision can help organizations build AI agents that are much closer to the problem and more adaptable to business changes. This approach will lead to AI implementations that deliver real business value.