AI is commonly defined as a computer's ability to replicate the cognitive functions of the human mind. It is one of the most rapidly evolving and revolutionizing industries, predicted to reach $60B by 2025. The number of AI-related startups has grown by 14 times since 2000, while investments have grown six times.
This is understandable when tech companies like Accenture say that business productivity increases by 40% after AI-integration. AI-powered machines like robots can complete mundane, everyday tasks, therefore allowing for greater productivity. This is why 85% of executives with companies over 100,000 employees are actively implementing AI for optimum employee performance and customer satisfaction.
That said, there are pitfalls associated with AI-implementation that could sink a company. Understanding what they are and how to avoid them is crucial for successful integration.
Challenges Faced While Adopting Artificial Intelligence
Several challenges mainly stem from ignorance or a lack of specialization in this field. These challenges are especially faced on an enterprise level. They include:
Technology is Still Building and Unproven
Many companies still equate AI with science fiction because, in several sectors, AI has not reached the level of affecting the common person. While governments can use it for surveillance or military tactics, the common person is far removed from the possible AI-dependent future. Therefore, while they may interact with it through Siri, Google, or Alexa, there is still time before it becomes an everyday phenomenon.
Difficulty in Quality Data Acquisition and Storage
Sensor data is the primary way that industrial AI systems gather information. However, the sheer volume of data sensors can collect present difficult-to-analyze datasets that hinder the company's smooth functioning rather than enabling efficiency. An incorrect AI implementation may also lead to a lack of data collection itself. This poses problems since artificial intelligence relies on data to function. Although the world grows more interconnected every day, the end-users are still contributing a minimal amount of data.
Lack of Adequate Budget
Small-scale companies often get the short end of the stick due to a lack of resources to invest in AI-powered technology adequately. While Google can afford to invest $3.9B in expert systems, smaller companies lack the financial means to build the infrastructure necessary for such artificial systems.
Lack of Understanding and Long Implementation Time
While AI-powered machinery and robots are attractive prospects to companies who can reduce manual labor for the greater precision and efficiency afforded by these systems, AI remains dependent on the data at its disposal. The idea of machine learning and reproducing cognitive functioning has limitations, including needing to teach the machine-specific functions. This lack of specialized talents can cause a problem.Additionally, the time it takes to see the result from AI-implementation differs depending on the company and the AI level. Not seeing immediate results is a popular reason companies do not invest in artificial systems.
Important Points to Remember
The challenges are many and quite common for those who have not adequately done their research or have the expertise. Therefore, when integrating AI into your production processes and business workings, there are essential points to keep in mind.
- Be realistic about the extent of your system’s functions. There are limitations to the technology, data, and engineering resources that prevent AI from achieving what we see in the movies. It will not solve everything and cannot completely replace human beings. YouTube and Facebook need human curators, and so do you. You also need a solid business core for your expert systems to deliver the results you desire.
- Instead of hiring 2-3 Machine Learning (ML) engineers to do all the work on the systems and come up with use cases, pair their talents with your business mind. Work cross-functionally for the best results from feasible projects that will add value to your company.
- The traditional planning process will have to change once the machine is involved. Consult your ML team about new deadlines and establish milestones, KPIs, timeline estimates, etc.
- Continue building your core team and business. Waiting for AI solutions will inevitably cause disappointment due to the previously-mentioned time difference between implementation and results. Therefore, you need a strong base of employees before your ML engineers join your team.
Keeping these pointers in mind will ensure that you go into AI-integration with an understanding of the expected outcome. Setting reasonable expectations is very important for satisfactory results.
How Ready-to-Use AI Platforms are Accelerating AI Adoption
IBM’s ready-to-use artificial intelligence, AutoAI, automates data analysis, among other key functions. AutoAI does the heavy lifting otherwise performed by data scientists who need to program the machine to collect the correct data and then sift through it for analysis. AutoAI is reflective of other ready-to-use systems in its ability to ensure:
- Faster Model Selection: When implementing your business model, AutoAI helps you determine the best candidates through an automated comparison method established through algorithms. It tests and ranks candidate algorithms against small subsets of the data. Then it gradually increases the subset until it finds the best algorithm match.
- Simple and Fast Onboarding: Using various algorithms, AutoAI analyzes, cleans, and prepares raw data for ML. This way, you can quickly start experimenting, evaluating, and deploying various business models. This is also available under Stradigi AI.
Stradigi AI, another ready-to-use system, offers business insights, including customer segmentation, demand forecasting, churn prediction, etc. that help entrepreneurs understand how to enhance their business strategy and boost their brand.
Artificial intelligence seems to be on its way to becoming a mainstay in the future. Integrating it successfully and effectively into your business is of utmost importance. Some of the pitfalls we have put forth are easily avoidable with some research and a good team. The ready-to-use systems are also noteworthy here since they can help you implement expert systems without worrying about understanding the intricacies. Once your business is financially stable enough, investing in AI is not a far-off prospect.