AI As A Service For Digital Transformation

Most organizations handle big data every day. ​Data accumulates and adds more terabytes without providing any value to a business or driving business intelligence.​ In 70+% of cases, actionable insights and business intelligence potential are underestimated.​

AI as a Service helps to solve these issues, allowing to employ any ready-made artificial intelligence solution customizing it to fit the organization’s needs and its digital transformation flows.

What is AI as a Service (AIaaS)?

AI as a Service is the outsourcing of artificial intelligence (AI). Today, organizations tend to partner with companies providing the full range of services to handle their AI solutions – that is the pure AIaaS case.

Many cloud providers offer APIs to use without creating Machine Learning models from scratch. And the outsourcing companies take advantage of the cloud vendors’ infrastructure to provide their customers with fully featured AI solutions that completely fit their business needs.

What experts predict about AIaaS?

Let’s delve a bit deeper into what experts (and latest researches) say about AI as a service and its influence on an organization’s digital transformation flows:

"Leveraging artificial intelligence to add business value with a competitive edge is no longer a vision of the future for businesses worldwide. Applying AI — and applying it in the right way and responsibly — is rapidly becoming a key part of their plans for transforming into high-performing digital businesses that predict and shape future outcomes, accelerate innovation, reduce risks, empower their employees to perform higher-value work, and automate and optimize business processes, decisions, and experiences today."

Ritu Jyoti, program vice president, AI and Automation Research Practice at IDC

Here are a few predictions about AI, digital transformation, and related technologies for the nearest future to back Ritu Jyoti’s claim from IDC:

  1. “By 2024, Over 50% of All IT Spending Will Be Directly for Digital Transformation and Innovation (up from 31% in 2018), Growing at a CAGR of 17% (Versus 2% for the Rest of IT).”
  2. “By 2025, at Least 90% of New Enterprise Apps Will Embed AI; by 2024, over 50% of User Interface Interactions Will Use AI-Enabled Computer Vision, Speech, Natural Language Processing, and AR/VR.”
  3. “By 2023, 60% of the G2000 Will Have a Digital Developer Ecosystem with Thousands of Developers; Half of Those Enterprises Will Drive 20%+ of Digital Revenue Through Their Digital Ecosystem/Platform.”

According to Allied Market Research, the global AIaaS market size is expected to reach $77,047.7 million in 2025, from $2,397.2 million in 2017 growing at a CAGR of 56.7% from 2018 to 2025.

Source: alliedmarketresearch.com

Based on region, the AIaaS market was analyzed across North America, Europe, Asia-Pacific, and LAMEA. In 2017, North America contributed the highest share in the AI market. Though the research predicts the possible shift, stating that the Asia-Pacific region would exhibit the highest CAGR of 59,9% till 2025:

Source: alliedmarketresearch.com

How AIaaS works?

Let’s say, an organization ‘A’ has lots of data but doesn’t have enough capacity to handle it and derive any actionable insights. The organization ‘A’ seeks to understand and automate tasks that the human visual system can do, plus solve a wide range of tasks that humans can’t handle. That is where AI-as-a-Service steps in:

  1. The organization ‘A’ partners with an outsourcing company ‘B’ capable of providing a full suite of services to manage large-scale AI solutions.
  2. Company ‘B’ holds the discovery phase with the organization ‘A’ to remove the gap between its current business needs and available AI solutions.
  3. Employing the chosen ready-made AI solution (or solutions) and applying the organization’s ‘A’ big data, the company ‘B’ customizes it to meet the organization’s ‘A’ expectations:

Customized existing AI solutions + organization’s ‘A’ Big Data = more actionable insights and added values for business!

Types of AIaaS

There are four basic types of AI as a Service. Which one to choose depends on the organization’s business needs and expectations:

  1. Cognitive computing APIs: allow employing a third-party technology or service without building its code from scratch.
Technologies used in popular cognitive computing API solutions:
  • Computer vision
  • Face recognition
  • Emotion detection
  1. Chatbots & virtual assistance: chatbots, for example, are virtual assistants, helping users execute simple actions and retrieve information using natural language.
Technologies mostly used while developing chatbots:
  • NLP
  • Knowledge mapping
  • Intelligent searching
  • Translation
  1. AI and Machine Learning (ML) frameworks: developers use those frameworks to build new AI models without feeding them tons of big data. Thus, this type of AIaaS works great for smaller companies that don’t own big data.
  1. Fully managed ML services: this type is pretty much the same as ML frameworks but it doesn’t even require developers to build an AI model from scratch. That’s because it already includes pre-built models, custom templates, and code-free interfaces.

Benefits of AI as a Service

Considering all the above said about AIaaS, here’s a list of its benefits:

  • Opening opportunities to enhance security, predict and prevent accidents, managing risks with ease
  • Staying focused on core business (no need to become AI&ML experts)
  • Minimizing infrastructure costs
  • Making project cost flexible and transparent
  • Increasing the benefits of data insights
  • Improving business flexibility

Lessons learned

  1. AI as a service with its amazing capabilities can be a game-changer for many organizations.
  2. AIaaS provides an opportunity to use any available out-of-the-box AI solution and customize it for the specific business needs of an organization.
  3. It grows the organization’s ecosystem smoothly​ and reduces technology costs​.
  4. Each company is unique, and every business case is distinctive. AI as a Service is supposed to cover every situation, deriving more actionable insights and added values for the business.