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AI as a service: The Next Big Thing

AI as a service

Artificial intelligence is breaking new ground every day, and in the future, AI will enable improved customer experiences and business process optimisation. Business leaders undoubtedly want to exploit AI power to get a competitive edge because the AI revolution generates vast data.

Even though 90% of firms today have adopted cloud technology, just a third are seeing the return on investment they had hoped for. The most cutting-edge businesses recognise that although cloud computing equips you with cutting-edge computing capacity and gives you access to new types of data in the proper quantity and quality, AI is the link that allows you to turn that data into economic value. It comes as no surprise that the whole C-suite is suddenly interested in the AI agenda and wondering what comes next.

According to a Gartner survey, 70% of respondents desired AI to manage every duty, including problem-solving, research, computations, and procedure simplification. More than 57% of respondents to the survey said they wanted AI to execute one or two tasks, and 18% said they wanted it to conduct more than five.

What is AI as a service?

Artificial intelligence as a service (AIaaS) is a cloud-based service offering artificial intelligence (AI) outsourcing. AIaaS makes it possible for people and organisations to experiment with AI and even put it into production for large-scale use cases with little risk and without a significant initial outlay of cash. Along with (infrastructure as a service), it is a new “as a service” product. However, creating internal AI-based solutions is a complex process that can be expensive. As a result, companies are aggressively adopting AIaaS, a business model in which third parties sell ready-to-use AI services.

What are the Benefits of AIaaS

  1. Lesser need for sophisticated coding (tech) skills

On the one hand, there is a large need for AI professionals, and on the other hand, there is a shortage as well. In this situation, AIaaS can be quite helpful. If you add a layer of no-code infrastructure to the game, AIaaS can be used even if you do not have an AI coder on staff.

On that note, it is important to highlight that even while some AIaaS solutions do not require coding experience, working with legacy software presents various implementation challenges.

  1. Advanced infrastructure – and fast

Before AIaaS, successful AI and machine learning models needed powerful, quick GPUs to run. Using high-tech infrastructure is advantageous, particularly given that most SMEs lack the time and funding to build solutions from scratch. Nevertheless, your model can only successfully complete a task if it has been fed high-quality data. Due to AIaaS’s ability to be customised, it will be possible to develop a customised task-oriented model on top of the wealth of data that most organisations already possess.

  1. Cost-Effectiveness

Organisations no longer must spend money developing sophisticated infrastructure, parallel workstations, and incredibly powerful GPUs thanks to the assistance of a third-party vendor. Businesses may utilise the power of AI and machine learning technologies without investing a lot of resources at much lower rates.

AIaaS also eliminates supplemental costs like training and anything that encourages incomplete decision-making. With these advantages in mind, businesses can now concentrate on their core competencies rather than squandering time and resources on areas AIaaS can handle.

  1. Usability

AIaaS is user-friendly, quick, and simple to set up, and is available immediately out of the box, unlike putting up an internal AI system or other open-source AI solutions. As a result, developers will have more time to focus on designing and implementing the technology and other more crucial tasks. Organisations can easily leverage their power without substantial technical knowledge.

  1. Scalability

When deciding whether AIaaS is the best option for your needs, you can begin with smaller initiatives. AI solutions are the ideal service for a business if it needs to optimise but does not need a lot of processing power. It is ideal for jobs where there is little value addition to the job itself and some degree of cognitive judgement is required. Without procuring or relinquishing software and hardware tools, one can easily scale up or down.

Types

An organisation must first determine what it wants before launching into any AI services. It is advisable to look at pain points and solutions that allow easy integration.

Here are three of the most well-known AIaaS solutions offered today:

  1. Chatbots

Chatbots are the most popular type of AI solution these days. Regardless of whether you search the web for anything from government websites to shopping stores, you come across bots – particularly their most common type, i.e., chatbots.

Chatbots are software programs that imitate human-like conversations using AI algorithms. To simulate human talks, they employ natural language processing (NLP) techniques. These bots are typically employed in customer support and offer relevant responses to the most frequent questions asked by clients.

Bots are making waves in customer service right now. A lot of work needs to be put in by developers to make chatbots successful. They increase customer satisfaction by lowering the percentage of first-time responses. Additionally, automation allows commonplace chores to be avoided, freeing up agents’ valuable time so they can concentrate on more demanding jobs.

  1. APIs

Software programs can access AI capability through APIs that AIaaS solutions offer. APIs serve as a bridge, enabling communication between two software programs. Cognitive services like APIs enable developers to integrate a specific technology and AI service into their applications without starting from scratch.

Developers employ APIs for a variety of purposes, such as the natural language processing API, which enables human-to-machine interaction in their programs. Application programming interfaces can also carry out difficult but necessary tasks like speech recognition and sentiment analysis. Computer vision and conversational AI are only a few other uses for APIs. APIs come in a variety of options:

  • Natural language processing
  • Computer vision
  • Conversational AI
  • Translation
  • Search
  • Knowledge mapping

  1. Machine learning

Machine learning and AI frameworks are used by companies that require insights to analyse and find patterns within their data. These AI tools can make predictions that were not explicitly programmed into them. Machine learning frameworks enable such a level of data analysis with minimal to no human involvement.

Fully managed machine learning and deep learning frameworks are provided by AIaaS solutions presented in a platform as a service (PaaS) format, which offers a whole MLOps process. Developers can assemble a dataset, build a model, train and test it, and seamlessly deploy it to production on the service provider’s cloud servers.

NashTech’s Artificial Intelligence (AI) capabilities

We provide AI consulting services and solutions that will help you accomplish your business goals more quickly and position you for long-term growth.

  1. Generative AI 

We help companies reinvent their enterprise and optimise business functions for breakthrough innovation and competitive advantage.

  • Enterprise reinvention: With NashTech’s generative AI services encompassing strategy & roadmap, design & build, and operationalise & run, you can transform your business from the top down and reinvent every element of it.
  • Business optimisation: With the help of our prebuilt AI solutions for sales, marketing, customer service, finance, people, legal and more, you can optimise the operations of your business to innovate more quickly, increase productivity and cut costs.

  1. Data led transformation

Realising the enormous potential of data is possible, but it necessitates a change in how data and AI are used throughout the organisation. Data led transformation is about linking data and people, ideas, and outcomes, as opposed to previous data and analytics programs that were in a technical silo. We collaborate with you to uncover the value in your data so you can produce quantifiable outcomes in line with your corporate objectives.

  • Operational efficiencies: Increased responsiveness, agility and speed to value can be achieved by streamlining processes to lower capex and opex spending and providing the flexibility needed to accommodate shifting company objectives and requirements.
  • New products and services: Innovate your offerings, expand into new markets and change as you see fit with the knowledge that you can scale to meet shifting client needs and demands.

  1. Responsible AI

Responsible AI is the practise of designing, developing, and deploying AI to empower employees and organisations and have an equitable influence on consumers and society. This enables businesses to build trust and confidently scale AI. With responsible AI, you can define your governance plan, define important objectives, and build tools that will help AI and your company grow.

  • Ensure AI transparency: Develop clear, comprehensible AI that spans processes and functions to foster trust among staff and customers.
  • Protect the privacy and security of data: To ensure that sensitive or private information is never utilised unethically, prioritise privacy and security.

  1. Nash.AI

NashTech’s portfolio of AI products is intended to accelerate game-changing innovation three times faster than the traditional product lifecycle while also unlocking new efficiencies and growth. Our scalable, modular solutions have a fast time-to-market and a big business effect because they are built and delivered on the foundation of NashTech’s unmatched AI experience, data services, intellectual property and ecosystem partners. Powerful AI solutions that we develop and implement transform the way our clients work.

  • Speed to value: We design and deploy solutions swiftly using pre-built, pre-integrated machine learning (ML) and artificial intelligence (AI) models and real-time, sector-specific data sets. Knowing that responsible AI must be incorporated from the beginning, we scale quickly and confidently.
  • Rapid innovation: To accelerate client co-innovation, we develop and update each solution using exclusive NashTech IP. And before we scale, we experiment with AIP+ to immediately demonstrate value.
  • Client-first flexibility: Our solutions are AI-powered and created solely to benefit clients. According to the unique client requirements, they are therefore designed to integrate with almost any technology partner and be used through a variety of consumption models and hosting settings.
  • Shrinkage Control: Our Edge AI capabilities coupled with video monitoring and building machine learning models on already existing data have helped major clients lower their shrinkage by 25%.

Alliances & partners

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sap
databricks
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Conclusion

Adopting AI is gradually becoming essential for organisations that wish to develop and advance their businesses. Making this technology available, meanwhile, is no simple undertaking given how expensive it can be for smaller businesses lacking the resources and data storage.

It is good that artificial intelligence as a service exists so that businesses that want to experiment with AI may do so at a lower cost. Machine learning technology may be easily incorporated into business operations with AI as a service.

AIaaS enables businesses to use AI capabilities for business optimisation without locating seasoned personnel with substantial technical skills or constructing a sophisticated infrastructure. Positive returns on investment were given to those who had considered implementing AI technology sooner.

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