Hotline: +84 (0) 8686 21 553  


Get ready for the emergence of AI-as-a-Service

Untitled design(43)

SaaS and PaaS have become part of the everyday tech lexicon since emerging as delivery models, shifting how enterprises purchase and implement technology. A new “_” as a service model is aspiring to become just as widely adopted based on its potential to drive business outcomes with unmatched efficiency: Artificial intelligence as a service (AIaaS).

The AIaaS opportunity

According to recent research, AI-based software revenue is expected to climb from $9.5 billion in 2018 to $118.6 billion in 2025 as companies seek new insights into their respective businesses that can give them a competitive edge. Organizations recognize that their systems hold virtual treasure troves of data but don’t know what to do with it or how to harness it. They do understand, however, that machines can complete a level of analysis in seconds that teams of dedicated researchers couldn’t attain even over the course of weeks.

But, there is tremendous complexity involved in developing AI and machine learning solutions that meet a business’ actual needs. Developing the right algorithms requires data scientists who know what they are looking for and why in order to cull useful information and predictions that deliver on the promise of AI. However, it is not feasible or cost-effective for every organization to arm themselves with enough domain knowledge and data scientists to build solutions in-house.

[Read: What are neural-symbolic AI methods and why will they dominate 2020?]

AIaaS is gaining momentum precisely because AI-based solutions can be economically used as a service by many companies for many purposes. Those companies that deliver AI-based solutions targeting specific needs understand vertical industries and build sophisticated models to find actionable information with remarkable efficiency. Thanks to the cloud, providers are able to deliver these AI solutions as a service that can be accessed, refined and expanded in ways that were unfathomable in the past.

One of the biggest signals of the AIaaS trend is the recent spike in funding for AI startups. Q2 fundraising numbers show that AI startups collected $7.4 billion — the single highest funding total ever seen in a quarter. The number of deals also grew to the second highest quarter on record.

Perhaps what is most impressive, however, is the percentage increase in funding for AI technologies — 592 percent growth in only four years. As these companies continue to grow and mature, expect to see AIaaS surge, particularly as vertical markets become more comfortable with the AI value proposition.

Vertical adoption

Organizations that operate within vertical markets are often the last to adopt new technologies, and AI, in particular, fosters a heightened degree of apprehension. Fears of machines overtaking workers’ jobs, a loss of control (i.e. how do we know if the findings are “right”?), and concerns over compliance with industry regulations can slow adoption. Another key factor is where organizations are in their own digitization journey.

For example, McKinsey & Company found that 67 percent of the most digitized companies have embedded AI into standard business processes, compared to 43 percent at all other companies. These digitized companies are also the most likely to integrate machine learning, with 39 percent indicating it is embedded in their processes. Machine learning adoption is only at 16 percent elsewhere.

These numbers will likely balance out once verticals realize the areas in which AI and machine learning technologies can practically influence their business and day-to-day operations. Three key ways are discussed below.

Empowering data

Data that can be most useful within organizations is often difficult to spot. There is simply too much for humans to handle. It becomes overwhelming and thus incapacitating, leaving powerful insights lurking in plain sight. Most companies don’t have the tools in their arsenal to leverage data effectively, which is where AIaaS comes into play.

An AIaaS provider with knowledge of a specific vertical understands how to leverage the data to get to those meaningful insights, making data far more manageable for people like claims adjusters, case managers, or financial advisors. In the case of a claims adjuster, for example, they could use an AI-based solution to run a query to predict claim costs or perform text mining on the vast amount of claim notes.

Layering insights for better outcomes

Machine learning technologies, when integrated into systems in ways that match an organization’s needs, can reveal progressively insightful information. If we extend the claims adjuster example from above, he could use AIaaS for much more than predictive analysis.

The adjuster might need to determine the right provider to send a claimant to based not only on traditional provider scores but also categories that assess for things like fraudulent claims or network optimization that can affect the cost and duration of a claim. With AIaaS, that information is at the adjuster’s fingertips in seconds.

In the case of text mining, an adjuster could leverage machine learning to constantly monitor unstructured data, using natural language processing to, for example, conduct sentiment analysis. Machine learning models would be tasked with looking for signals of a claimant’s dissatisfaction — an early indicator of potential attorney involvement.

Once flagged, the adjuster could take immediate action, as guided by an AI system, to intervene and prevent the claim from heading off the rails. While these examples are specific to insurance claims, it’s not hard to see how AIaaS could be tailored to meet other verticals’ needs by applying specific information to solve for a defined need.

Assisting humans at a moment’s notice

Data is power, but it takes a human a tremendous amount of manual processing to effectively use it. By efficiently delivering multi-layer insights, AIaaS provides people the capability to obtain panoramic views in an instant.

Particularly in insurance, adjusters, managers, and executives get access to a panoramic view of one or more claims, the whole claim life cycle, the trend, etc., derived from many data resources, essentially by a click of a button.

The place for AIaaS

AIaaS models will be essential for AI adoption. By delivering analytical behavior persistently learned and refined by a machine, AIaaS significantly improves business processes. Knowledge gleaned from specifically designed algorithms helps companies operate in increasingly efficient ways based on deeply granular insights produced in real time. Thanks to the cloud, these insights are delivered, updated, and expanded upon without resource drain.

AIaaS is how AI’s potential will be fulfilled and how industries transform for the better. What was once a pipe dream has arrived. It is time to embrace it.

R&D Engineer, MedCrypt — Om is a seasoned engineer who specializes in applying emerging technologies to new fields. Om regularly advocates for a more open government and is a passionate skier.

There are only a few $100 billion dollar industries out there — yet Facebook and Google sell personal data to advertisers for more than this amount on an annual basis. When the tech giants exploit consumers’ personal data for revenue gain, this data often gets sold and used without any regard to the individual.

Europe and California have introduced regulations to protect individual consumer data, attempting to fundamentally change the universal perspective on consumer privacy rights, yet there is no federal law to protect the rights of the individual. Data privacy will only continue to get more convoluted as we get further into the era of tech innovation, but with the right rules and regulations in place, we have the power to stay in the driver’s seat and maintain full control.

Today, as consumers in the US, we do not have any right to own or manage our data. Companies whose products or services we use on a daily basis use our data and sell it to advertisers. This data can include anything from your full name and address to who you are friends with as well as your full Google search history. There’s even evidence that DMVs in the US sell information such as addresses and age to advertisers. All of this without our explicit permission.

Over the past two decades, our data has become a gold mine for corporations. When corporations have to choose between protecting user data and maximizing profits, they’ll choose profits every time (they have to — it’s their duty to shareholders!). It is only through external pressures that a change can be enacted. There are few citizens in the community that are taking a proactive approach towards data privacy, while others continue to try and exploit consumer data.

The current legal framework does not sufficiently protect consumer rights at an institutional level, instead relying on individual behavior to ‘opt-in’ or not. Even when signing up for a service and given the chance to read the terms and conditions, there is no plausible way to limit the exposure of personal data. In reality, the only way to keep your data to yourself is to avoid operating in mainstream society, something that is nearly impossible today.

The first large-scale experiment in this realm was when Europe began enforcing a new legislation called General Data Protection Regulation (GDPR) in 2019 that gave consumers control over their data instead of the corporations. Tech companies went into an uproar, which stemmed from a fear that they would lose revenue previously gained from selling consumer data to advertisers. An unexpected side effect of GDPR is the creation of a competitive advantage for companies that already have access to consumer data.

Companies that aren’t meeting GDPR regulations, like Google and Facebook, have historically faced huge fines up to $5 billion, whereas other companies have blocked consumers in Europe from accessing their website completely. What the US needs to do is introduce similar legislation that will hand back control of personal data to each individual consumer. While there is a lot of opposition from the tech industry, California recently published the California Consumer Privacy Act (CCPA) bill, that does exactly this. On January 1, 2020, California began enforcing the first set of consumer data privacy protection laws in the US.

While GDPR has increased visibility into what information can be shared and allowed for better control of consumer data at large, we’re still being required to agree to blanket terms of service that ask us to consent to data sharing, and people still aren’t reading these terms. Is there any hope that CCPA will play out any differently?

The data privacy debate doesn’t stop here. Everyone’s data is being packaged and sold across multiple industries, such as healthcare, for example — and what we’re in dire need of is a larger retroactive set of rules to be put into place. HIPAA (Health Insurance Portability and Accountability Act), a law that’s been around since 1996, was ahead of its time in considering patient data protection. Yet in today’s connected era, the extent of interpretability has left it short as HIPAA does not apply to the entire healthcare industry, such as medical devices, for example.

Consumer rights should not only include the ability for a consumer to know what data the company is using, but also enable the consumer to control that data. Going one step further, companies need to ensure consumer data is protected and secured. This applies to nearly every day-to-day facet of life — including websites, apps, and IoT devices (including your connected toothbrush!). As more devices become connected, the risk of hacks at the scale of the Experian data leak rises and becomes more critical across various industries.

Privacy used to be an afterthought in the wake of a breach, yet today, consumers, regulators, and society mandate and require proactive security. To fully protect consumer data as we enter 2020, security must be the number one priority for organizations everywhere.