According to Technavio, the global artificial intelligence-as-a-service (AIaaS) market is expected to grow by $14.7 billion from 2021 to 2025. AI has proven itself to be vital to businesses for everything from customer experiences to automating redundant tasks. The use cases of AIaaS align closely with goals of digital transformation. Achieving growth for your organization requires not only the right talent, but future-focused technology as well.
In an era where data is accumulating faster than ever, implementing technology that can quickly process it to enable companies to act in a matter of minutes versus months will put them ahead of their competitors.
The Quickest, Cheapest Way to Implement AI: Third-Party Services
The quickest and most cost efficient way to deploy AIaaS is through third-party services. Building a team of AI experts internally is more expensive, and the recruiting process can be quite challenging. When finding the right experts, recruiters need to evaluate technical skills, creativity, business acumen and — because AI systems amplify any biases of its creators — an unwavering sense of ethical and moral responsibility.
Not only is the talent behind the technology expensive, but developing in-house AIaaS processes and frameworks is a long, complicated process and requires significant capital investment. Going through the process of implementing AI with current processes within a company results in significant cost and effort, often without a guaranteed return. Even when such talent is secured, teams are often bogged down by laborious tasks associated with data preparation, model selection, as well as operational tasks such as the deployment of models into a production environment.
To start, companies are well advised to seek out external experts who have experience working with a variety of clients and already possess the latest AI tools and unique intellectual assets. But, successful projects start with upfront acknowledgement that the organization will have to address more than just the technical aspects of AI — they will also be examining what AI really means to the organization and how the most value can be derived from it. There are many benefits to this approach, including a frictionless onboarding process, short time to prove value, and incremental return on investment through a focus on business outcomes, applied with experience and a knowledge of industry best practices. Without the ramp-up required in an otherwise new context, their services save time, effort, and money.
Choosing the Right AI
Companies that are evaluating different AI platforms should consider what levels of bias exist in the data that is used to build the algorithms and models. Although measurement of bias is an emerging field, a critical step is backtesting AI outcomes and then evaluating the effects on various stakeholders. For example, if a bank loan algorithm is based solely on prior lending activity, who does it leave out? The right AI platform should enable a broad set of different scenarios aligned with the goals and values of your organization.
Some of those AIaaS capabilities could include:
AI-as-a-Service Capabilities
- Anticipating retention and churn
- Outcome prediction
- Product recommendation
- Fraud detection
- Marketing and messaging
- Customer segmentation
1. Retention/Churn
Developing a system to understand your customers in terms of their potential economic value is critical. By identifying people that are more likely to buy again, or people who are more likely to buy another product or service from the same provider, an organization can, through online AI systems, preemptively anticipate the customer’s needs and start laying the foundation that the additional product or service is what they need.
2. Outcome Prediction
AIaaS enables organizations to achieve quicker time-to-value by employing the power of AI to activate modern products that are laser-focused on outcomes for the end-user. By combining AI with cross-functional third-party expertise to develop adaptable systems, the best possible outcomes are delivered continuously.
3. Product Recommendation
More and more, organizations are relying on AI and machine learning to deliver recommendations based on the behavior of individual users. Systems can provide tailored, personalized recommendations at scale, making buyers less reliant on reviews and ratings. In a world where there is an expectation for products to provide a tailored or contextual experience, it’s more important than ever to carefully prioritize the products or features to invest in.
4. Fraud Detection
As the sophistication of fraud continues to evolve, so do the tools used to detect fraudulent activity. Fraud and other malicious activity detection are frequently implemented as event-based systems. Event-based systems can both allow for quick detection of malicious activity while also handling bursts in load, without blocking high-volume transactions.
5. Marketing Technology/Messaging
A better way to increase the performance of your marketing function is to work with an organization that can dedicate the time to leverage every touchpoint with the audience to guide each next action, from a creative marketing aspect, and then identify the technology that will help deliver that. With the right expertise, the right investment and plenty of time to feed the engine to deliver the results, the promised benefits of marketing technology can become reality.
6. Customer Segmentation
Leverage AI and machine learning (ML) to segment customers by their behavioral data such as: how many sessions they have on the website, how many transactions they made, how much revenue was generated by those transactions, etc. Once you have these groups, you can identify potential pain points for particular types of customers and improve the intelligence behind your A/B testing program.
