Artificial intelligence (AI) is a combination of software and hardware that enables machines to perform actions and tasks in a way that simulates human intelligence. It holds the potential to revolutionize business by enabling organizations to automate and improve decision-making, analyze vast amounts of data, and reduce costs. Companies want to take advantage of these capabilities, but there are significant obstacles to achieving them.
AI technology is complex, requiring specialized expertise and tools that most companies lack. Deploying it is difficult because of the need for integration with existing systems across an organization. For many organizations, particularly those in highly regulated industries, security considerations also pose a barrier to entry.
Companies can overcome these challenges by integrating existing systems and deploying AI across the entire organization.
Some Use Cases of Integrated AI
Integration makes it possible for companies of all sizes to achieve their goals with the latest AI techniques. It also makes it easier for organizations to meet regulatory requirements as they implement new technologies. In some cases, organizations can use AI to improve their compliance efforts.
Leading companies are using automated audit and monitoring tools to proactively identify risk, detect when policies have been violated, and flag when incidents require human review. The solution can also provide employers with a “continuous view” of compliance by identifying patterns such as repeated employee terminations that may indicate a problem.
Integrated AI can also give companies a competitive edge by enabling them to better manage risk and offset the costs of complying with regulations. In addition, integrating AI into business processes allows organizations to automate tedious tasks that sap employee productivity and generate valuable insights from vast amounts of data, leading to more informed decisions that drive growth.
AI solutions are typically built around an existing enterprise resource planning (ERP) system to meet requirements for data governance, reporting, compliance, security, and analytics. The benefits of integrating AI into an ERP include reduced workloads for employees; better decision-making based on insights derived from the integrated systems; improved end-user satisfaction; and the ability to leverage existing investments in business processes.
The Process of Integrating AI
The process of integrating AI into an ERP involves several steps. First, the company acquires AI software that is supported by a robust open architecture to ensure it can be deployed on multiple platforms across its entire operations.
Next, companies must integrate the system into their existing systems for security, analytics, and other applications. To achieve this, they need to select a partner that has deep expertise in both AI and their particular industry. Finally, companies should ensure they have the right talent—machine learning engineers or data scientists—to use the system effectively.
Only after these steps are complete does the company start reaping the benefits of integrated AI. This usually takes between one and two years but can be shorter depending on the size and scope of an organization’s AI project.
How AI Can Help Businesses Manage, Analyze, and Secure Data
Machine learning tools are being used for a variety of processes that involve managing company data. They can be applied to portfolio management, risk mitigation efforts, predictive maintenance, identity verification, and more.
In finance, organizations are using machine learning to identify patterns in purchasing behavior, flagging unusual activity such as unauthorized expenditures or transactions with vendors that don’t usually do business with an organization. For example, Yoti, a UK-based authentication company, has used machine learning to build models that can help banks determine whether a customer applying for a loan is trustworthy.
In the insurance sector, integrated AI has been used to create more accurate predictive models that can better forecast claims costs based on historical data and machine learning techniques. Machine learning also allows insurers to set premiums with a greater degree of precision by predicting an individual’s likelihood to make a claim in the future.
Other industries can use machine learning to help manage and analyze their data as well. The healthcare industry, for example, can better inform patient care and reduce medical errors by applying these techniques to electronic health records (EHRs).
The human resources sector can also benefit from AI. Employers are using machine learning to automate the recruitment process, identify high-potential talent, and predict employees’ future success based on their past behavior.
Businesses can also use machine learning to improve security. Machine learning techniques are the basis of many cybersecurity tools available today, including firewalls, intrusion detection systems (IDS), anti-virus software, spam filters, and phishing protection. They allow companies to get in front of threats by flagging suspicious events that could indicate a cyberattack.
Insider threats are another area where machine learning can help improve security, with companies using it to monitor emails, screen employees’ communications, and identify unusual activity within their corporate networks.
Companies can use these tools to better manage risk across the organization. Machine learning monitors the quality of products or services an organization provides. When anomalies are detected—such as poor customer reviews, higher rates of returns than usual, or fewer repeat purchases—the business can take steps to address any issues before they escalate into major problems.
Internal processes such as expense reports and invoice approvals are other areas where machine learning can be used to automate tasks that were previously done by humans. The benefits here are similar to those for finance—reducing the time employees spend on routine activities, freeing them up to focus on more complex tasks, and increasing accuracy.
Integrating AI into existing structures allows for the streamlining of existing data repositories, platforms, and tools. Organizations should make it possible to share real-time information across various departments or functions—from engineering to finance to customer service—because this will minimize the time between actionable insights and decisions that can be made by employees at every level of the organization.
Organizations lacking AI integration technology risk falling behind competitors that do. They will find themselves susceptible to losing vast amounts of data they gather to inform strategic decisions.
AI system integration allows companies and organizations to capitalize on their existing investments—such as ERP systems, cloud computing platforms, and other enterprise applications—to create a more robust platform to be used across the entire organization. Finally, companies should integrate AI into existing business processes because it offers the opportunity for employees to spend less time on tedious tasks, freeing them up to focus on more valuable activities. The pros far outweigh the cons.
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