Artificial intelligence technology sees one of the most rapid and stable implementations all around the world. It is used by many entities, from governments and large organizations to small online businesses. AI facilitates daily processes, transforms the corporate environment, and finds its way into specific projects.
In this article, you will learn about some leading challenges that companies may encounter while implementing AI.
Some Crucial Statistics on AI Implementation Challenges
According to a recent poll, companies are ramping up the adoption of AI initiatives with a rate of 77.8%, up from 65.8% last year. Only 4.1% of respondents report not using AI applications.
As the overall investment in data and AI continues to increase, the technology’s capabilities will continue to be rolled out steadily. However, there is still a lion’s share of doubts that impact the company leaders’ decision-making in implementing AI in business.
Companies may face technological challenges such as shifting from pilot projects to production or merging AI with other technologies. But the PwC report demonstrates that these aren’t major issues.
The respondents stress that business and people-centered challenges are the main bottlenecks with AI implementation in daily life. They include, for example:
lack of confidence in ROI,
slow budget approval,
insufficient expertise of existing employees in the use of AI.
Now let’s dig a bit deeper and take a closer look into the 4 major challenges that companies face as they take a step towards AI implementation.
1. No Clear Use Case Defined
Many companies are using AI with high expectations and don’t understand the ultimate goal of introducing such complex technologies. Instead of using AI just for the sake of it, businesses need to define the problem that they would like to solve with its help.
For instance, AI chatbots may automate customer service work as you can see on the screenshot below. Clients of the CleverReach® company can address their problems with the help of this technology regardless if it is day or night.
Chatbots can answer the most frequently asked questions, book a table at a restaurant or make an appointment at the doctors’ without having to wait until the human operator is free. Since such customer requests are usually similar to each other, they can be automated with the help of auto-learning algorithms.
At the same time, it is important to realize that there are tasks for which the use of AI is not suitable. Perhaps some of them can be better solved by traditional computing combined with good analytics. Only after the clear determination of the issues, you can launch projects for AI implementation.
A McKinsey AI adoption survey shows the responses of professionals from companies in various industries around the world. 57% of high-performing companies noted that they lacked a clear strategy in implementing AI, and only 17% of all other respondents said that their companies had a defined AI vision.
2. Not Enough Data
Successful artificial intelligence implementation and acceptable results require plenty of data. So, at the initial stage of launching a project, the lack of accurate data can prevent companies from getting started.
What kind of data is needed depends on each business individually. Let’s take Puma’s online store as an example. The company employs AI technology to analyze customer behavior and the pages that they view. Then based on the findings, it makes personalized offers accordingly.
To collect data, companies may use services that have open data models that ensure seamless work across different platforms. The necessary data is always scattered across different platforms and requires a lot of effort to collect. In this case, AI comes to the rescue.
Machine learning mechanisms analyze large datasets and uncover patterns in them. In this way, AI improves through machine learning using patterns to predict. The more data, the better the predictions become over time. And AI, in its turn, learns on its own without human intervention.
However, the use of huge amounts of personal data from different sources can raise fears among customers. Transparency and trust play a key role in building loyalty, so it is crucial to tell customers that you employ AI in your processes. Building rapport helps to eliminate suspicions about the misuse of clients’ personal information by third-party sources.
3. Lack of Expertise
A successful large-scale digital transformation within an organization requires a combination of three skill sets:
a team with knowledge of artificial intelligence;
businessmen with marketing experience;
technical analytic engineers who can control a huge amount of data.
To deploy intelligent systems on a large scale, most companies need to find technical talent, develop skills, and acquire tools. Assembling the perfect team is not an easy task. Especially if you have an already established system of roles for existing employees in the organization and need to integrate AI projects into it.
A Gartner study proves that point and unveils that of those companies that decide to start AI projects, only 53% proceed from prototyping to production.
Initially, you may need to define new roles, recruit employees, or educate the existing ones. Besides, finding artificial intelligence experts is complicated due to a complex current shortage of skilled labor, so it may take time.
Although forward-thinking CIOs and IT leaders use third-party vendors, it is necessary to create conditions for the transfer of knowledge to further develop resources and expand internal capabilities.
4. Anti-Innovation Culture
New technologies require significant changes in the corporate workflow. And broad implementation of AI solutions might inflict fears in the minds of specialists.
AI possesses threats to some jobs indeed but doesn’t mean complete redundancy of people. It is empathy, cognitive thinking, and emotional intelligence that make people superior to robots.
AI aims at reducing effort spent on repetitive tasks thus complementing human beings, not eliminating them. It’s crucial to understand that and promote this idea among the staff.
The abovementioned AI challenges clearly show why promising ambitions can remain on paper and not receive a further stage of development. Therefore, senior leaders must consistently manifest commitment to AI and implement it in their companies.
Companies that want to adopt AI must have a clear understanding of how AI will solve a specific business problem. They need to consider the availability of resources and the culture of interaction in the company.
There is also a need to maintain transparency and ethics in reporting the use of personal data and invest in improving and adapting systems. This will help to avoid unnecessary business risks that conflict with regulatory requirements or violate general codes of ethics.
About the Author
Kate Parish, Chief Marketing Officer at Onilab with 8+ years of experience in Digital Marketing and website promotion. Kate always strives to stay in pace with the ever-advancing online world, and the sphere of Magento 2 PWA development. Her expertise includes in-depth knowledge of SEO, branding, PPC, SMM, and the field of online sales in general.