How to Use the Power of the Cloud to Accelerate AI Adoption | Entrepreneur

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Artificial intelligence (AI) and machine learning (ML) are not new concepts. Equally, leveraging the cloud for AI/ML workloads is not particularly new; Amazon SageMaker was launched back in 2017, for example. However, there is a renewed focus on services that leverage AI in its various forms with the current buzz around generative AI (GenAI).

GenAI has attracted lots of attention recently, and rightly so. It has great potential to change the game for how businesses and their employees operate. Statista’s research published in 2023 indicated that 35% of individuals in the technology industry had used GenAI to assist with work-related tasks.

Use cases exist that can be applied to almost any industry. Adoption of GenAI-powered tools is not limited to only the tech-savvy. Leveraging the cloud for these tools reduces the barrier to entry and accelerates potential innovation.

Understanding the basics

AI, ML, deep learning (DL) and GenAI? So many terms — what’s the difference?

AI can be distilled to a computer program that’s designed to mimic human intelligence. This doesn’t have to be complex; it could be as simple as an if/else statement or decision tree. ML takes this a step further, building models that make use of algorithms to learn from patterns in data without being programmed explicitly.

DL models seek to mirror the same structure of the human brain, made up of many layers of neurons, and are great at identifying complex patterns such as hierarchical relationships. GenAI is a subset of DL and is characterized by its ability to generate new content based on the patterns learned from enormous datasets.

As these methods get more capable, they also get more complex. With greater complexity comes a greater requirement for compute and data. This is where cloud offerings become invaluable.

Cloud offerings can be generally categorized into one of three categories: Infrastructure, Platforms and Managed Services. You may also see these referred to as Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), and Software-as-a-Service (SaaS).

IaaS offerings provide the ability to have complete control over how you train, deploy and monitor your AI solutions. At this level, custom code would typically be written, and data science experience is necessary.

PaaS offerings still offer reasonable control and allow you to leverage AI without necessarily needing a detailed understanding. In this space, examples include services like Amazon Bedrock.

SaaS offerings typically solve a particular problem using AI without exposing the underlying technology. Examples here would include Amazon Rekognition for image recognition, Amazon Q Developer for increasing software engineering efficiency or Amazon Comprehend for natural language processing.

Practical applications

Businesses all across the world are leveraging AI and have been for years if not decades. To illustrate the variety of use cases across all industries, take a look at these three examples from Lawpath, Attensi and Nasdaq.

Challenges and considerations

Whilst opportunity is plenty, harnessing the power of AI and ML does come with considerations. There’s lots of industry commentary about ethics and responsible AI — it’s essential that these are given proper thought when moving an AI solution to production.

Generally speaking, as AI solutions get more complex, the explainability of them reduces. What this means is that it becomes harder for a business to understand why a given input results in a given output. This is more problematic in some industries than others — keep it in mind when planning your use of AI. An appropriate level of explainability is a large part of using AI responsibly.

The ethics of AI are equally important to consider. When does it not make sense to use AI? A good rule of thumb is to consider whether the decisions that your model makes would be unethical or immoral if a human were making the same decision. For example, if a model was rejecting all loans for applicants that had a certain characteristic, it would be considered unethical.

Getting started

So, where should businesses start with AI/ML in the cloud? We’ve covered the basics, a few examples of how other organizations have applied AI to their problems and touched on the challenges and considerations for operating AI.

The starting point on any business’s roadmap to successful adoption of AI is the identification of opportunities. Look for areas of the business where repetitive tasks are performed, especially those where there are decision-making tasks based on the interpretation of data. Additionally, look at areas where people are doing manual analysis or generation of text.

With opportunities identified, objectives and success criteria can be defined. These must be clear and make it easy to quantify whether this use of AI is responsible and valuable.

Only once this is defined can you start building. Start small and prove the concept. From the solutions mentioned, those at the SaaS and PaaS end of the spectrum will get you started quicker due to a smaller learning curve. However, there will be some more complex use cases where greater control is required.

When evaluating the success of a PoC exercise, be critical and don’t view it through rose-tinted glasses. As much as you, your leadership or your investors may want to use AI, if it’s not the right tool for the job, then it’s better not to use it. GenAI is being touted by some as the silver bullet that’ll solve all problems — it’s not. It has great potential and will disrupt the way a lot of industries work, but it’s not the answer for everything.

Following a successful evaluation, the time comes to operationalize the capability. Think here about aspects like monitoring and observability. How do you make sure that the solution isn’t making bad predictions? What do you do if the characteristics of the data that you used to train the ML model no longer represent the real world? Building and training an AI solution is only half of the story.

AI and ML are established technologies and are here to stay. Harnessing them using the power of the cloud will define tomorrow’s businesses.

GenAI is at its peak hype, and we’ll soon see the best use cases emerge from the frenzy. In order to find those use cases, organizations need to think innovatively and experiment.

Take the learnings from this article, identify some opportunities, prove the feasibility, and then operationalize. There is significant value to be realized, but it needs due care and attention.

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