By 2025 is predicted the estimated value of the AI market will reach USD $190.61 billion (Fobes). AI or artificial intelligence and ML or machine learning are buzzwords commonly used in a wide array of industries and markets. Artificial intelligence describes the science and engineering that allows computers to behave in ways that would commonly require human intelligence. Machine Learning (ML) is a branch of artificial intelligence that helps improve and automate data processing without the need for consistent human intervention. The Global Machine Learning Market is expected to grow by 42.08% (Learn Hub) during 2018 and 2024.
As the technology grows in popularity and adoption, companies are likely to make errors around implementation and need to understand the best methods and practices to remedy the same. Common concerns around AI/ML implementation include;
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Mismanaging AI Security and Privacy aspects
Implementation of an AI system by default presents cyber threats. Preventive security controls should be incorporated to preserve the application of the system. Cyber criminals operate by identifying the weakest link within an organization’s digital system and aggressively implement their destructive measures. Without adequate protective measures, companies are at risk of not only having the newly implemented technology fail but further and more insidious threats to data and day to day activities.
According to a Huawei AI Security White Paper, businesses must identify the difference in security vulnerabilities within both traditional software and AI driven solutions. When AI systems are not equipped with adequate security measures, adversarial ML techniques including evasion, poisoning and backdoor attacks can derail and negatively influence the AI system creating undesirable results and opening a back end for virtual criminals to exploit.
During the design stage of the AI software it is imperative to install security measures that work to minimize possible threats. When implemented in the pre-production testing stage it could be considered too late.
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Mismanaged Training Data
AI is unable to exist without relevant large volumes of high quality data. Without this information, ML models cannot be accurately built. In various fields, AI systems require training that consists of updated and improved data to help maintain an active and ever learning decision-making system.
Unfortunately, a large number of organizations underestimate the importance of data use in AI projects and end up with unusable systems that are unable to perform as needed resulting in long standing financial losses. While there are some tasks that require minimal data input into the system, the quality of this data needs to be upheld through frequent cleaning and pre-processing. If an organization is running data heavy AI, the organization must ask themselves whether this information and the format of the data would work for AI training purposes.
In order to prevent future concerns when implementing an AI solution, the business must take a data first approach in order to best understand that existing data pools are usable for AI training. This in turn helps the business better understand the outputs of the AI system and pinpoint possibilities for inaccuracies or biased outcomes.
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Do Not Neglect an AI Business Strategy
The implementation of an AI system is to further propel business goals and meet business objectives. A common mistake organizations make is to implement the AI system in order to solely move through the cycle of day-to-day activities faster. Businesses must consider the purpose of the AI system, discuss and approve the same internally. Legal and concerns around regulation must also be assessed. This helps prevent legal situations in the future around conflicts with stakeholders or possible violations of the law.
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Understanding How Necessary Transparency around AI generated Output is
A number of fields are mandated by law to release the methods used to create their output. When entering the realm of AI and ML, there are two possible ways to generate a result;
- White Box AI: This AI system offers crisp details around how output is developed. Data scientists are able to understand the algorithm used and behavior factors considered during the decision-making process. White Box systems are ideal for business use as it offers as much information as possible. This helps a business understand predictions for better business decisions and possibly detects areas of concern should something go wrong.
- Black Box AI : Black Box AI systems offer insights but does not alert the user on how these insights are produced. ML programs are able to reach conclusions from data inputted but there is little clarity around how the program came to this system. While this was standard industry practice, as laws and regulations around transparency are becoming more important, businesses are forced to migrate to using White Box AI. Though Black Box AI does come with its advantages; the output of the system tends to be alarmingly accurate. This stems from the complexity behind the AI algorithm creating the lack of transparency.
It is up to an organization to find middle ground between a transparent, end to end system and creating accurate results. As both systems are needed to generate the best possible information for a business, striking the right balance between the two is critical. If an organization is using a large number of Black Box AI solutions, the system needs to be contained to smaller functions like anomaly or pattern detection. This helps keep the algorithm transparent to some degree within the organization.
It is also important to understand deep learning models are often obscure and complicated by design. This concern can be bypassed to some degree through extensive testing and reviews. Human supervision is a non-negotiable part of using an AI system especially during the implementation part. When an organization is able to understand the method used to uncover mission critical findings, it helps build reliability and sustainability around the information used for business.
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Replacing High Level Skilled Human Labor with AI
A common side effect of the pandemic of 2021 is the replacement of human labor by technology including ML and AI systems. As fear of spreading the virus increased, more employees were let out of their jobs forcing organizations to switch to more technology based solutions. This may work for menial and repetitive tasks, but cannot work for tasks that require complex human skill sets.
AI and ML systems are able to enhance internal efficiency through minimizing redundant activities and carrying them out during shorter periods of time without the risk of human error. It is important to note that AI has its limits. AI systems cannot replace experienced professionals within any field and conduct business or activities based on underlying context.
It is imperative for a business to not overestimate the capabilities of modern AI and retain hired professionals with strong skill sets within the organization. AI is still far off from entirely replacing human instinct and it would be best to have in-house professionals aided by AI and ML systems in order to optimize business operations.
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Lack of Coordination around AI Implementation
While AI and ML systems are able to solve a multitude of problems, it is imperative for implementation to include a strong strategy that binds the system with existing processes and technologies. While organizations are quick to replace existing systems with AI, that decision may not be the most feasible.
AI solutions work as almost an accessory to existing systems and should be implemented where it is needed. When a solid implementation strategy that aligns with existing solutions and processes is implemented, the problem solving system moves along faster while minimizing the possibility of internal conflicts.
At a more birds eye level, gaining insights from employees at all levels of the organization around where to best implement the AI system could prevent a possible mishap or waste of resources.
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Not Understanding Budgetary Requirements
43% (PMI) of all organizations that have implemented some degree of AI have superseded their initial budgets. When implementing an AI system the organization must put together a realistic budget estimate accounting for continuous improvement, training and maintenance costs.
Data qualification is also a critical component of successful AI implementation which can get quite expensive. Is an inhouse team required to service the system or address concerns as needed? There are a number of questions a business must ask themselves before considering adding AI into the mix.
In order to avoid a waste of significant time and resources, a business must do its due diligence or hire professionals with the capabilities to ascertain end to end costs before deciding to implement even the smallest of components.
Conclusion
With over 51% of organizations today moving into 2022 relying on some degree of AI and ML systems to move day to day activities, understanding the best possible practices around implementation is critical.
For businesses unsure of how to implement the right system to meet future goals, it would be advisable to recruit BI services or business intelligence professionals. These teams are equipped with the necessary skills to introduce ML and AI systems into organizations of any size in the best possible way.
As the technology gains momentum across industries, it is advisable to jump on the bandwagon with haste or amplify existing use with accuracy to avoid falling behind competitors.
