What is Artificial Intelligence?
A theory and development of computer systems capable of performing tasks that are normally performed by humans, such as visual perception, speech recognition, decision-making, and language translation. Using PositiveEdge AI applications, it is possible to understand the underlying contexts and generate hypotheses, recommendations, and launch operations automatically.
About Machine Learning and Deep Learning
Machine Learning: A subset of AI and computer science where algorithmic models are trained to learn from existing data to make decisions or predictions. PositiveEdge is one of the best machine learning partner in India, USA and UAE. We implement RPA (Robotics Process Automation) using a strategic vision that focuses on high-level cognitive RPA powered by machine learning, artificial intelligence, and chatbots.
Deep Learning: A machine learning technique that uses layers of neural networks to process data and make decisions. Need more information on deep learning, contact us now.
What is Generative AI? Which Microsoft Partners in India are using Generative AI in business applications such as dynamics 365, power apps and microsoft power automate.
A type of AI technology that generates new written, visual, and audible content based on prompts or existing information.
Generative AI refers to a category of artificial intelligence systems that are designed to generate new content, often in the form of text, images, audio, or other types of data. These systems use various techniques, such as machine learning and neural networks, to learn patterns from existing data and then generate new, similar content.
PositiveEdge is a Microsoft gold partner in India. Through our technology advisory and consulting services, we drive innovation through research, design, customer experience, and prototyping.
PositiveEdge offers Microsoft Power platform consulting services & implementation in India, USA and UAE. Through collaboration, Power Apps and Power Virtual Agents allow you to innovate and accelerate delivery and implementation.
Our mission is to help leaders successfully apply AI to their organizations so that they can innovate, compete, and excel.
Pillars of AI Success
1. Business strategy
Clearly defined and prioritised business objectives, use cases and measurement of AI value
One of the biggest drivers of AI success is the degree to which the organisation has defined and prioritised business objectives, use cases and how it will measure value. This is particularly important given the wide applicability of AI to so many different needs, such as process optimisation, content generation, summarisation, procurement, supply-chain optimisation and more. Success requires rigorous focus on strategic goals as well as a growth mindset to embrace challenges and learn from failure.
“Rather than starting by asking what AI can do, we need to turn the telescope around and ask, ‘What are you trying to do in your business, and how can AI help?’” says Jason Price, Director of Specialist Management at Microsoft.
In fact, the Gartner® 2022 AI Use-Case ROI Survey states that the “main barriers preventing implementation of AI are unable/hard to measure value and lack of understanding AI benefits and uses.”
19% of respondents cited “unable/hard to measure the value”, and 19% cited “lack of understanding AI benefits and uses.”
In Quick Answer: What Is the True Return on AI Investment? Gartner stated: “Enterprises do not achieve maximum leverage from artificial intelligence investments, despite increased spending. Executive leaders must become keen and discerning creators of AI investment strategies in order to obtain optimum value from AI initiatives,” and that “the best return yield from AI investment will come from an extensive portfolio of AI, guided by an expansive and evolving investment thesis that is aligned to strategic priorities and helps to allocate resources based on business impact. Organisations that follow a portfolio management plan to determine most AI use cases are 2.4 times more likely to reach ‘mature’ levels of AI implementation. Being an AI implementation partner in Bangalore, India, PositiveEdge will help your organization maximize the benefits of AI through AI use cases in manufacturing and other industries.
2. Technology Strategy
An AI-ready application and data platform architecture, aligned parameters for build versus buy decisions and plans for where to host data and applications to optimise outcomes
The pace of AI innovation has captured the imaginations of people around the world. It has also intensified many of the biggest questions leaders face when seeking to optimise AI value, such as:
- Do I have the infrastructure required for AI applications to access data securely, quickly and at scale?
- Based on my top-priority use cases, should I buy, build or modernise AI applications?
- How should I determine whether to host data and AI applications on premises or in the cloud?
As you work to resolve these questions, the first step is to choose an application and data platform architecture that will meet your organisation’s requirements. Your architecture will determine the technologies you need, whether you buy a prebuilt solution, build it yourself or opt for a combination.
“You can’t democratise AI if you don’t have an architecture that connects everyone across the company,” says Andy Markus, Chief Data Officer at AT&T. “The cloud makes that possible.”
3. AI Strategy and Experience
A systematic, customer-centric approach to AI that includes applying the right model to the right use case and experience in building, testing and realising AI value across multiple business units, use cases and dimensions
Customer-centricity, and taking a systematic approach to AI, are both emerging as key contributors to AI success. The 2023 Gartner® report Survey Analysis: AI-First Strategy Leads to Increasing Returns found that “41% of mature AI organisations use customer success-related business metrics”, while “the strategic importance of AI techniques has once again been confirmed by our respondents. 77% of mature organisations adopt an AI-first strategy, systematically considering AI for every use case.”
Another critical driver of AI success is applying the right model to your use case – in other words, using the right tool for the right job – to solve specific problems and realise value. This applies whether you’re buying applications or building or your own.
Additional factors that tend to correlate with an organisation’s AI success include:
- The number of AI use cases deployed.
- The length of time they’ve been in use.
- The degree to which they have scaled across the business.
- The degree of value they have generated, measured by productivity, revenue or another metric.
These factors can also reveal potential barriers to success. One of the most common examples is the ‘perpetual proof of concept’ loop, which tends to point to gaps in alignment between projects and valued business outcomes. Finally, the degree to which AI is democratised throughout the organisation – extending AI capabilities and tools from a small group of experts to the organisation as a whole – is also a leading indicator of success.
AI, or Artificial Intelligence, has experienced significant success and advancements in various fields, transforming industries and impacting daily life. For more details, contact PositiveEdge now.
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