Artificial intelligence offers endless benefits to a business but can be overwhelming to implement without proper guidance and support. Here, we’ll discuss the common errors enterprises make in AI implementation along with the ways to avoid them.
Artificial intelligence has been a part of the global market for years. While it has several definitions, AI is mainly the science and engineering of making machines intelligent. It combines computer science and datasets to enable quicker problem-solving.
Many SMBs, MSMEs, and large enterprises are adopting AI technology and tools for various purposes. A research report shows 44% of companies in the private sector will adopt AI in 2023.
According to Statista, AI is mainly used for managing data as a business asset, establishing data culture, driving innovation, performing data analytics, and building a data-driven enterprise. However, several business organizations have suffered from artificial intelligence failures. In fact, various studies show that 70% of companies have minimal or no impact of AI, and a whopping 87% of data science projects don’t make it to production.
In a way, AI mishaps are one of the reasons for organizations to be wary of adopting advanced technology for digital transformation. For example, the automated hiring algorithm used by Amazon turned out to be biased against women and hired only white men (due to biased training data). A real estate tycoon in Hong Kong filed a lawsuit against a business that sold him an AI robot to manage his funds. The robot lost around $20 million every day instead of increasing the funds by making the right investment decisions.
It’s crucial to understand where things go wrong and avoid such mistakes to successfully implement AI technology in a business.
In this blog, we’ll read about the common AI mistakes every business should avoid and ways to achieve the desired results.
Artificial intelligence can offer a multitude of benefits to a business. However, it requires heavy investment in building a strong IT infrastructure and training employees to work with the latest tools. Making half-hearted attempts or investing in AI for the sake of it is a sure way to generate losses.
AI adoption requires planning, implementation, and continuous tracking to deliver results. Haphazard processes will only waste business resources and lead to additional complications. To prevent such issues, businesses should take the time to develop a strategy for AI adoption and follow it.
Is your data large enough to make AI effective? This is one question many people ask since AI is associated with large datasets. However, businesses forget to consider data quality and its impact on artificial intelligence. The AI model is as good as the data used to train it. If businesses don’t provide high-quality data input, the model will not deliver accurate results.
Lack of data quality is another major reason for AI failure in enterprises. This can be avoided by investing in proper data systems and ETL (extract, transform, load) models to collect, clean, format, and process the data before it is fed into AI models for training. Data management is necessary to avoid skewed or incorrect models.
Why should a business invest in AI? What problems does the top management wish to solve using artificial intelligence? Which business goals can be achieved through AI adoption? Every enterprise should have clear answers to such questions before investing in AI. Simply copying a competitor is likely to result in failure.
Establish definite and measurable business goals. Align these goals with the business vision and mission. Then create an AI adoption strategy that supports the business goals and objectives. Evaluate the impact the AI model would have on the establishment and calculate the expected ROI. The trick to avoiding AI failure is to spend more time strengthening the foundation instead of building AI on weak bases.
AI adoption requires many changes across the organization. The internal processes, IT systems, employee working methodologies, and organizational culture have to be changed and revamped to align with how AI systems work. Quite a few businesses don’t pay enough attention to these changes. They don’t have a comprehensive plan to implement the changes cohesively at each level. This results in disruptions, miscommunication, delays, and unexpected losses.
Companies offering AI consulting services insist on developing a change management strategy to ensure employees, management, systems, and processes are in sync and work towards the same goals. It’s vital to address the cultural and behavioral aspects of AI adoption to overcome roadblocks and ensure a smoother transition from outdated methods to the latest ones.
AI models are complex and hard to understand. There’s no denying this statement. However, when a business relies on such black box models (ones that offer almost no explanation of how the algorithms work), it creates opacity in the enterprise. Lack of transparency and accountability can lead to many issues for the business. Who will be responsible for the wrong insights?
Avoid using black-box AI models in the organization. Work towards building transparent models and maintaining clear documentation to understand how things are done. Businesses that cannot afford to build AI models from scratch can partner with AI companies to customize existing tools and increase transparency. Put in extra effort to explain the process to employees and establish procedures that make people accountable.
Introducing artificial intelligence in a business is no small task. AI models are best handled by experts with the necessary domain knowledge. Asking the existing talent pool to use AI technology without proper training is likely to cause errors and additional problems. When planning the digital transformation of a business, it’s important to identify the talent gap and find ways to fill it.
Organizations can hire an in-house team of AI experts to initiate the process or rely on offshore service providers. Building a team from scratch is cost-intensive and time-consuming. However, working with AI companies is quicker, cheaper, and more effective. That’s because companies are used to helping businesses create an AI strategy and implement it successfully. They work with enterprises from different industries and have the necessary expertise and experience to solve any kind of problem that arises during AI adoption.
Implementing AI models in an enterprise is not a single team’s job. It requires collaboration and continuous communication between different departments and stakeholders. For example, the IT teams have to work with data scientists, business strategists, C-level executives, legal heads, etc., to ensure that everything is streamlined within the business.
Not involving the relevant stakeholders during AI adoption will lead to siloed decision-making and result in many missed opportunities. There could also be compliance issues, data breaches, and lawsuits. Bring all stakeholders on board right during the early stages of AI adoption. Don’t wait until AI mistakes affect the business. Get the teams to collaborate and work together for a common goal.
Artificial intelligence is not meant for the short term. Technology will continue to play an active role in the future. Hence, businesses should focus on how to manage, maintain, and upgrade AI systems over the years. AI will continue to evolve, and enterprises should be prepared to adapt as necessary.
Planning for the long term implies developing AI systems with flexibility, scalability, and agility. Create a roadmap for the future and ensure there will be enough funds and resources to upgrade the systems to stay relevant in the competitive markets. AI is never a one-time investment. It requires regular monitoring and upgrades to deliver the expected outcomes.
One of the biggest risks of artificial intelligence is the misuse of data and AI, leading to ethical violations. Data privacy, bias, transparency, accountability, etc., are some issues a business needs to focus on when adopting AI models to make data-driven decisions. Using customer data with consent or collecting data without permission can lead to ethical and legal complications.
Such issues should be addressed during the initial stages of implementation to ensure transparency at all levels. Communicate the developments to customers and get the necessary permissions. Hire AI service providers to find a foolproof method to avoid breaking ethical AI regulations. This will ensure the brand value and reputation don’t take a hit.
Businesses spend more time building AI models and less time testing them before deployment. Whether the AI model has been developed from scratch or customized to suit business requirements, it has to be extensively tested and validated to ensure functionality and results.
Enterprises should invest in establishing a rigorous testing methodology where the AI models are tested in multiple environments and refined after each result. This reduces the risk of using faulty data or incorrect insights to make crucial business decisions.
Two common mistakes many businesses make when developing AI models are to either underfit them or overfit them for the data.
Underfitting is when the AI model is not complex enough to detect all the patterns in the data, resulting in missed insights and opportunities. The model is too simple to handle large datasets effectively.
Overfitting is when the model is too complex and designed exclusively to handle certain kinds of data. This makes it almost impossible for the AI model to learn from any other type of dataset and provides limited results.
Businesses can avoid both extremes by establishing the parameters for developing the AI model and following the necessary regulations to ensure a balance. Partnering with AI firms is the easiest way to achieve this balance.
AI malfunctions will be higher when the systems are not supported by the right IT infrastructure. Legacy systems are a boon and bane of an organization. Continuing to use outdated legacy systems will make it hard to develop the essential IT infrastructure required for artificial intelligence tools to be fully functional and effective. It also increases operational costs, slower results, and inaccurate insights or predictions.
Businesses should be prepared to revamp the IT infrastructure and replace it with advanced technologies (on-premises and cloud) to create a conducive environment for AI models to thrive.
Bias is a significant concern when using AI models. The AI algorithm is trained using datasets and learns from the given inputs. If the datasets and inputs are biased, the algorithm will display the same signs. So, how can organizations avoid a key mistake in AI model training?
Enterprises should acknowledge and accept that bias is real and found in datasets. This makes it easier to identify gender, religious, political, and racial biases already present in the data. Then, more datasets representing the wider and marginal community have to be included. The AI model has to be repeatedly tested to determine if it is learning bias so that necessary corrections can be made during the development stage.
Monitoring and maintenance are two key factors that ensure the business AI model is functioning without errors. Enterprises should allocate resources to continuously monitor the system even after it has been tested and validated.
This is an ongoing process and should not be avoided. The AI models should be retrained whenever there is new data or the business changes its KPIs. Neglecting AI management and maintenance can lead to inaccurate and outdated insights that affect business decisions.
The adoption of AI fails for a business when the management doesn’t pay enough attention to choosing the right partner. With so many AI service providers in the market, it’s necessary to know more about their offerings, domain expertise, knowledge, pricing, and project portfolio. Hiring a consulting company without proper research can lead to misaligned ideas and expectations.
Even if the service provider has a good reputation, the company may not be the best fit for certain business requirements. Take time to go through the service provider’s offerings and testimonials before finalizing them. Always consider the long-term goals when hiring an AI partner. Changing partners frequently can lead to unwanted complications.
Ultimately, it’s important to realize that AI is not a magic tool and has its limitations. However, most of the common artificial intelligence errors can be avoided with planning and continuous improvements to the systems and work culture.
Align goals, people, processes, and technology to get the expected results for the business. Hire experienced AI partners for end-to-end digital transformation and adoption of advanced technology. Increase ROI and enhance customer experience by revamping the business without making costly mistakes.