The goal of business intelligence and intelligent applications is to answer questions, inform reasoning, and support decision-making, with the goals of solving problems of all sorts and generating multifaceted value.
Intelligent applications can enhance various facets of enterprise capability by streamlining tasks and offering data-driven insights. These applications use machine learning and algorithms to analyze data, identify patterns, and provide suggestions, which allows businesses to make well-informed decisions and optimize their strategies.
Real-world Intelligent Application: Here are some examples of how AI tools can support business functions:
-Accounting AI can categorize transactions, detect suspicious charges, manage invoices, and forecast cash flow.
-Customer service AI can handle common inquiries and schedule appointments, freeing up staff to focus on complex issues.
-Marketing AI can automate the creation of ads, social media posts, and newsletters, as well as optimize posting schedules.
-CRM AI can track customer behavior and suggest targeted offers.
-Recruiting AI can write job postings and screen applications.
Intelligent application in governance enforcement: Intelligent applications in governance enforcement are affected by the rapid evolution of AI technology, which often outpaces the ability of regulatory frameworks to adapt. The complexity, diversity, and global nature of AI systems create difficulties in establishing and enforcing uniform standards and regulations.
AI can automate messages but cannot build genuine relationships with customers, manage staff, think creatively, or navigate gray areas that require judgment, empathy, or tact. AI tools are best suited for tasks that are repetitive or time-consuming, such as responding to customer inquiries, screening job applicants, or drafting marketing emails. The goal is to make challenging tasks easier, not to overhaul everything. AI is unlikely to replace employees but can help them work more efficiently and focus on tasks that require human attention.
Despite these challenges, regulating AI is critical to avoid potential misuse by enterprises and governments, which could lead to unwanted effects. However, overregulation may restrict innovation, so leaders and GRC professionals face the challenge of balancing technological advancement with public safety, ethical use, and accountability. The AI Act, for example, uses a risk-based approach to AI regulation:
AI systems that pose an "unacceptable risk" are prohibited. These include systems that manipulate users, discriminate against social groups, assign social scores, or create databases of individuals likely to commit crimes.
-"High-risk" AI systems, such as those used in critical infrastructure, biometrics, and employment, are subject to intense scrutiny.
-"Limited risk" AI systems, like generative AI and chatbots, have transparency requirements to protect consumers from manipulation, including disclosing when content is synthetically generated.
-"Minimal risk" systems are generally expected to follow nondiscrimination principles.
Companies that violate these principles may face significant problems. AI is built on rules-based systems, which may not be suitable for situations that require judgment, empathy, or tact.
The convergence of machine learning, artificial intelligence, analytics, business intelligence, statistics & decision science makes a direct impact on building an intelligent business. The goal of business intelligence and intelligent applications is to answer questions, inform reasoning, and support decision-making, with the goals of solving problems of all sorts and generating multifaceted value.
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