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And guess what? It's happening right now. So, join us on a journey as we unfold How Generative AI and Automation can revolutionize the future job market.

Back in the day, being a lift operator was a highly skilled job. Old elevators used a "shipper rope," and operators had to pull that rope to stop at a desired floor. because even when they stopped, the cabin still moved. It was all about experience – knowing exactly when to pull and when to ease off.

In the 1920s elevators went automated, giving passengers control of elevators, and operators shifted to jobs like customer service, advertising and many more, which were easier but more valuable for businesses.

Current Landscape of Generative AI and Automation

This shift represents the evolution of automation, as of now Generative AI and automation are sparking a positive transformation in the job market. By automating mundane and repetitive tasks, these technologies have liberated human workers from the drudgery of manual labor, allowing them to redirect their efforts toward more meaningful and strategic endeavors. This shift has unlocked unprecedented levels of productivity, efficiency, and creativity in various industries.

But here's the cool part – it's not a battle of humans versus robots. We're all on the same team. Humans bring the smarts, and AI's got the data skills, and when we team up, magic happens. It's like our problem-solving and innovation powers get an upgrade, and everyone's job satisfaction gets a major boost.

So, what do we do to keep up? Learning never stops. You've got upskilling, where you add new skills to your current job, and reskilling, which is like hitting the reset button to learn something new. These are keys to staying valuable in the job market.

Generative AI and automation are not just augmenting existing ones but giving birth to entirely new career avenues.

Existing job roles are benefiting from these technologies by becoming more efficient and focused. For example, in healthcare, they're giving a hand to doctors by helping with diagnoses, so they can focus more on giving personalized care. Over in finance, it helps to detect risks and fraud to make our transactions safer.

In the creative world, they are using generative AI to assist in generating articles, artwork, or music, allowing them to concentrate on higher-level creative aspects. Also, customer support professionals are leveraging chatbots powered by automation to streamline routine inquiries, enabling them to spend more time addressing complex customer issues.

Generative AI and Automation are making waves in industries like Banking, Fintech, Adtech, and Martech. They're shaking things up, making operations way snazzier. But we've got to be responsible with these tech marvels. We need solid privacy rules and ethical guidelines to keep things on the straight and narrow.

Also, schools are changing too. They're teaching students about AI and Automation so they're ready for future jobs. So, it's not just businesses; education is changing too, and it's all leading to a future where tech is a big part of everything we do.

In essence, generative AI and automation are enhancing the way we work and offering an exciting array of opportunities in both established and emerging job roles, shaping a more dynamic and adaptable job market. As these technologies evolve, they will continue to influence how we approach work, bringing forth an era where human creativity and innovation are amplified by the power of AI.

‘Genieaz’ represents the new breed of accurate & convenient Invoice and Financial document capturing software which easily connects to your existing accounting software and helps you even expand to automating your financial data capturing and processing workflows.

If you are new to understanding what OCR or optical character recognition is, let’s explore how it works.

OCR, which stands for optical character recognition. It is a technique for turning scanned images or documents, mobile uploaded invoices etc., into digital information. This means that it is used to scan your receipts, invoices, purchase orders, and other pertinent documents in the context of accounting.

Why 'Genieaz' is effective than the competitors out there?

Using Optical Character Recognition ‘Genieaz’ captures data from and then processes through AI and machine learning engine which provides accurate inputs compared to standard OCR-based data capturing software such as DEXT, which currently being used by accounting & bookkeeping professionals. This eliminates the cumbersome manual data entry in the accounting & bookkeeping process and helps you to work faster with a high level of accuracy.

It is understood that one of the biggest challenges for accountants is to keep up with the rapidly evolving technology. Even accounting software such as Quickbooks, XERO, Netsuite and many others has also evolved over the years, but it still requires manual entry of data up to a certain extent. This is mitigated by accountants by hiring many data entry workers or outsourcing to firms in offshore destinations.

While there are some data capturing programs, that can capture data from financial documents, these programs have various limitations and issues when it comes to;

• Training templates every time a new invoice or a document is submitted

• Poor customer service when it comes to training the data-capturing models.

• Discrepancies in pricing where users are overcharged and sometimes expensive pricing that limit affordability.

• Connecting with external business systems and co-existing with current accounting and bookkeeping ecosystems.

With an intelligent document processing solution dedicated for the accounting and bookkeeping industry ‘Genieaz’ users at accounting firms can now leverage the power of Artificial Intelligence and automation for low value-added tasks.

A real competitive advantage in using Invoice & financial document capturing solutions such as ‘Genieaz’ helps its OCR, AI and ML modules to free up accountants' and bookkeepers' time and allows their clients to get answers faster and ensure that everything is properly documented for accounting purposes in their own workflows.

How to request a Demo of 'Genieaz'?

Please connect with us by scheduling a demonstration
https://www.aizenit.com/genieaz/request-demo

Is it the reality?

As opposed to outsourcing manual entry with lesser levels of accuracy on your daily, repetitive duties, workflow automation solutions that can integrate with your existing environment of accounting software and tools can actually deliver efficiency, accuracy, and cost savings.

How can 'Genieaz' help you?

AI is transforming the way you have been doing your job by complementing with faster information capturing using AI-based tools such as ‘Genieaz’ a breakthrough product from AIZENIT. Compared to existing AI-based solutions ‘Genieaz’ provides a combination of Natural Language Processing (NLP), Machine Learning (ML) with the latest AI to capture data from invoices, receipts, pro forma invoices, bank statements, etc., and process individually or batch-wise to capture the data automatically and process to your favourite accounting software such as Xero, QuickBooks and many other.

How to view the 'Genieaz' demo?

If you are someone who is keen to learn how ‘Genieaz’ can help you to eliminate your manual data entry process from Purchase orders, Pro formas, Invoices, credit notes or receipts, etc., simply reach us via https://www.aizenit.com/genieaz/request-demo

#ai #intelligence #accounting #automation #Audit #OCR #ML #NLP #technology #Aizenit #Genieaz

You are part of an AI / ML Company, do you practice MLOps? It’s the next cool thing in the industry. How many dedicated MLOps engineers does your company have? I want to join your company as a DataOps Engineer! Do ML-Engineers and MLOps Engineers perform the same tasks? Is it just the DevOps team in an ML project?

These questions strike to me as seeing the industry needing more awareness regarding MLOps, and in general DevOps (Development Operations). In General, the computer science field is attributed with vague job titles, primarily carried away by the market trends and bandwagons. This is not all a negative aspect of the industry, rather a rapidly changing industry means new job opportunities are being created more frequently.

Before anything, I would like to brief you about other existing job titles common in an ML / AI engineering team but not limited to.

Data Engineers

Data engineers deal mostly with the infrastructure and architecture that stores and organizes data. They are strong coders who like learning and using modern technologies and enjoy discovering new ways to make software and systems more efficient, Because of this, many data engineers get their start in software engineering or business intelligence systems analytics roles that give them exposure to the systems and infrastructure that are crucial to the field of data science.

Data Scientist

The data scientist solves business problems by translating them into science problems which can be addressed using statistics and mathematics. Generally, will be good at ML Algorithms, Mathematics, Statistics, and Probability with programming skills.

ML Engineers

A machine learning engineer is someone who specifically implements a particular class of tools like deep learning neural networks. Good ML Engineers will be good at ML Algorithms, Data Structures and Algorithms, Object Oriented Programming, Software Architecture and Design principles.

Additionally, they carry out tasks like building inference systems, integrating ml systems with other products and adding security, performance, scalability, and maintainability aspects to the models created.

Again, I would like to stress the fact the titles and job descriptions can vary from one organization to other, depending on the industry being served, expertise, project attributes and other parameters.

A company building AI / ML-based automation systems for clients might have more responsibilities covered under “ML Engineer,” and at the same time a company using AI/ML as a tool to create business opportunities themselves will have more responsibilities under the role of a “Data Scientist.”

Let us bring DevOps into perspective:

DevOps

DevOps combines the concepts of development and operations to describe a collaborative approach to performing the tasks, usually associated with separate application development and IT operations teams. DevOps, in its broadest sense, is a philosophy that encourages improved communication and cooperation between these (and other) teams within an organization.

MLOps

MLOps focuses on streamlining the process of taking machine learning models to production and then maintaining and monitoring them. MLOps is a collaborative function, often comprising data scientists, DevOps engineers, and IT.

DevOps is a success story in the Software Engineering space. MLOps is an emerging trend in the ML / AI Space. Before speaking about MLOps let us take a page or two out of the DevOps success story.

The success of DevOps is due to cultural adoption within an organization. I have come across many examples where organizations without a single DevOps engineer are succeeding, whilst some organizations failed despite having multiple DevOps departments. The difficult part is inculcating a culture where people contribute, and the responsibilities are shared across the members. When an organization adopts DevOps as a mantra without realizing its cultural consequences, they might end up in an isolated set of small teams each working like a part of a massive production line. Keep in mind, Software is not a built-in factory.

The introduction of teams, processes, or anything else to an organization should not hinder the existing workflow, instead, it should add value to the organization, especially to the people of the organization. Anything that adds value to an ecosystem gets adopted automatically (Survival of the fittest, Darwinian evolutionary theory), eliminating the need for a technology/process evangelist.

It’s not just MLOps and DevOps

DataOps

DataOps is a collaborative data management practice focused on improving the communication, integration, and automation of data flows between data managers and data consumers across an organization.

Any system that works with data can adopt DataOps practices.

Wait! One more thing! What about CloudOps, InfraOps, GitOps, DataSecOps, DevSecOps, ChatOps, AIOps, RevOps, SalesOps, MarketingOps and NoOps?

Most individuals who emphasize any specific *Ops culture tend to miss the fact that there are much more Ops practices that exist, or in general miss the fact that any process automation and collaboration practice should not be viewed as a set of templated diagrams that can be adopted to increase efficiency. Whether it's Technology, Process, Practice, Products, Business or anything we consume or live with it, - it is all about people. Not job positions, roles, or title descriptions.

It's all about PEOPLE.

Back to the Questions

Is it just the DevOps team in an ML project? Do ML-Engineers and MLOps Engineers do the same tasks?

The answer is Yes and No.

Think of cross-functional teams inculcated with a culture where everyone works together to deliver great products. As an organization building ML AI Products you must have experts who can do development, machine learning, data science, cloud, security, Testing, Deployment, and Operations. that might include teams with people specializing in product, dev, test, ops, security, even marketing and sales to build a good *Ops culture.

However, it should not be taken vice versa. Just having an MLOps team does not automatically translate to having a good “MLOps practice” in place.

This does not mean I am discouraging the concept of “MLOps or DevOps Teams,” all the dedicated *Ops teams should not be there to take tasks and execute on behalf of the others, it should be a team which promotes enablement and help the organization with tooling, knowledge, and appropriate skills contributed towards the product or project teams.