Dec 16, 2020

AI in business: five tips for successful implementation

David Ingham
3 min
While AI applications have become more mainstream, businesses must understand and apply these solutions in a smart, ethical, and economically friendly way
While AI applications have become more mainstream, businesses must understand and apply these solutions in a smart, ethical, and economically friendly w...

Artificial Intelligence (AI) has long since outgrown its vague sci-fi connotations to become a central pillar of business operations. While AI applications have become more mainstream, businesses must understand and apply these solutions in a smart, ethical, and economically friendly way. 

No two areas of AI application are exactly the same; each must be assessed in its own right. However, here are five shared guidelines all decision makers should keep in mind when considering implementing AI into any business operation: 

1. Target quick-value delivery 

In the balance between return on investment (ROI) and speed, speed is critical. When selecting the first business challenge to solve with AI, IT decision makers often make the mistake of only choosing the challenge that will create the biggest ROI. ROI must of course play a role in decision-making – no solution should cost more than the value it generates – however, this will not necessarily yield the biggest or best result for the business as a whole in the longer term. Quick wins, while often deemed smaller tasks, will usually demonstrate value more rapidly and help with broader buy-in. One example of this is implementing AI for large data analysis.   

2. Connect with everyone 

Buy-in is crucial. It must come not only from quickly being able to demonstrate value, but also from bringing people on board and keeping them engaged and connected throughout the whole AI journey. This includes everyone right up through senior leadership. Generating agency with decision makers is a lot easier if everyone understands the goals and requirements from the start and if they receive regular updates on the challenges and performance metrics throughout the project lifecycle.  

This feeds into funding conversations too, which can often be tricky. Even when early results are positive, it’s essential to manage expectations of varying business leads. Don’t be held to unrealistic standards. For example, achieving 100% automation or accuracy immediately, while great in theory, is not an appropriate benchmark. Having a greater understanding and accommodation of what everyone wants to achieve will make it easier when setbacks arise. 

3. Use data properly 

AI is only as good as the data that is fed into its engine. Disorganisation is one of the biggest hindrances to effective AI initiatives. Data must be both high quality and trustworthy, and it needs to be fed into the AI system in real time to produce effective results. Old data only drives insights that no longer have value. Most importantly, the data should be prepared, categorised, and classified to enable easy analysis.  

4. Don’t be scared to fail 

The phrase “right first time” rarely applies to AI implementations, especially for those making predictions and forecasts. Refining to an acceptable level of accuracy will take time and will often follow a series of failures in order to determine what to further correct and refine. 

Starting with a smaller problem or a subsection of a large problem can help reduce the risk associated with the cost of failure. There is also no shame in dropping an idea and rethinking the approach – in fact, that willingness to rethink is vital.  

Persisting with a flawed system – and, by so doing, wasting time and money – is never the right way to go. Instead, changing course or even dropping the idea altogether helps accelerate progress. Once a smaller problem is resolved and the business can see its value and ROI, the solution can be scaled to solve a bigger problem. 

5. Measure outcomes, not outputs 

AI projects are inherently different from IT projects, which tend to proceed with a clear idea and a set target for the desired output from day one. By contrast, we mostly use AI to try to understand the unknown, thus we really can’t know what the output will be ahead of time. As a result, AI needs to be tuned, monitored, and modified continuously over time.  

Successful AI implementations may therefore require more iterations than originally planned, and the outcome might not deliver the same level of accuracy or automation as initially imagined. Determine success by the degree of impact it creates and how much value that yields.  

Implement AI with cause 

Organisations should not implement AI without purpose. However, because the technology offers so many possibilities, prioritizing them can be overwhelming.  

It’s important to take a collaborative approach and focus on what is required and doable. Above all, be fearless. This is much easier when working alongside partners with much more experience at implementing new technologies like AI. AI is a learning technology, and its learning process always comes back to the people instigating the programmes as much as the AI itself. It takes courage and patience to appreciate these lessons. 

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Jun 15, 2021

The advantages and disadvantages of AI in cloud computing

3 min
AI is being used in cloud computing, which works by allowing client devices to access data over the internet remotely, but are there pros and cons?

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


Lower costs

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.

Deeper insights 

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. 

Intelligent automation 

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. 

Increased security 

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


Data privacy 

 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.

Connectivity concerns 

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.

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