Utilize AI to improve your company

The adoption of artificial intelligence (AI) has arrived. Instead of debating whether to add AI capabilities, businesses are instead focusing on how they want to employ this rapidly developing field of technology. In fact, the application of AI in business is moving past niche, use-case-specific applications and toward a paradigm that positions AI at the strategic center of enterprise activities. Workers will have more time to perform distinctly human duties including collaborating on projects, coming up with novel ideas, and improving experiences thanks to deeper insights and the elimination of repetitive tasks.

There are difficulties associated with this advancement. Even while 42% of businesses claim to be investigating AI, only 54% of AI projects actually succeed in moving from the pilot stage to production. Changes in IT architecture, data management, and culture are just a few of the business models and procedures that must adapt in order to meet these difficulties. Here are some examples of how businesses are currently adapting and using AI to their advantage in real-world and moral contexts.

How businesses employ artificial intelligence

In order to obtain insights and create new business processes, artificial intelligence in business uses data from several departments as well as external sources. These models are intended to lessen repetitive work and difficult, time-consuming tasks as well as assist businesses in strategically changing how they conduct business for increased effectiveness, better decision-making, and better business outcomes.

The notion that artificial intelligence is only as good as the data foundation upon which it is built is one you’ll frequently hear in relation to AI. As a result, an effective data governance framework is also necessary for a well-built AI for business program. It guarantees that the data is being handled in a secure and moral manner and that the AI models are accurate, producing a higher-quality result.

The purpose of all this chatter about AI for businesses

In today’s business world, it’s difficult to avoid talking about artificial intelligence. Whatever the industry, company executives want to know how leveraging data may provide them a competitive advantage and help them with the day-to-day difficulties they encounter after COVID. This includes healthcare, retail, financial services, and manufacturing.

For good reason, generative AI capabilities have dominated a lot of the discussion. The media has focused on this innovative AI technology, but that only conveys a portion of the tale. Further exploring, the potential of AI systems is also pushing us to look outside of these tools and bigger: How will the use of AI and machine learning models advance broad, long-term corporate objectives?

Organizational changes in how businesses approach data analytics and cybersecurity threat detection are already being driven by artificial intelligence in business. Especially when combined with other technologies like chatbots or virtual agents, AI is being used in critical activities like hiring and retaining employees, providing excellent customer service, and modernizing applications.

Recent advances in AI are also assisting organizations in automating and optimizing biotech manufacturing, DevOps and cloud management, and HR recruiting and professional development. Businesses will start to transition from using AI to support current business processes to one where AI is driving new process automation, decreasing human error, and giving deeper insights as these organizational c take place. It’s a strategy referred to as AI+ or AI first.

The foundations of AI first

How does creating a process using AI as the initial step look? It is a three-step approach that, like all systemic change, allows firms to develop a clear business strategy and expand their AI capabilities in a smart, fully integrated manner. 

Setting up data storage with AI in mind

Modernizing your data in a hybrid multicloud environment is the first step toward AI first. To combine different capabilities and workflows in a team platform with AI capabilities, a highly elastic infrastructure is needed. This is available in a hybrid multicloud environment, offering your business options and flexibility.

Constructing and educating foundational models

Starting with clean data, foundational models can be built. Building a method to integrate, purify, and catalog the entire lifecycle of your AI data is part of this. By doing this, your business can grow in a transparent and trustworthy manner.

Constructing and educating foundational models

Effective data governance improves bias detection and decision-making for organizations by increasing trust and openness. Accessible, reliable, and accurate data also makes it possible for businesses to more effectively adopt AI across the entire enterprise.

How are foundation models altering the AI landscape? What are they?

AI foundation models can be utilized for a variety of applications with little to no fine-tuning because they were created using machine learning algorithms on a large amount of unlabeled data. The model can use transfer learning and self-supervised learning to transmit knowledge it has gained about one circumstance to another. For instance, ChatGPT is based on the OpenAI-developed GPT-3.5 and GPT-4 foundation models.

The adoption of AI can help organizations save endless hours spent developing their own models and reap the benefits of well-built foundation models. The benefits of saving time are what are luring many businesses to greater usage. According to IBM, a third of AI in corporate settings will be powered by foundation models in two years.

From a financial standpoint, foundation models demand a substantial upfront investment; but, because they are easily scaled to different purposes, they let businesses to save on the initial cost of model construction, offering a greater ROI and a quicker time to market for AI investments.

In order to achieve this, IBM is developing a collection of domain-specific foundation models that go beyond natural language learning models and are trained on a variety of business data types, such as code, time-series data, tabular data, geospatial data, semi-structured data, and mixed-modality data, such as text combined with images. Slate, the first of which, was just made public.

Data is the basis of AI

You need clean, high-quality datasets, as well as a suitable data architecture for storing and accessing it, in order to start an AI program for your company that is actually productive. Your firm’s digital transformation must be advanced enough to guarantee that data is gathered at the necessary touchpoints throughout the organization and that the data is accessible to whoever is performing the data analysis.

AI must create a successful hybrid multicloud approach to handle the enormous amounts of data that need to be processed, stored, and evaluated. Data fabric architectural approaches are frequently used in modern data architectures because they simplify data access and facilitate self-service data consumption. Adopting a data fabric design also produces a composable architecture that is ready for AI and gives uniform capabilities across hybrid cloud systems.

Governance and understanding the source of your data

Data governance is crucial to any organization’s AI strategy due to the value of accuracy and the ethical usage of data. To ensure uniform standards, this entails implementing governance tools and integrating governance into workflows. A data management platform also helps businesses to effectively document the data that was used to create or improve models, giving users knowledge of the data that shaped outputs and regulatory oversight teams the data they require to guarantee security and privacy.

Key things to keep in mind when developing an AI approach

Businesses will have a competitive edge over those that do not fully integrate AI into their processes if they are the first to adopt AI and use it ethically and efficiently to grow revenue and enhance operations. Here are some crucial factors to take into account when you construct an AI-first strategy:

How will AI add value to businesses?

The first step in integrating AI into your company is to determine how different AI platforms and types connect with important objectives. Companies should talk about the desired results as well as how AI will be used to accomplish these aims.

Data, for instance, presents possibilities for more tailored client experiences and, thus, a competitive edge. Employing tailored AI models based on client data, businesses can develop automated customer support workflows. Customer satisfaction may increase with more genuine chatbot conversations, product recommendations, personalized content, and other AI features. Teams can create new goods by using better insights into market and customer trends.

Focus on how AI might improve crucial workflows and systems, like as customer service, supply chain management, and cybersecurity, for a better customer experience—and operational efficiency.

How will you enable groups to utilize your data?

The idea of treating data like a product is one of the fundamental components of data democratization. On-premises data centers, mainframes, private clouds, public clouds, and edge infrastructure all house your company’s data. You must effectively use your data “product” in order to scale your AI efforts.

You may use data from several sources smoothly and scale your organization effectively with a hybrid cloud architecture. Decide which data is most important and which offers the biggest competitive advantage once you have a handle on all of your data and where it is located.

How will you make sure AI is reliable?

With the rapid advancement of AI technology, many people have started to wonder about issues like bias, privacy, and ethics. Companies must have well-structured data management and AI lifecycle mechanisms in place to guarantee that AI solutions are accurate, fair, transparent, and preserve customer privacy.

Consumer protection laws are constantly being expanded; The EU Commission proposed new GDPR enforcement guidelines and a data policy in July 2023, both of which would take effect in September. Companies run the risk of losing money, violating regulations, and having their reputations ruined without good governance and transparency.

Some uses of AI in the workplace

There are various ways deep learning, generative AI, natural language processing, and other AI technologies are being used to optimize corporate processes and customer experience, whether leveraging AI technology to power chatbots or writing code. Here are some instances of how artificial intelligence has been used in business:

Modernizing applications and coding

Businesses are utilizing AI to automate the coding, deploying, and scaling of business IT processes and application modernisation. Using Red Hat Ansible, for instance, Project Wisdom enables developers to enter a coding command as a simple English statement through a natural-language interface and receive automatically created code. The project is the consequence of IBM’s AI for Code initiative and the publication of IBM Project CodeNet, the largest dataset of its kind created with the intention of teaching AI how to program.

Consumer assistance

With the use of chatbots, virtual assistants, and other customer interfaces, AI is adept at scaling up the creation of tailored experiences. The largest restaurant chain in the world, McDonald’s, is using IBM Watson AI technology and natural language processing (NLP) to construct customer care solutions in order to develop its automated order taking (AOT) technology more quickly. This will not only assist in scaling the AOT technology across markets, but it will also assist in integrating new languages, dialects, and menu variances.

HR operations optimization

When IBM Consulting in North America used IBM watsonx Orchestrate as part of a trial program, the organization saved 12,000 hours in one quarter on manual promotion assessment duties, cutting the process from 10 weeks to 5. Important HR insights were also simpler to obtain because to the pilot. The IBM HR team now has a stronger understanding of each employee up for promotion and is better able to determine whether important benchmarks have been fulfilled thanks to its digital worker tool, HiRo.

Business’s use of AI in the future

Business AI has the ability to advance a variety of business functions and areas, particularly when an organization adopts an AI first strategy.

Businesses will probably scale AI initiatives more quickly over the next five years if they take inspiration from current developments in areas like digital labor, IT automation, security, sustainability, and application modernization.

Success with new AI technologies will ultimately depend on the quality of the data, the data management architecture, the foundation models that are being developed, and appropriate governance. Businesses can take full advantage of AI prospects by combining these factors with business-driven, practical aims.