Public Notice
Yes, the new world is here! Employees’ queries are no more a nightmare but can be handled by an AI-driven chatbot. Similarly, the voice-to-text option is changing the way people write their emails or even a small text. There are many such examples..
If these days, real wars are replaced by business competitions, you should not miss on your high-tech guns (AI, ML (Machine Learning), etc). Like all historic wars, one who will carry the latest weapon will win.
So, we suggest please do not ignore the trend. You will remember, we struggled with your first smartphone and similarly, we do not understand or it could be difficult to implement, but that doesn’t mean the world will wait for you to come to terms. We do have the answer for all this. Let us connect and work further.
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AI Strategy, Consulting, and Implementation
To foresee and solve your business difficulties, Salahkaar Consultants creates AI-powered products, develops machine learning algorithms, and implements predictive analytics.
Find AI Use-Cases That Fit Your Business’s Needs
With our AI consulting, you can empower your company now and apply Artificial Intelligence models tailored to your industry, whether it’s healthcare, advertising, financial markets, or any other niche. We bring your complete business pipeline to a point where intelligent technology improves the productivity of your main functions with AI implementation. AI’s main goal is to automate and simplify repetitive operations so that genuine and quick growth may occur.
Implementation of AI and Machine Learning
AI experience, technical services, and machine learning capabilities can help you improve your company infrastructure and agility. Salahkaar Consultants provides ready-to-use AI-powered services with intelligent features that help businesses achieve better results. We have a full spectrum of AI/ML services to build, deploy, and maintain AI-enabled products, thanks to our skilled AI developers and platforms. We can assist you to harness AI/ML algorithms to cater to your business needs, from retail to banking, education to healthcare, and IT. Allow your online and mobile applications to learn, improve, and adapt over time.
AI/ML algorithms will assist you in processing unused data, recognizing trends that would be invisible to a human, and then making decisions to achieve certain goals. Machine learning’s competitive advantage holds the key to propelling the company to new heights.
Our AI/ML consultants and developers have worked on a variety of projects:
- Machine Learning Consulting and modeling
- Natural Language Processing
- Predictive Modeling
- Image Processing
- Data Mining
- Data-driven IoT
- Cognitive modeling
Services for Artificial Development
Every project involving AI-based software applications necessitates an end-to-end requirement study with dedicated multidisciplinary teams. Salahkaar Consultants brings together emerging technologies and AI expertise to power digital workforces and intelligent processes, allowing businesses to improve their operational efficiency. Our solutions help our clients increase productivity by automating procedures and making them faster.
Why should you choose Salahkaar?
Next-Generation AI Solutions can help you transform your business.
We at Salahkaar Solutions will assist you in developing AI-based solutions as quickly as possible. We’ll collaborate with you to design the best tools for you, utilizing the most advanced frameworks. Our clever and powerful solutions include high-end Artificial Development services that you can effectively incorporate into your company processes.
Applied Methodologies
We become a beneficial partner in bringing extensive expertise in the field of artificial intelligence and machine learning thanks to our dedicated data science team. We can assist you in developing a practical framework to aid in the adoption of machine learning and other AI components.
Various Platforms
We have delivered AI chatbots and predictive analytic solutions in several frameworks, including (Watson, DialogFlow, and Azure), based on our experience. We have assisted our clients in developing business applications that enable informed and faster decision making, rapid anomaly detection, automation, and increased productivity using our solutions.
Data Engineering
Our data engineers are experts not only in transforming data into useful information but also in data mining. Our strategy entails the following:
- Identifying the data sources that are required.
- Cleaning and processing of data
- Assisting in the development of tools for measuring, analyzing, and reacting.
- Use what you’ve learned to make changes to your current workflow.
You can alter your business today with breakthrough AI solutions from Salahkaar Consultants. We believe in providing top-of-the-line performance by providing AI/ML services that increase corporate efficiency. Our AI engineers have the potential to completely transform the way businesses function. You can uncover new values with Machine Learning and customized web applications with our AI-infused apps for a smarter and more intelligent business-specific solution that will help you become more proficient in responding to customer needs. Our AI professionals are well-versed in a variety of Artificial Intelligence-related technologies that are critical to advancing machine intelligence.
We focus on emerging technologies to create simple and user-friendly solutions for complicated challenges while staying current and delivering real-time business advantages.
Our Approach To Resolving Your Business Issue
Our in-house AI developers, data scientists, engineers, and analysts are ready to tackle today’s artificial intelligence difficulties to generate accurate and practical commercial results.
Analyzing
Analyzing and suggesting the model type, tools, technologies, and architecture based on your present capabilities.
In this phase, we focus on
-Information retrieval
-Knowledge representation
Concept
Creating and testing a small-scale model to demonstrate the machine learning model’s viability.
In this phase, we focus on
-Validating the model
– Defining the process and technique
Refinement
Refining the model to improve its quality and stay up with the ever-changing business model.
In this phase, we focus on
-Refinement
– Performance Analysis
Deployment
Integrating the machine learning system into your current business model while keeping costs and implementation in mind.
In this phase, we focus on
-Behavior Modeling
-Testing
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Check our blog hereunder for more understanding. This is written by our associate, Sanya Tyagi
Artificial intelligence (AI) and machine learning are rapidly gaining traction among consumers, becoming practically ubiquitous in our daily lives. Amazon has sold 10 million Echo smart speakers in the last three years, and Apple’s new iPhone X contains an AI chip dedicated to functions like facial recognition and processing spoke instructions. Despite this, AI isn’t nearly as widely used for automating and improving commercial processes. Although an Oxford research claims that AI would automate more than half of all occupations in the next two decades, many CEOs, may be influenced by media hype and disinformation, are skeptical of the technology’s potential impact.
We present a practical, fact-based assessment of AI trends and ramifications for enterprise automation over the next five to seven years in this article. We highlight four significant AI facts, analyze the ramifications, and suggest ideas on how to get started based on our work with a wide range of enterprises and interactions with academia, start-ups, and enterprise users.
1: The AI Revolution Is Here to Stay and Shouldn’t Be Ignored
Machine-learning algorithms have beaten humans for the first time in tasks like picture recognition and voice-to-text translation, as well as sophisticated games like Go. A convergence of three factors has fuelled the AI boom: a breakthrough in deep-learning algorithms, the proliferation of big data (structured data) to train these algorithms, and an exponential increase in processing power for machine-learning hardware, such as graphics processing unit (GPU) chipsets, which have reduced a machine’s training time from months to days and hours.
All three of these factors are projected to continue to accelerate. In less than 3 years from now, about 70% of all company data will be stored and processed in cloud-based data centers, resulting in unparalleled big data infrastructure for training machine-learning algorithms. Hardware processors that can accelerate algorithm training are also rapidly improving. Next-generation GPU chip hardware has been disclosed by Google, NVIDIA, and Intel, which will speed up training by 10 to 100 times. Advances in the underlying machine learning algorithms will continue to accelerate, based on the growing number of patent applications. These trends point to the fact that AI will continue to advance. This means that ignoring AI is no longer an option for corporate leaders.
2: AI is being used in a variety of organizations, but only to a limited extent.
So, in the next five to seven years, what will AI be able to achieve for enterprise automation? According to the majority of experts, businesses will adopt narrow AI, or supervised machine learning that is focused on a single activity. AI algorithms will be able to learn how to automate a task using training data, but once the task is learned, the solution will be limited, and the machine will not be able to generalise that learning to execute other tasks in most circumstances. Widespread use of wide, human-like general intelligence—that is, intelligence that is unsupervised and context-aware—could be beneficial.
Consider a corporation that needs an AI system to scan PDFs and handwritten invoices, evaluate field formatting, and then activate the accounts payable process automatically. The approach might also function with unlabelled data, such as gathering historical bills without having fields labeled as valid or invalid, using more powerful algorithms or unsupervised learning. This company’s AI solution, on the other hand, is confined to automating text-field recognition and formatting. To employ AI for more advanced invoicing functions, such as fraud detection, the organisation would have to create and train an entirely new solution based on other underlying traits and patterns. In the near future, AI applications will follow a narrow paradigm of supervised machine learning using training data. The acquisition of labeled data for training becomes a strategic competence and a source of distinction, and AI solutions necessitate deep functional and domain-specific human co-creation and process innovation.
3: AI Is Ready to Be Used in Specific Tasks
Between 2000 and 2016, Goldman Sachs employed machine learning to transform its 600-person trader unit into a much leaner 200-person team, while Fukoko, a Japanese insurer, expects to use AI to replace more than two dozen human agents who handle claims. However, given today’s restricted paradigm, not all organisational functions are suited for AI automation. A–B activities, defined by computer scientist Andrew Ng as processes that accept a set of clear inputs and provide a response, are one method to characterise machine-learnable tasks. Retail demand forecasting, for example, is an A–B activity.
Another notable example is financial trading. To assess if the best alternative is to buy or sell, an algorithm can analyse historical prices, macro trend drivers, and arbitrage rules accumulated from previous traders. Although finding the best decision based solely on these inputs may be difficult given the market’s unpredictability, an AI solution remains appealing if it can outperform humans over a large number of trades.
Managers can identify chances to employ AI automation and augmentation solutions by categorizing corporate processes and activities into A–B versus non-A–B categories.
4: It’s About More Than Technical Feasibility When It Comes to AI Adoption
Even though the technical requirements are similar, certain AI applications will be embraced faster than others. Broader solutions can help a company’s AI projects capture value in the short term while also laying the groundwork for long-term goals.
When selecting where to apply AI for enterprise automation, keep the following in mind:
- Costs that are only incurred once.
Consider the first investment in a new AI solution, such as designing an algorithm and obtaining training data. Algorithms are open-source, and “AI as a service” is available on a pay-as-you-go basis. Platforms can help with fixed costs, but access to training data can be a costly bottleneck or a potent source of difference.
- The cost of switching.
Examine the costs of replacing an existing solution with an AI solution, taking into account technological challenges such as the capacity to open the AI algorithm’s black box to track and explain decisions, as well as human challenges such as political and cultural reluctance to change.
- Needs of the ecosystem.
Determine whether any supplementary technologies are required for an integrated solution. An AI system that must be combined with cutting-edge IoT sensors and upcoming robotics technologies, for example, will be more difficult to implement.
- Hurdles posed by the system’s externality.
Consider how the AI solution might influence third parties that don’t want to use it, keeping in mind that the solution’s value will rise as more people use it.
When it comes to implementing AI solutions, businesses confront a number of obstacles, as well as varying deadlines for making a significant effect. For instance, one call center with whom we collaborated was evaluating a technology that would turn handwritten forms into structured database entries. Because the solution could be implemented using off-the-shelf machine-learning modules, the software development and switching costs were low. Furthermore, the organization had a considerable amount of labeled data for training, as well as optical character recognition scanners to process the data and extensive databases. Proof-of-concept solutions in specific functions and geographic units might serve as training data and points of reference for other units, and the solution faced relatively low externality hurdles. By 2021, the company intends to have a large number of people using the product. Another approach would improve agent interactions at the call center by automating sentiment analysis from customer audio conversations or chats. In terms of cultural and risk barriers, this approach has substantially greater switching costs. To avoid any negative influence on its clients, the company chose to start small.
If the approach works, the organization will have to rethink its entire training process to allow the AI engine to recommend solutions to agents. Higher adoption will generate more training data to improve performance, but collecting the initial critical mass of training data will take time and need company leaders to take a leap of faith. Given these complexities, an AI sentiment analysis bot will most likely take seven to ten years to implement, rather than the two to three years that the original solution took.
Using an AI Road Map to Help You Find Your Way
AI and machine learning will continue to grow in popularity as more sophisticated algorithms, more data, and more capable technology become available. Forward-thinking businesses, on the other hand, evaluate prospects with rigor. Based on the aforementioned developments and their ramifications, firms can take the following four stages to implement a comprehensive AI automation strategy:
Mapping the A–B activities.
Rather than relying on one-off AI solutions, consistently identify and map all AB operations along the value chain to see whether they can be automated using machine intelligence. This guarantees that downstream concerns like value potential, time to impact, development costs, and alignment with the broader business plan are all taken into account.
Determine where AI will be utilized first.
Because AI solutions are not generalizable and require specialized data and training, prioritize the AB operations to be automated based on the potential business benefit and adoption complexity to ensure efficient resource allocation.
Adopt a portfolio strategy.
Manage the AI automation pipeline as if it were a product innovation portfolio to maximize short- and long-term value creation while minimizing risks. Segment the projects based on the facts provided and the risks of adoption.
Consider all of your alternatives for making, buying, or partnering.
Multidisciplinary competencies and insights are frequently required while using AI. Utilize internal specialists, consultants, and corporate partners, as well as AI technology start-ups, to implement best-of-breed solutions and create quick proofs of concepts that can be delivered in weeks rather than years. Have a clear overarching acquisition strategy for areas where AI has disrupting potential to prevent costly rushed bets or late entrants in a context of record-high valuations.
To stay relevant, forward-thinking corporate executives must develop machine-learning capabilities, data, and partnerships using a systematic, portfolio-based approach.
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