AI Enabled Enterprise Decision Making: Reconstructing Business Intelligence with AI, Moving into a New Era of Real-Time Strategic Decision Making

In the wave of digital economy, data has long been one of the most critical assets of enterprises. However, for enterprise management — especially CEOs, COOs and business directors — traditional business intelligence (BI) tools are facing a ceiling. Although the market is flooded with all kinds of data reporting systems, “data-driven” is not the same as “intelligent decision-making”.

High-level decision-making often requires both speed and perspective, and the traditional BI system has three major problems:

① data time lag: data processing and report generation cycle is long, the information has long been out of date;

② High interpretation threshold: high-level charts and technical terms are not friendly to managers with non-data backgrounds;

③ Slow response: there is a long chain from data insights to action execution, resulting in missed opportunities.

Therefore, enterprises are in dire need of upgrading BI from a “reporting tool” to a “real-time strategy engine” to truly arm decision makers to cope with the rapidly changing market environment.

1.From Reporting to Engine: The Evolutionary Path of Business Intelligence

1.1 Reporting

In the 1990s and early 2000s, BI systems centered on reporting, mainly serving finance, human resources, supply chain and other functional departments. The system was usually maintained by the IT team, with a long data update cycle, and was only suitable for after-the-fact archiving and auditing purposes. At this stage, the target users of BI are mainly business analysts and financial personnel, and executives are mostly passive receivers.

1.2 Self-Service BI

In the 2010s, Tableau, Power BI, Qlik and other tools emerged to promote the trend of “data democratization”. Business departments can complete their own analysis by dragging and dropping charts and setting filters. Although this stage reduces the threshold of data use, but still puts forward high requirements for the user’s analytical ability, and the analysis process is still mainly human-driven, decision-making response speed is limited.

1.3 Augmented Analytics

In recent years, AI has begun to penetrate into the business intelligence system, and the system can automatically identify trends, generate insights and even make preliminary recommendations. For example, when sales are down, the system can point out whether it’s due to a drop in unit price or a low conversion rate. The key to this stage is the introduction of machine learning algorithms and natural language processing technology, starting to allow the system to have the ability to “explain” and “predict.

1.4 Real-time Strategic Engine

The new stage that is currently taking shape is the combination of real-time data stream processing technology and AI reasoning capabilities to build a “real-time decision support system” with executives as the core users. The system is no longer just a tool, but more like a Virtual VP (VP), capable of capturing changes, interpreting impacts and proactively recommending actions in real time.

Core features of this stage include:

①The system actively pushes information and no longer passively waits for queries;

② Decision-oriented, rather than simply displaying data;

③ Constructing views and models from an executive perspective, emphasizing strategic thinking;

④ Faster insight, stronger prediction ability, and shorter response path.

2.how AI empowers management’s real-time decision-making

The value of AI to management lies in its ability to extract key decision-making information in the “information explosion” and accelerate the pace from perception to action. Here are four key capabilities that AI provides to managers in the strategic decision-making process:

2.1 Predictive Analytics

Purpose: To help managers see trends and risks ahead of time rather than after the fact.

Ways to achieve this:

Collecting historical data (sales, customer behavior, supply chain, etc.);

Use machine learning algorithms to train predictive models;

Apply new data to the model in real time to output risk probabilities or predicted trends;

Feedback of predictions to management in visualization or natural language.

For example, an FMCG company can predict that a product category in a certain region is about to experience a peak in demand in the next two weeks, so that it can deploy its goods in advance.

2.2 Natural Language Q&A and Visual Interpretation

Purpose: Lower the threshold of use, so that executives with non-technical backgrounds can also quickly obtain data answers.

Ways to achieve this:

Construct a Q&A interface based on a large language model (e.g. GPT);

Access to enterprise database and BI system;

Managers can directly ask questions in natural language (e.g., “Which region has the lowest profit margin?”) (e.g. “Which region has the lowest profit margin?”);

The system responds with a combination of charts + text.

This is equivalent to the executive’s desktop placed at any time online “analysis consultant”.

2.3 Real-time data flow and event-driven decision making

Purpose: Rapidly identify anomalies, immediately trigger a response, and improve resilience.

Ways to achieve this:

Access real-time data sources (IoT devices, CRM systems, market data APIs, etc.);

Set rules and thresholds, or let the AI learn the “normal state” on its own;

Once the data crosses the threshold, the system immediately recognizes it as an “event”;

Pushing alerts and recommending countermeasures to the relevant person in charge.

For example, if the system monitors a delay in the arrival of raw materials in the supply chain, it automatically alerts the COO and suggests adjusting the production schedule or activating alternative suppliers.

2.4 Personalized Dashboards and Automated Insights

Purpose: Allow managers to see only “important + actionable” information every day, saving attention resources.

Ways to achieve this:

Customize data prioritization based on job role (e.g. CEO, Marketing Director);

Utilizes AI to filter and sort information to recommend the “5 trends you need to see most”;

High-priority topics can be set, such as “profit anomalies”, “market share declines”, etc.

This is not only a BI panel, but also a “data sentinel”.

It is worth noting that there are enterprise AI solutions that have taken substantial steps in this direction. For example, Joule, an enterprise AI assistant launched by SAP, has been integrated into its core business platform.SAP Joule is able to understand the business context, assist managers to ask questions in natural language, and call information between multiple data sources, generate insights, recommend actions, and significantly reduce the threshold and time cost of high-level access to insights. For SAP users, this “embedded intelligent analysis” is no longer a future scenario, but a realistic and usable strategic tool.

3.Practical application scenarios

Transforming AI business intelligence into a strategic engine is no longer a concept, but a reality that many leading companies are already implementing. The following are typical management application scenarios:

3.1 Operational efficiency optimization

A manufacturing enterprise analyzes production data flow through AI to automatically identify bottlenecks and logistics delays in the production line, and the COO can adjust the scheduling plan based on the system recommendations directly to shorten the delivery cycle by 30%.

3.2 Sales and Marketing Strategy Adjustment

Retail enterprises use AI to predict the sales trend of categories in different regions, and business directors adjust promotional resources and channel ratios in real time to optimize ROI.

3.3 Customer insight and service optimization

SaaS enterprises identify high-risk customers through customer behavioral modeling, and customer success teams intervene in advance to significantly reduce churn; at the same time, it is used to explore the portraits of high-value customers and promote renewal and upgrade.

3.4 Strategic Planning Support

The enterprise group combines macroeconomic, industry data and internal operation data, and simulates various strategic scenarios (such as exchange rate fluctuations and raw material price increases) with the help of AI to support the CEO to quickly formulate response strategies.

4.Landing points: how to make AI BI really “use up”

The real AI BI landing, not by deploying a new system alone, but need to be led by the management of the systematic promotion:

4.1 Organizational level:

Establish a data-driven culture: managers personally speak with data, driving business teams to value data;

High-level participation in BI design: the design of AI dashboards should be directly based on management needs, and should not be independently led by IT.

4.2 Technical level:

Open up data silos: Integrate key data sources such as CRM, ERP, finance, and human resources to realize a unified view;

Real-time processing capabilities: deploy platforms that support data stream processing (e.g., Kafka + AI model fusion);

Integrated business and intelligence platform: choosing an AI BI platform that can work seamlessly with an organization’s existing systems is key. For example, SAP Joule serves as an intelligent hub for SAP Business Suite, helping executives gain real-time insights in a familiar work environment;

4.3 Talent and process:

Enhance data literacy: train management in AI and data thinking to develop the ability to “think with AI”;

Reshape the decision-making process: integrate AI recommendations and human judgment into regular meetings, quarterly reviews, budget allocations, and other processes.

4.4 Risk and Governance:

AI transparency: managers need to understand the principles and limitations of AI models and avoid “black box beliefs”;

Data security and compliance: establish clear boundaries for data usage to prevent AI from misusing sensitive information.

4.5 Suggestions for accelerating landing: Collaborate with professional implementers

In the process of technology landing and scene adaptation, working with experienced SAP implementers can greatly improve the success rate. They not only understand system integration, but also can help enterprises build exclusive AI decision panels for executives according to local conditions, realizing the two-way landing of “scenario-driven + technology adaptation”. This is a mature and risk-controlled path for enterprises that want to quickly realize their AI BI strategy.

5.AI era managers, decision-making power is competitiveness

The essence of business competition is rapidly shifting from “resource competition” to “cognitive competition” and “decision-making speed competition”. In this era, who can use less time, better data, more accurate judgment to make key decisions, who will have the first opportunity in the market.

The end point of AI Business Intelligence is no longer to read and understand reports, but to build a real-time strategy system that can evolve itself, actively identify problems and suggest paths. It amplifies a manager’s strategic vision and improves the overall responsiveness of the organization.

As one CIO put it, “AI BI is not replacing management, but rather equipping each executive with a ‘super think tank’ so they are no longer on their own.” The future is here, it is time to evolve BI from a “reporting tool” to a “real-time strategy engine”, and use AI to truly arm the brain of managers, so that decision-making runs before the market.

SAP’s flagship product, SAP Business Suite, takes the BTP business technology platform as its base, the business data cloud as its core, and cloud ERP as its support, covering five major business applications, and deeply integrating AI functionality, data, and core business applications into a single entity. the AI intelligence body becomes a gas pedal of end-to-end business processes, creating value for customers.

If you are considering the introduction of SAP, AI intelligent scenarios, professional SAP partner – Acloudear can be based on your specific needs, to provide customized services for your enterprise’s digital transformation escort.

This article "AI Enabled Enterprise Decision Making: Reconstructing Business Intelligence with AI, Moving into a New Era of Real-Time Strategic Decision Making" by AcloudEAR. We focus on business applications such as cloud ERP.

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