The advent of artificial intelligence (AI) is here. Companies are no longer wondering whether to add AI capabilities, but rather how they will use this rapidly evolving technology. In fact, the use of artificial intelligence in enterprises goes beyond small specific applications and becomes a paradigm that places artificial intelligence at the strategic center of business operations. By providing deeper insights and eliminating repetitive tasks, employees free up time for human tasks like collaborating on projects, developing innovative solutions, and creating better experiences.
This progress is not without challenges.Although 42% of companies say they are exploring AI technology, the failure rate is high; On average, 54% of AI projects move from pilot to production. Addressing these challenges requires changes to many of the processes and models companies currently use: changes in IT architecture, data management and culture. Here are some examples of how companies today are making this shift and leveraging the benefits of AI in practical and ethical ways.
How companies use artificial intelligence in business
AI in the Enterprise uses data from across the organization and fromexternal sources to gain insights and develop new business processes through the development of AI models. These models are designed to reduce routine and complex, time-consuming tasks and help companies make strategic changes in the way they do business to increase efficiency, make better decisions and achieve better business results.
A common statement about artificial intelligence is that artificial intelligence is only as good as the database it models. Therefore, a well-built enterprise AI program must also have a good data management framework. This not only ensures the accuracy of the data and AI models for higher quality results, but also ensures that the data is used safely and ethically.
Why we’re all talking about artificial intelligence in business
It’s hard to avoid talking about artificial intelligence in the business world these days. Healthcare, retail, financial services, manufacturing – no matter the industry, business leaders want to know how leveraging data can give them a competitive advantage and help them overcome the challenges they face every day in the wake of the pandemic.
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Much of the conversation focused on the possibilities of generative artificial intelligence, and for good reason. Although this revolutionary AI technology has been in the media spotlight, it only tells part of the story. Upon closer inspection, the potential of AI systems challenges us to go beyond these tools and think bigger: How will the use of AI and machine learning models help achieve broader strategic business goals?
Artificial intelligence in business is already driving organizational changes in how companies approach data analysis and cybersecurity threat detection. Artificial intelligence is being implemented in key processes such as talent acquisition and retention, customer service and application modernization, especially when combined with other technologies such as virtual agents or chatbots.
Recent advances in artificial intelligence are also helping companies automate and optimize recruiting and professional development, DevOps and cloud management, and biotechnology research and production. As these organizational changes continue to evolve, companies will begin to move from using AI to improve existing business processes to an approach where AI enables new process automation, thereby reducing human error and providing more granular information. This is an approach known as AI First or AI+.
First, the
building blocks of artificial intelligence
What does it mean to build a process with an AI First approach? As withany system change, it is a step-by-step process – the AI Ladder – that allows companies to create a clear business strategy and develop AI capabilities in a thoughtful and fully integrated way in three clear steps.
AI specific data storage configuration
The first step towards AI is data modernization in a hybrid multi-cloud environment. AI capabilities require a very flexible infrastructure to connectdifferent functions and workflows on a team platform. This creates a hybrid, multi-cloud environment that provides choice and flexibility across the enterprise.
models of construction and training foundations
Building basic models starts with clean data. This includes creating a process for onboarding, cleaning, and cataloging the entire AI datalifecycle.This allows your business to evolve while maintaining trust and transparency.
Implement a management framework to ensure safe and ethical use
Good data management helps companies build trust and transparency and strengthens bias detection and decision making. When data is accessible, reliable and accurate, companies can better implement AI across the organization.
What are basic models and how do they change artificial intelligence?
Foundation models are AI models trained with machine learning algorithms on a large, unlabeled dataset and can be used for a variety of tasks with minimal modifications. The model can transfer information it has learned about one situation to another using self-supervised learning and transfer learning.For example, ChatGPT is based on the GPT-3.5 and GPT-4 base models created by OpenAI.
Well-designed foundation structures offer significant benefits; Using artificial intelligence can save companies countless hours building their models. These time-saving benefits are pushing many companies toward wider adoption. IBM predicts that entry-level models will support about a third of artificial intelligence in enterprise environments within two years.
For cost reasons, entry-level models require a significant initial investment; However, they allow companies to save on initial model building costs as they can be easily extended to other applications, ensuring higher ROI and faster time to market for AI investments.
To this end, IBM creates a number of key domain-specific models that go beyond natural language learning models and are trained on many types of business data, including code, time series data, tabular data, geospatial data, and semi-structured data. and mixed-mode data such as text combined with images. The first of these, Slate, was recently released.
Artificial intelligence starts with data
To run a truly effective AI program for your organization, you need clean, high-quality data sets and the right data architecture to store and accessthem. Your organization’s digital transformation must be sophisticated enough to ensure that data is collected at the necessary touchpoints across the organization and that the data is accessible to anyone performing data analysis.
In order for AI to handle the massive amounts of data that need to be stored, processed and analyzed, the creation of an effective hybrid multi-cloud model is required. Modern data architectures often use a data fabric architecture approach that simplifies data access and facilitates self-service data consumption. Adopting a data fabric architecture also creates an AI-ready architecture that provides consistent functionality across hybrid cloud environments.
Manage and understand where your data comes from
The importance of accuracy and ethical use of data makes data management an important part of every company’s AI strategy. This includes introducing management tools and integrating management into workflows to maintain consistent standards.A data management platform also allows companies to adequately document the data used to build or customize models. This gives users insight into the data used to model outcomes and provides oversight teams with the information they need to ensure security and confidentiality.
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Important questions to consider when developing an AI strategy
Companies that adopt AI early to use it effectively and ethically to generate revenue and improve operations will have a competitive advantage over companies that do not fully integrate AI into their processes. When creatingan AI First strategy, several key questions need to be considered:
What added value does artificial intelligence bring to companies?
The first step in integrating AI into your business is to determine how different platforms and types of AI fit with your key goals. Companies need to discuss not only how to use AI to achieve these goals, but also the desired outcomes.
For example,
data opens up opportunities for more personalized customer experiences and in turn provides a competitive advantage.Companies can create automated customer service workflows using custom AI models based on customer data. More authentic chatbot interactions, product recommendations, personalized content, and other AI features have the potential to give customers more of what they want. Additionally, a deeper understanding of market and consumer trends can help teams develop new products.
To provide better customer service and operational efficiency, focus on how AI can optimize critical workflows and systems such as customer service, supply chain management, and cybersecurity.
How do you enable teams to use your data?
One of the key elements of data democratization is the concept of data as a product. Your organization’s data is distributed across on-premises data centers, mainframes, private clouds, public clouds, and edge infrastructures. To successfully scale your AI efforts, you must leverage the “data product.”
‘s hybrid cloud architecture enables seamless use of data from multiple sources and efficient scalability across the enterprise. Once you have all your data and know where it is, decide which data is most important and provides the greatest competitive advantage.
How do you ensure artificial intelligence is reliable?
With the rapid acceleration of artificial intelligence technology, many people have begun to ask questions about ethics, privacy and bias. To ensure that AI solutions are accurate, fair, transparent, and protect customer privacy, companies must have well-organized data and AI lifecycle management systems.
consumer protection laws continue to evolve; In July 2023, the European Commission proposed new standards for enforcing the GDPR and a new data policy that will come into force in September. Without proper governance and transparency, companies risk reputational damage, economic loss and regulatory violations.
Examples of the use of artificial intelligence in the workplace
Whether you use AI technology to power chatbots or write code, there are countless ways to implement deep learning, generative AI, natural language processing, and other AI tools to optimize business operations and serve customers . Here are some examples of business applications of artificial intelligence:
Application Coding and Modernization
companies are using AI to modernize their business IT applications and operations and are using AI to automate coding, deployment and scaling. For example, Project Wisdom allows developers using Red Hat Ansible to enter a coding command as a plain English sentence through a natural language interface and receive automatically generated code. The project is the result of IBM’s AI for Code initiative and the release of the IBM CodeNet project, the largest dataset of its kind aimed at teaching artificial intelligence to code.
Customer service
AI effectively creates personalized experiences at scale through chatbots, digital assistants and other customer interfaces.McDonald’s, the world’s largest restaurant company, is developing customer service solutions using IBM Watson AI and Natural Language Processing (NLP) to accelerate the development of automated ordering technology (AOT). This will not only help spread AOT technology across all markets, but also facilitate integration, including additional languages, dialects and menu variations.
Optimization of HR activities
When IBM implemented IBM Watsonx Orchestrate as part of a pilot program for IBM Consulting in North America, the company saved 12,000 hours on manual promotion review tasks in one quarter, reducing a process that once took 10 weeks to five. The pilot also made it easier to obtain important human resources information. With the digital employee tool HiRo, IBM’s HR team now has a better overview of each employee up for promotion and can more quickly assess whether key standards have been met.
The future of artificial intelligence in business
AI in business has the potential to improve a variety of business processes and areas, especially when an organization uses AI.
Over the next five years, we will likely see companies expand their AI programs more quickly, focusing on areas where AI has made recent progress, such as the digital workplace, IT automation, security, sustainability and modernizing applications.
Ultimately, the success of new AI technologies will depend on data quality, data management architecture, new underlying models and good governance. With these practical, business-focused elements and goals, companies can take full advantage of AI.