Put AI to Work | How to Apply Artificial Intelligence in Your Business?

Artificial Intelligence is beginning to transform the way we do business and live our lives as consumers. It promises increased profitability as well as a digital transformation. The goal of this article is to highlight the steps involved in successfully using AI in your business.

Artificial intelligence (AI) is frequently and justly described as revolutionary and game-changing. Without machine learning, Amazon, Facebook, Google, and Netflix would not be what they are today. AI has significantly affected large banks, pharmaceutical and automotive sector leaders, and practically all tech organisations. As per the 2019 RELX Emerging Tech Executive Report, people are becoming more aware of the benefits of AI and, as a result, are using it more.

Only 37% of respondents in Gartner’s 2019 CIO Agenda Survey said they had deployed AI technology or were planning to do so soon. Moreover, AI applications appear to have benefited only large corporations. Smaller companies are still in the “pilot” stage of AI development or see it as a distant goal.

There are a variety of reasons for the perceived slow adoption of AI in business processes. According to the RELX Report, for example:

  • Budget constraints are cited as the primary reason by 50% of companies who have not yet adopted AI.
  • The lack of technical expertise is cited by 36% of those polled.
  • Unproven return on investment (ROI) is mentioned by 30% of the respondents.
  • 16% require the appropriate C-suite or Board buy-in.

We could add misconceptions to this list as well. Let us take a look at these and other issues and how companies may address them.

The Barriers of Using AI for Business Needs

Budget Constraints

Let us be clear: unless a company is large enough and has a unique set of business needs and adequate competencies, and a limited budget, there is no need to invest in costly AI innovation. It is usually sufficient to be early or late followers. The AI technology market already has a lot to offer. Small and medium-sized businesses can access cutting-edge AI solutions that are open-source and free or pay for solutions built by Amazon and other prominent AI inventors on a use basis. As AI advances, the technology and knowledge required will become more widely available.

Successful AI applications will be replicated by more suppliers, who will provide AI solutions at low costs, making them more accessible.

If a company decides to develop and integrate its own AI solutions, it should honestly analyse the amount of research and development it can handle, both mentally and financially. A project manager, data engineers, and sophisticated networking infrastructure are all involved in developing and maintaining AI systems.

Only by processing massive volumes of data can AI systems become more ingenious. Infrastructure that is AI-compatible must be adaptable, scalable, and capable of processing large amounts of data. The good news is that cloud-based services drop the costs of purchasing new servers and processing capacity. Partnerships with universities and tech firms with the knowledge and infrastructure to construct credible AI models can help make AI cheaper.

If building a full-fledged data science team in-house appears to be too costly, the problem may be solved by outsourcing the AI project.

AI projects can consume a significant amount of a company’s resources, but digital transitions are always costly. At the very least, you may mitigate the risks by choosing the appropriate task for your AI pilot project.

Where Should Artificial Intelligence Be Applied?

The business media’s coverage of artificial intelligence sometimes generates the notion that AI is a one-size-fits-all solution that “everybody” uses. Business leaders must think independently about how AI may assist their firm and integrate it into their current business model. Vendors, consulting firms looking for business, and tech media focused on clicks and shares should not influence their judgments. They should also be aware of the complexities of AI implementation and keep expectations in check.

Leadership should first identify the most critical issues and assess whether AI is the best answer.

Discover what is feasible and what is feasible. A small business, for example, may have a great idea but be unable to implement it due to a lack of company data. It is also crucial to anticipate how the AI solution will affect customers and staff.

According to the RELX Report, AI is helping to improve and develop products in 57% of the organisations questioned. AI, according to 54% of respondents, aids in improving control and collaboration. According to a Gartner survey from 2018, AI was primarily utilised to improve customer experience or fight fraud.

It is better to begin with jobs that are neither customer-facing nor mission-critical. For example, in back-office processes such as data input, processing, and sorting, you might first accelerate and decrease human error. Begin small and work your way up.

Big firms have teams dedicated to defining AI-related business problems and testing theories. These organisations may also develop ways to manage the data pipeline, provide enterprise-wide training, and so on. Other companies frequently go elsewhere for data scientists that are well-versed in their industry. It may be preferable, however, to train in-house experts to recognise problems that AI can handle.

Find out who is utilising AI to solve problems similar to yours. Look at your competitors’ websites, social media accounts, press releases, news coverage, and blogs to see how they use AI. Reach out to firms your size that has implemented successful AI applications to learn what it takes to do so and what to expect. When speaking with vendors, inquire about which firms similar to yours have seen a clear return on investment from their AI solutions.

Cultural Barriers

A company’s management must believe in AI’s business potential and learn new techniques and make adjustments. AI-related decisions, on the other hand, necessitate dialogue and collaboration at all levels. Thus, the optimal environment for AI implementation is a company culture that encourages experimentation and more dynamic and scalable ecosystems.

The responsibility of implementing AI is often delegated to the IT department. That is a blunder. Every employee should be aware of and on the lookout for AI’s benefits in their workplace. Otherwise, businesses run the danger of underutilising them.

For example, if the marketing team is unaware of the benefits of machine intelligence, they may never approach the AI team. People who operate daily business processes must contribute and actively participate in deploying an AI solution for it to be successful. Data scientists should serve as a conduit between all departments in data-driven businesses.

Adopting AI into an organisation’s environment may necessitate a large amount of research and development. Therefore, before an AI solution function with the existing system and offer meaningful results, it needs a lot of initial training and data effort. Following that, subject matter experts will monitor the machine to appropriately interpret the change in the business environment.

Lack of Technical Expertise

According to a 2018 Gartner survey, the most common concern among CIOs who listed AI as the most challenging technology to integrate was that it requires new skills. It may be challenging to find data scientists and specialists with specific technical skills, but there is nearly always someone prepared to pay them more. Unfortunately, it is much more challenging to find employees who can develop insights from company data and have a solid understanding of company strategy and digital technology.

Companies may respond by training their own AI staff to meet this problem. Organisations that help their existing workforces obtain AI capabilities are 40% more likely to have generated value from AI than companies that don’t. 

Unfortunately, investment in such training does not appear to be keeping pace with the increase in AI usage. Only 62% of CEOs polled for the 2019 RELX Report claimed their organisation provides AI training. However, this is an increase from 46% in 2018. Of those whose employers do not already provide training, 53% said they intend to do so in the future. 93% of respondents feel that universities and other educational programmes should invest in the future AI workforce in the United States. 

More Extended Timeline

Following the initial planning, research, and development, you will need to devote time to tailoring and configuring it to your specific business and knowledge area. It can be much more challenging to put your machine learning application into action if it manipulates language.

The integration of the created AI system into your business operations and IT architecture will take some time. For example, transitioning from prototypes to production systems might take time. You will also need to restructure your business procedures to accommodate AI technology.

Entirely autonomous AI systems are pretty rare. Employees who work alongside them will often be assigned new responsibilities as a result. Retraining employees on the new process and system may take some time.

Even a fully autonomous AI system will almost certainly require some augmentation. Interaction between the system and the users and observers should take place throughout this time. The acquisition of new data sets and their incorporation into machine learning algorithms can take months.

AI Applications’ Uncertain Value in Business

According to MIT SMR-BCG AI Report from 2019,

65% of executives globally report that they have not yet seen value from their AI investments; 40% of organisations making “significant investments” in AI do not report business gains from AI. And only 50% of organisations across maturity groups that have invested in high-risk projects have seen the value to date.

Because most businesses are merely experimenting with AI applications, it is difficult to assess and anticipate ROI. Furthermore, AI and machine learning increase quality and efficiency, which may not be evident immediately but will be evident in the long term.

The goal and most important outcome of AI deployment should be to improve people’s lives. AI-powered voice user interfaces, for example, will provide personalised and emotional user experiences, and digital assistants may soon be able to recognise clients by face and voice across channels and partners.

After all, why do you need to use AI applications in business?

Deploying artificial intelligence for commercial purposes may be time-consuming and costly, especially when massive data and sophisticated technology are involved. The most typical roadblocks on this journey include a pricey yet restricted AI skill pool, ROI uncertainty, and insufficient data. Organisations that overcome these constraints, on the other hand, may use AI to improve procedures, raise employee satisfaction and productivity, and establish a competitive advantage.

Before investing in any AI applications, it is critical to establish whether they are the most effective way to reach your business goals and strategic objectives. The company’s AI strategy should be shaped by the C-suite, which should also encourage a culture of experimentation and related education and training.

Their motive should be the need for advancement. The impact of artificial intelligence on business will increase slowly for innovators and quickly for laggards. Existing AI technologies can alter marketing, customer service, information technology, human resources, decision-making, administration, finance, and cybersecurity.

If you anticipate danger from AI-driven competitors or new entrants, it is best to begin investigating AI’s capabilities right away. Although the full potential of artificial intelligence in business is unlikely to be reached for another three to five years, do not wait until it is too late. Early adopters will be operating at reduced costs and with a more remarkable performance when a late adopter is ready to employ an AI solution. Companies that are slow to implement AI may never catch up.