What is AI as a Service? Learn How it Can Benefit Your Business

Artificial Intelligence as a Service (AIaaS) is a service offered by cloud providers that allows businesses to leverage ready-to-use AI models and tools for the price of an upfront or monthly subscription fee. This lets the company use, test, and benefit from AI tools and processes without investing the resources to build them from scratch.

We now know that AI is pivotal to business success. So much so that generative AI is among the top three business priorities for 81% of Australian C-suite executives, this doesn’t address the core problem, though: AI models are expensive. And they require enormous amounts of expertise to create and maintain.

AIaaS bridges that gap by giving companies access to AI without the requirement to build it themselves. That means businesses of all sizes can use AI and machine learning (ML) regardless of whether they employ experienced data scientists or have the resources to manage deployment internally.

AIaaS offerings are ready-made AI and ML models. That means the vendor has deployed them to perform a specific task like answering customer service queries or analysing data.

These solutions are ready to go out of the box, and they’re easier to use and require minimal technical expertise. In some cases, this can limit customisation options, though, meaning businesses need to work harder to find a solution that’s the right fit for their goals.

AI Platform as a Service (AIPaaS) is an end-to-end solution that provides organisations with the tools to train and deploy their own AI models for a flat fee or subscription. This makes them ideal for custom solutions like building predictive models for specific business requirements rather than relying on a pre-built model.

However, AIPaaS solutions typically require more technical input. The right choice between the two will depend on your requirements.

AIaaS provides artificial intelligence capabilities to businesses through the cloud. Vendors maintain and operate their solutions on their own cloud computing infrastructure, so they have the storage and power that they need to deploy AI models at scale.

Once they pay the initial fee, a business gains access to the AIaaS provider’s AI and ML resources through application programming interfaces (APIs). These interfaces enable an organisation to integrate its existing tech stack with an AI service to directly send business data to it.

From there, the AIaaS platform organises and processes the data using machine learning algorithms that can draw patterns and derive insights from large datasets. Depending on the task, the algorithm will provide predictions and recommendations to the business in real-time through the organisation’s integrated applications or dashboards.

As you can imagine, this is a powerful prospect for any business that wants to make the most of their data while keeping control of their budget.

One of the primary benefits of AIaaS is its wide range of use cases. Let’s run through some common types of AIaaS and how a business can apply them to their operation.

Various AIaaS vendors package AI solutions designed to benefit a business’s end customer. For instance:

• Chatbots and more advanced AI agents use natural language understanding (NLU) and processing to handle user queries and respond in context. You’ll be familiar with this conversational AI model if you’ve recently engaged with a company’s live chat.
• Recommendation systems analyse patterns to provide personalised product recommendations based on customer data. Consider how Netflix’s AI digital assistant recommends similar shows based on your past watching habits.
• Sentiment analysis AIaaS solutions use natural language processing (NLP) algorithms to find patterns in customer conversations and evaluate opinions, enabling businesses to refine their brand strategy and, in turn, provide better customer experiences.
• Digital assistants (or virtual assistants) use speech recognition and NLP to understand and respond to spoken language queries. Some AIaaS tools even offer instant translation, letting businesses interact with customers in different languages.

Each of these AIaaS variations allows organisations to provide a better, more efficient service to their customers.

On the business side of things, several types of AIaaS exist to help companies make sense of and derive insights from their data. For example:

• Predictive analytics models use ML algorithms to analyse business data and predict future outcomes. The company can then use these forecasts to plan ahead and make more informed decisions.
• Data labelling models annotate datasets automatically, helping to organise and cleanse information ready for data analytics or supervised learning.
• Business intelligence tools use AI to create dashboards and visualise business data so that organisations can see key insights within the data they possess.
• Anomaly detection solutions identify outliers in data to support accurate data analysis and help with tasks like fraud detection.
• Computer vision analyses visual data like images and video data for object detection and pattern recognition. It’s ideal for tasks like quality control in manufacturing and gaining customer insights through image data.

AIaaS creates an affordable, structured way for companies to benefit from data science without investing the resources to manage projects themselves.

AIaaS vendors also offer packages that help businesses automate tedious tasks, reduce manual errors and increase efficiency. Examples include:

• Robotic process automation (RPA) tools use AI to automate all kinds of processes, such as data entry and workforce scheduling, freeing up time to focus on core business activities.
• Supply chain optimisation solutions use predictive analytics models to forecast demand and optimise inventory levels accordingly. This reduces costs by improving supply chain efficiency. It can also boost customer satisfaction by ensuring products are readily available.
• Artificial Intelligence of Things (AIoT) services bake AI and ML capabilities into IoT devices, enabling models to gather and analyse data automatically. For example, an AIoT sensor will let the device predict maintenance before it leads to an interruption.
• AIaaS marketing solutions can use natural language generation (NLG) to automate content production across all business channels, such as social media posts and blogs, allowing businesses to create content at scale while expending fewer resources.

Each of these AIaaS models comes ready-made to let businesses automate processes and scale effectively.

Certain AIaaS providers package frameworks and tools to help developers build and deploy their own AI and machine learning models in less time.

• Machine learning frameworks give developers the tools to build and deploy ML models. They can create their own custom solutions without investing significant resources.
• Application Programming Interfaces (APIs) are a framework for apps to communicate and interact with one another. For instance, an eCommerce business might sync an AI agent with its CRM platform and marketing data to manage leads more effectively.
• No-code or low-code ML Platforms let companies build custom AI models with minimal expertise through pre-built modules and simple interfaces. Agentforce, for instance, allows businesses to build custom AI agents using a library of pre-built skills with little to no technical knowledge.

Whether a seasoned developer or a first-time business owner, there’s an AIaaS that will let you build a custom solution for your organisation.

To bring the advantages home, let’s recap the core benefits of an AIaaS for businesses.

• Save cash: AIaaS lets businesses get the best out of AI and stay competitive without investing significant amounts of capital in internal AI infrastructure and expertise.
• Save time: Because AIaaS is already built, businesses can receive the benefits of AI instantly rather than spending months training their own models from scratch.
• More productivity: AIaaS offerings provide professionals with more time to focus on high-value processes by automating tedious tasks.
• Easier scalability: AIaaS vendors operate on the cloud. This removes infrastructure barriers to implementing AI technologies, letting businesses scale up as they grow.
• Greater accuracy: AIaaS solutions significantly reduce the possibility of human error by performing repetitive tasks with a high degree of accuracy and reliability.

Overall, AIaaS lets businesses leverage advanced, easily scalable AI solutions, all at a fraction of the price of building one alone.

We’ll now show you how access to AI capabilities can benefit companies. Let’s discuss two real-world case studies of businesses that have implemented these solutions and how they helped.

The hospitality sector is particularly challenging because businesses need to deliver personalised experiences to keep pace with consumer demands. This costs a great deal of cash and takes significant staff resources to implement, as you can imagine.

NightOwl, an Australian-based hospitality company, experienced this challenge firsthand when they wanted to create more unique moments for their customers. To overcome resource issues, they needed a solution to provide engaging experiences at scale across Australia.

NightOwl partnered with Salesforce to make their goal a reality. With Agentforce, the venue host created an autonomous AI agent to provide 24/7 customer support on all its venue websites. The agent has a unique knowledge base for each of the business’s venues and can provide conversational, relevant advice tailored to that venue’s specifications and tone of voice.

Agentforce now handles over a third of all of NightOwl’s customer inquiries, which frees up staff time to focus on delivering exceptional customer experiences. Soon, the brand plans to use Data Cloud, our native hyperscale data engine, in tandem with our Marketing AI to create personalised journeys and better engage customers at every touchpoint.

One of the current goals in healthcare is determining ways in which AIaaS can help to improve patient treatment protocols for deadly diseases.

Max Kelsen is one of Australia’s leaders in this emerging field. The analytics and software engineering agency is aiming to combine whole genome analysis with AI to deliver personalised treatment plans for those living with cancer.

While Max Kelsen is no stranger to AI technology, the brand faced a problem: exploring a single whole genome demands around 300GB of computer power. Analysing information on this scale is nearly impossible without the correct infrastructure.

To help, Max Kelsen turned to Google Cloud AI’s TensorFlow (Google’s open-source ML framework). This not only gave the organisation access to the computing power required for big data analytics but also provided the toolkit needed to build a custom AI model for the task at hand.

With AIaaS behind them, Max Kelsen now has the resources to spearhead its Immunotherapy Outcome Prediction (IOP) AI project, which aims to support cancer research with AI and help Australians impacted by the disease.

AIaaS is an out-of-the-box solution. But you should still take some care to make sure the integration goes as planned. Here are the steps to take to implement AIaaS properly.

Start by defining your objectives for AIaaS adoption. This is the goal that you’re hoping your solution will achieve. It could be tied to a problem you want to solve or a benefit you’d like to achieve. For instance, maybe you want to:

• Boost efficiency for sales teams
• Improve data analytics to make more accurate forecasts
• Find new ways to optimise your current supply line
• Use AI to handle common customer inquiries

Liaise with stakeholders and break down your objectives into quantifiable KPIs. This step will help you choose the right AIaaS vendor for your use case and measure success once your project is underway.

Now, you can take your goal and use it to find the appropriate provider. Here are some of the elements you should consider:

• Expertise and reputation: Look for a vendor that has a strong reputation relevant to your industry and use case.
• Integrations: Make sure to opt for a provider that provides API integrations with platforms and apps, such as your CRM system.
• Pricing models: Check different pricing packages to determine if the solution is within your budget.
• Scalability: You need to choose a vendor that can scale their services easily as your business grows.
• Security and compliance: Look for a diligent approach to security and a proven record of compliance with legislations like GDPR and HIPAA, if relevant.

Above all, choose a provider that aligns with your goals, but that is still tailored towards the problems you’re facing.

For instance, if you wanted to generate more leads and personalise client experiences while also automating tedious tasks for sales reps, a solution like Sales AI is comprehensive enough to help with all of these goals, but it’s also tailored specifically to the sales sector.

It’s important to pick an AIaaS that has a breadth of features but isn’t overly complex to the point it loses focus of your unique challenges.

You’ll need to collect and process your data to make sure it is high-quality and reliable before you can begin with your AI solution. You should also filter through data at this point to find the information most relevant to your goal.

Some AIaaS vendors offer a built-in solution that can help you collate and process your data without needing to do it manually.

As an example, it’s common for our customers to combine our AI solutions with Data Cloud, our data platform. This solution unifies and organises all of your siloed, unstructured and structured data through a library of connectors, meaning you can spend more time benefiting from AI insights rather than aggregating disconnected data yourself.

Integrations will link your AI solution to your existing tech stack. This is vital on both sides of the equation. First, it allows your integration to provide the AIaaS with a consistent stream of information. Second, it lets your AIaaS feed insights and predictions back into your systems, enabling real-time decision-making.

Use your AIaaS vendor’s APIs and SDKs to make sure your current tools can communicate with your AI solution.

For instance, it’s good practice to link your marketing AI solution to your CRM system: the CRM provides recent customer data to the AI about past interactions and purchase history; the AI returns with real-time insights that the whole marketing team can leverage.

Even the most straightforward AIaaS package has a learning curve. Train your team so they know how to use and benefit from the solution.

A technical workshop is usually the best way to go. It’s also helpful to reiterate that AI is only there to boost efficiency and free up time for important tasks rather than replace the role of a human.

Measure the performance of your new AIaaS solution against your KPIs once you deploy it. This is the simplest way to see if the model is having the desired results.

Also, the qualitative elements should be measured at this point. How are your staff finding the model? Is it helping them or hindering them? Similarly, if your AI solution handles inquiries, it’s important to get feedback to gauge customer sentiment.

AIaaS is revolutionary for businesses that want to keep pace in the AI race but lack the time, resources, or expertise to build and deploy models internally.

The key is to find an AIaaS solution that’s specific to your goals. With a list of potential candidates, you can then work backwards to evaluate reputation, compliance and pricing models. With the right business strategies, you’ll land on a rapid, ready-made solution that’s fast and affordable to implement while being highly relevant to your organisation’s use case.

Salesforce AI has a range of plug-and-play AI solutions tailored for specific business use cases and problems. Try Salesforce AI with a free trial today to see what’s possible.