Between OpenAI’s research release of its language model ChatGPT and Microsoft’s inclusion of an embedded (and still improving) artificial intelligence (AI) chatbot in its Bing search engine, it’s impossible to escape news about the rise of AI. Of course, these new AI tools are simply the most recent customer-facing applications after decades of development across various computing interfaces and industries. For enterprise businesses, AI has changed the landscape of daily activities and overall corporate strategy. These innovations don’t show signs of slowing. The global AI market size was valued at $93.5 billion in 2021 and is projected to expand at a compound annual growth rate of 38.1 percent from 2022 to 2030.
In addition to AI, machine learning (ML) and deep learning are vital processes to successfully run computer systems that can learn and adapt by using algorithms and statistical models to evaluate and take actions based on patterns in data without explicit human direction. Deep learning is a type of ML that takes this analysis and action a step further to approach more complex tasks specifically modeled after the human brain. To successfully adopt intelligent enterprise software enhanced by ML across an organization while maintaining data integrity takes a dedicated and committed approach.
What Is Intelligent Enterprise Software and Why Is It Important?
Digital transformation is no longer a groundbreaking concept. Today, it’s a natural evolution and vital step for businesses, especially enterprise-level businesses with vast networks of data, people, and operations. An intelligent enterprise is a business that utilizes advanced technologies, such as AI and automation, to improve decision-making, streamline operations, and enhance the customer experience. This can cover everything from internal needs such as spam filters, sales and business forecasting, facial recognition, and security technologies, to customer-facing needs including automated helpdesks, personalized recommendations, and assessing urgent customer needs.
According to recent studies, it’s estimated that 40% of enterprise intelligence initiatives will be business-specific and purpose-built for business, shortening the data to decisions time frame by 25% by the end of 2023. The adoption and successful integration of intelligence initiatives allow companies to gain a competitive advantage, improve efficiency, and make better use of data to drive growth and innovation.
Additionally, an intelligent enterprise facilitates a company’s ability to respond quickly to market changes, increase agility, and improve the overall customer experience. Intelligent enterprise systems are valuable across virtually every industry ranging from manufacturing, retail, finance, healthcare, human resources, transportation, energy and utilities, government, service, marketing and advertising, and more.
Opportunities for Enterprise Systems to Benefit from Machine and Deep Learning
With the abundance of AI-augmented technology available, it can be a challenge to know where to start. The first step is to focus on business priorities and then strategically narrow in on the systems and technologies that can make those high-level goals possible. To identify the right opportunities, consider the following benefits for enterprise businesses. Intelligent enterprise software can help organizations:
1. Personalize the customer experience and offerings. Customers are accustomed to tailored content and expect a degree of guidance from the companies they purchase from, even if they never speak to a customer service representative. Algorithms that personalize product and service suggestions to the customer’s needs lead to increased customer satisfaction and loyalty. Also, this degree of personalization can resolve customer issues proactively based on common challenges or customer prioritization triggers that identify urgent concerns.
2. Improve product quality and reduce waste. Intelligent software arms businesses with vital product data through consistent analysis and research and development. This helps minimize downtime and improve equipment performance, ultimately leading to streamlined production, better products, and happy customers.
3. Improve targeting and marketing strategies by anticipating and responding to changes in the market. Intelligent market analysis leverages data to analyze customers’ interests, rising demands, competitor activities, and the state of the supply chain. This analysis is then used to predict market changes and improve decision-making.
4. Automate repetitive or time-consuming tasks, such as data entry, analysis, and decision-making. AI and ML reduce the number of human interactions needed by speeding up repetitive or low-value-added tasks and processes. Robotic process automation (RPA) technology can handle manual tasks such as interactions with data from multiple sources, high-volume and repetitive tasks such as data migrations and approval workflows, and multiple system tasks where AI can access different applications across third-party solutions and other software. This can lead to increased productivity, better products, and increased customer satisfaction.
Uniting Technology and Human Processes
It isn’t enough to identify priorities and assume the implementation of intelligent enterprise software will simply plug and play. Rather, it demands a strategic plan that unites technology decisions with human processes to make the enterprise run smoothly, in tandem with business objectives. Keep these essentials in mind when adopting or optimizing intelligent enterprise software initiatives:
1. Set clear objectives based on overall business goals and proven use cases. There’s no question that intelligent enterprise software is vast. Without prioritizing technology based on business goals first, businesses can fall prey to mismatched tactics and strategies, leading to patchwork fixes, backtracking, and wasted resources.
2. Focus on data management and governance, including data quality, data privacy, data security, and data lineage.The first step toward an intelligent enterprise includes the adoption of an enterprise resource planning (ERP) software package such as SAP S/4HANA, to enable the organization to collect and process huge amounts of data, generate real-time data insights, and analyze this data to increase transparency and efficiency. Raw data, however, isn’t as useful as many assume. It requires organization, quality inspection, governance, and security tactics, all before making its way into ML processes or decision-making.
3. Build a skilled workforce. In the context of intelligent enterprise software, this means a workforce that embraces AI and technology and supports innovation and experimentation. AI and advanced technology adoption will only succeed with the support of the organization’s most fundamental engine: its people. Keep employees informed and involved in intelligence decision-making to ensure their skillsets and mindsets match technology goals. Employee training and development help teams work with and manage AI and other advanced technologies. This includes reskilling and retraining existing employees, as well as hiring new employees with the necessary proficiencies and expertise.
4. Take advantage of business process optimization. This includes identifying bottlenecks and inefficiencies in existing processes and redesigning them to improve efficiency and effectiveness. Take aerospace leader, Airbus, for instance. The company uses intelligent enterprise software to improve the efficiency of its supply chain operations. The software helps the company to optimize production processes, manage inventory, and predict equipment failures.
5. Engage in collaboration and partnerships. A successful enterprise is not an isolated entity. Rather, excellence hinges on collaborating and partnering with other organizations, such as technology vendors, research institutions, and academic institutions, to initiate progress and shared growth. This includes distributing knowledge and expertise, as well as accessing new technologies and capabilities in tandem with other experts across interconnected industries.
6. Practice ethical principles and transparency. In part, due to its rapid growth, the ethical use of AI is top of mind for developers, businesses, and individuals alike. Ensure AI systems are fair, trustworthy, and clearly communicated to maintain organizational transparency about its use of data and technology for the sake of customers, stakeholders, and employees.
7. Plan for future growth based on scalability. The technologies of intelligent enterprise software are constantly evolving as data and requirements change over time. This means costs and capabilities are always changing, too. To scale for future growth, an enterprise must be ready to invest further in the storage space and capital to address future needs.
AI in Enterprise Business Is Still People-Centric
The impact of AI enterprise software presents huge benefits for businesses, but reticence still exists, in part due to concerns for and from the workforce. While it is true AI can lead to job displacement, it also can free up employees to focus on more complex and higher-value tasks. This can lead to increased job satisfaction and productivity. The potential for job displacement can be mitigated by retraining and reskilling the workforce to perform more intricate and important jobs and to use technology to support decision-making, problem-solving, and creativity, all human tasks that cannot be replaced by machines.
The impact of AI enterprise software on the workforce will depend on how businesses choose to incorporate it. By implementing a plan that focuses on business goals in tandem with customer goals, it’s possible mitigate the potential for job displacement, and maximize the benefits of intelligent enterprise software.