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With the AI revolution firmly under way, organizations across verticals recognize that their systems hold treasure troves of data that can be harnessed to achieve better insights, improve efficiency, and power the future of customer experience and business optimization. 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.
However, there is tremendous complexity involved in developing AI and machine learning solutions that meet a business’ actual needs. That is one of the main reasons that while the vast majority of enterprises understand the value of AI, according to Forbes only 14.6% of firms have deployed AI capabilities in production. This disparity between intention and actual implementation rates is evidence of the extreme complexity of AI projects. 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. For most organizations, especially those that have not originated and matured in the technology ecosystem, it is not feasible or cost-effective to achieve enough domain knowledge and data expertise to build solutions in-house.
The handbook on how to adopt AI is yet to be written. Productizing AI solutions requires companies to build complex, use-case driven systems that address unique business requirements and needs. Customizing and training the AI model is only one part of a complete puzzle. Adopting AI and improving its success rate demands an investment in many fundamental elements. Monitoring, compliance customization, hardware, business thresholds, measurements, validation, trust, regulation and feedback are only some of the factors that address the algorithmic components that take part in the AI solution. This profound complexity is the core reason that, as opposed to previous technological pivots of recent years, AI implementation in production is still at a stage where neither the build nor most buy options provide working solutions for companies seeking AI transformation.
Every AI problem, scenario and use case is completely unique, requiring minute customization. For most companies and use cases, this rules out most off the shelf verticalized or use case based solutions that lack the specificity required to achieve significant business and ROI impact in the long term.
Enterprises attempting to build AI solutions in-house often opt for establishing an AI center of excellence, amassing huge costs and an extremely long time to value on their AI projects, which, depending on scope, can take anywhere between one and three years to achieve — if success in production does indeed ensue.
This AI Catch-22 is at the core of the current massive failure rate for AI adoption. Although they vary across industries, adoption challenges and failures originate from business related issues, technical related issues, and their intersection. Among the main challenges are the ability to customize a solution for the specific environment, data, constraints and requirements; achieving stability and robustness; the operational effort, over time, in maintenance, updates, improvements and course-correction; and the adoption of new use cases towards a full AI transformation. The depth of these challenges is thoroughly demonstrated through Venturebeat’s research that a massive 87% of AI projects in enterprises never even reach the production stage.
Artificial intelligence as a service (AIaaS) refers to AI platforms that enable companies to implement and scale AI techniques at a fraction of the cost of a full, in-house AI. The “Service” in AIaaS refers not only to the delivery model – cloud-based software – but to the scope of the vendor’s involvement in the process. Tech companies offering AIaaS deliver the nine yards of AI as a unified platform, all the way from problem definition, keeping the model on the rails and expanding to new use cases, to including building the model, deploying the solution in production, and maintaining it in real world conditions.
AI as a service is an emerging AI adoption model that has its roots in the general Software as a Service, or SaaS, wave of recent years. The SaaS model began to gain traction as industries across verticals began shifting toward the web as the primary application delivery mechanism, initially for external customer-facing use, but increasingly for internal enterprise delivery. This period saw an increasing need for rapid delivery of software, including initial deployments as well as feature additions which could now be deployed onto a server with instant effect.
With applications rapidly shifting from single on-prem deployments to cloud-based solutions that enable constant development, it was only a matter of time until this model was adopted to also enable the deployment of AI solutions.
AIaaS enables companies to implement customized AI solutions on the one hand, with minimal investment in AI domain expertise on the other. AIaaS vendors understand vertical industries and build sophisticated models to address their unique use cases with remarkable efficiency. Since AIaaS solutions are cloud-based, providers are able to deliver them as a service that can be accessed, refined and expanded in ways that were impossible in the past.
The AI as a service model has been gaining momentum during the past year mainly because AI-based solutions can be used by companies across verticals and use cases. These solutions prove cost-effectiveness for businesses who are willing to invest in AI, since AIaaS providers maintain their infrastructure — while companies leverage services. The AIaaS model enables companies that were unable to embrace AI, due to a lack in either appropriate off-the-shelf solutions or in-house AI expertise, to adopt, deploy and run effective, customized solutions for their unique use case, data and business needs — with the fastest possible time to value.
The AI as a Service delivery model enables companies to implement and run advanced AI solutions at a fraction of the cost of building and maintaining their own model in production. AIaaS solutions also offer improved flexibility, usability and scalability. In a nutshell, the up and coming Artificial Intelligence as a Service model empowers organizations to implement highly customized state of the art AI solutions, while continuing to work on their core business, without turning valuable resources and attention to new and complex areas of development.
Here are some of the ways that these benefits come into play when implementing AIaaS solutions in the real world.
BeyondMinds has built the first enterprise AI platform that is universally applicable and easily adaptable for production environments. We deliver hyper-customized, production-ready AI solutions as a service, enabling enterprises to scale their AI transformation and rapidly create ROI from AI. While many other AI solutions can take months or years to successfully reach production, if ever, the BeyondMinds platform enables companies to achieve production-grade AI within weeks.
AI-as-a-Service (AIaaS). The BeyondMinds AI platform is provided as a service to ensure continuous improvement and increasing value over time. As a partner in production, BeyondMinds enables organizations to rapidly build, deploy, run and maintain their AI-based technologies. There are no data scientists required to build, train or maintain the model. BeyondMinds is dedicated to keeping the AI solutions on the rails, even with shifting data and requirements. Ongoing AI maintenance considerations and costs, which often burden AI projects post-implementation, are mitigated and streamlined. BeyondMinds works with organizations, aligning their AI needs, use cases and existing platforms around mutual data repositories and logics, so that AI modules are synergetic rather than siloed. This is a significant enabler of AI at scale and further future-proofs the organization.
Customized. As mentioned above, every AI use case is unique: even in the same vertical or operational area, every company uses specific data, requires unique goals and is driven by an idiosyncratic business logic. All these specifications are minutely built into every BeyondMinds solution, ensuring that it provides a perfect fit for data, usability, and business needs. The model is trained on the company’s data and customized for that data’s constraints and needs.
Production-grade. The BeyondMinds platform is designed as a robust, end-to-end state of the art AI platform, that goes far beyond a simple trained model. The platform includes innovative technologies, such as monitoring, security and compliance, smart feedback capabilities, data and model version control, explainability, and more. Included among these integrated technologies are robustness and stabilization tools that can leverage noisy, unstructured, or small data sets to meet the most demanding production requirements and extreme data scenarios.
The platform’s intuitive and user-friendly management interface offers a no-code experience so that a variety of stakeholders, not just data scientists, can deploy, manage, monitor, and maintain the AI solution. By empowering more employees to take control of AI with rich data visualization and straightforward, actionable insights, the platform helps to eliminate bottlenecks in the process so that greater value can be generated more quickly. Further contributing to the exponentially faster value creation, is the ability of the interface to support the simultaneous management of multiple use cases.
While the BeyondMinds AI platform helps enterprises solve specific and unique use cases, it also enables a company-wide AI transformation. Its modular technology stack supports scalability so enterprises can efficiently expand their AI goals and address each and every use case in the company on a single platform; and by using a single platform, enterprises are able to ensure continuity of data and processes throughout their organizations.
BeyondMinds’ mission is to create AI partnerships that enable the world’s most sophisticated companies to benefit quickly and enduringly from AI’s benefits, while freeing themselves of the risks and burdens of its development and ongoing maintenance.
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