Most organisations fail to make proper use of AI.
Legacy systems, unclean data, failed model deployments and a lack of understanding leaves businesses stuck in a constant loop of struggle.
With AI rapidly becoming a critical component of a successful enterprise, it’s important that your organisation is aware of the four key components that must be in place for it to be successful.
Which components, you might ask. We’re talking about:
- Cloud migration,
- Data engineering,
- Machine Learning engineering, and
- Software engineering.
1. Cloud Migration:
The first step in implementing AI in an enterprise is to move all data and operations to the cloud.
This is essential because AI algorithms require large amounts of data to train, and the cloud provides the scalability, security and flexibility needed to handle this data. Additionally, the cloud makes it that much easier to access the compute resources necessary to run AI algorithms.
According to Apps Run the World:
- The Cloud market has grown year-over-year from £24,53 billion in 2013, to well over the £100-billion mark in 2018.
- By 2025, the cloud applications market is expected to be worth $168.6 billion.
A report by Synergy also found that cloud infrastructure services — including Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and hosted private cloud — pulled in over £40,46 billion in sales during the 4th quarter of 2021 alone.
(Looking to get onto the cloud? Our cloud services might be exactly what you need)
2. Data Engineering:
Once the data is in the cloud, it must undergo the right preparation for use in AI algorithms.
This involves cleaning and transforming the data, as well as building data pipelines to make the data available to machine learning models.
According to a LinkedIn article by Kesha Sha, approximately 2.5 quintillion bytes of data are created each day.
With these vast amounts of data, it’s essential that data engineers have a good understanding of the data and the business domain in order to make the data usable for AI.
(Get your data ready for AI with our data engineering specialists)
3. Machine Learning Engineering:
With the data prepared, machine learning engineers can begin to build and train models.
This involves selecting the appropriate algorithm and tuning the parameters to achieve the best performance. ML engineers must also be able to interpret the results of the models and make decisions about how to use them in the enterprise.
According to Finances Online, McKinsey findings reveal that:
- The potential global economic activity that AI could deliver by 2030 is over £10,49 trillion.
- 50% of survey respondents said that their companies have adopted AI in at least one business function.
- Revenue increases from AI adoption are commonly reported, with 80% of people saying that AI has helped increase revenue.
(For ML models that work, check out our Machine Learning offers)
4. Software Engineering:
Once the models are trained, they must be integrated into the enterprise’s existing software systems.
This requires software engineers to design and implement the necessary APIs and interfaces. As well as ensure that the AI systems are secure, scalable, and maintainable.
According to Statista:
- The total value of global consumer spend on mobile apps amounted to £26,63 billion in 2021.
- IT spending on enterprise software is set to amount to around £544,67 billion worldwide in 2022.
(Software for your specific business needs? We’ve got Software Engineers!)
Make AI Work For Your Business Today
By focusing on these four critical components, enterprises can ensure that their AI systems are effective, elastic and efficient. It’s the only way to
It’s also important to remember that these components are interdependent. For example, data engineering is critical for machine learning engineering to work well.
Having a holistic approach when implementing AI in an enterprise and understanding the interdependencies among these components is an absolute must.
It’s safe to say that cloud adoption, data engineering, machine learning engineering, and software engineering are essential for successful AI implementation in an enterprise. By focusing on these four critical components, your organisation can ensure that AI systems are effective, efficient, and integrated with your existing systems.
Want to make AI work for you? Contact us and find out how!