The modern hurdles preventing widespread AI adoption
Artificial Intelligence is used to inform and shape strategies across a range of industries, but there are still several challenges holding it back from widespread adoption. 2020 has proved the need for digital services and supporting AI is essential, but in many ways AI is not there yet. Ethical considerations must be addressed and operational difficulties, such as building a team with the right skill set, always provide an obstacle.
COVID-19 has given organisations across the world the need to expand their digital services. At first glance this would appear to benefit the spread of machine learning. When more people move their financial transactions and activity online, there is more data to tally and learn from. The question now becomes – is AI robust enough for the challenge?
In 2021 I believe AI will cross the chasm, becoming a reliable and safe, mainstream business technology — but maybe not how, or for reasons why, you might expect.
We often see technology developing at speeds that regulation cannot match. It can be a laborious task to bring new legislation into effect but, once ready, new tech can be swiftly implemented to meet regulation. This is why it is no longer good enough for AI-using organisations to ‘just do their best’. They must document and audit AI development around defined corporate standards of responsible AI.
Organisations must formally document and enforce their model development and operationalization standards and set them in the context of the three pillars of responsible AI: explainability, accountability, and ethics.
- Explainability: Organisations relying on an AI decision system must ensure they have an algorithmic construct that captures and communicates the relationship between the decision variables to arrive at a final business decision.
- Accountability: AI models must be properly built and focus has to be placed on the limitations of machine learning and careful thought applied to the algorithms used.
- Ethics: Adding to the requirements of explainability and accountability, ethical models must be tested continuously, and any discrimination removed.
There is no question about it, building responsible AI models takes time and is painstaking work. In a recent survey, more than 93% of data and analytics executives said that ethical considerations represented a barrier to AI adoption within their organizations. The meticulous and essential scrutiny is an ongoing process to ensure AI is used responsibly. This scrutiny must include regulation, audit and advocacy.
Regulations play an important role in setting the standard of conduct and rule of law for use of algorithms. In the end, however, regulations are either met or not, and demonstrating alignment with regulation requires audit. Organizations that adopt technology such as model governance blockchains will be in the best positions to respond.
Building a team with the right set of skills can be difficult. This is exhibited across a range of industries, with analytics leaders consistently ranking it as a high or medium barrier to entry.
Integrating new technologies, however, is often seen as the biggest problem in creating a machine learning framework. If an organisation is a long-standing business, it is highly likely it will face issues around legacy estates and integration of new AI technology into operational systems.
The list of challenges is long but by no means outweigh the benefits AI brings with it. As its advocates become more vocal and industry grapple with the rapid acceleration of digital, we will see Responsible AI rise up to cement itself in industries across the world.
The advantages and disadvantages of AI in cloud computing
Cloud computing offers businesses more flexibility, agility, and cost savings by hosting data and applications in the cloud. AI capabilities are now combining with cloud computing and helping companies manage their data, look for patterns and insights in information, deliver customer experiences, and optimise workflows.
We take a look at some of the benefits and drawbacks of AI in cloud computing.
The benefits of AI in cloud computing
A major advantage of cloud computing is that it eliminates costs related to on-site data centers, such as hardware and maintenance. Those upfront costs can be restrictive with AI projects, but with cloud enterprises you can access these tools for a monthly fee, making research and development related costs more manageable. AI tools can also gain insights from the data and analyse it without human intervention, reducing staff costs.
AI is able to identify patterns and trends in large data sets. Using historical data, AI compares it to the most recent data, which provides IT teams with well-informed, data-backed intelligence. AI tools can also perform data analysis fast so enterprises can rapidly and efficiently address customer queries and issues. The observations and valuable advice gained from AI capabilities result in quicker and more accurate results.
Improved data management
AI enables extensive data management, and cloud computing maximises information security, making it possible to deal with massive amounts of data in a programmed manner to analyse them properly, allowing the business to leverage information that has been “mined” and filtered to meet each need. AI can also be used to transfer data between on-premises and cloud environments.
Businesses use AI-driven cloud computing to be more efficient and insight-driven. AI can automate repetitive tasks to boost productivity, and also perform data analysis without any human intervention. IT teams can also use AI to manage and monitor core workflows. IT teams can focus more on strategic operations while AI performs the mundane tasks.
With businesses deploying more applications in the cloud, security is crucial in order to keep data safe. IT teams can use different AI-powered network security tools which can track network traffic, they can flag issues, such as finding an anomaly.
The drawbacks of AI in cloud computing
Enterprises need to create privacy policies and secure all data when using AI in cloud computing. AI applications require a large amount of data, which can include consumer and vendor information. While some data can be anonymous and can't be tied to personally identifiable information, knowing who the data belongs to makes it more valuable. When sensitive information is used, data protection and compliance is a major concern.
IT teams use the internet to send raw data to the cloud service and recover processed data. Poor internet access can hinder the advantages of cloud-based machine learning algorithms, as cloud-based machine learning systems need consistent internet connectivity.
While processing data in the cloud is quicker than conventional computing, there is a time lag between transmitting data to the cloud and receiving responses. This is a significant issue when using machine learning algorithms for cloud servers, where prediction speed is one of the primary concerns.