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The emergence of Generative AI pushes for rapid adoption of AI like no other technological innovations in recent times.
Everyone wants to use Generative AI for its reasoning, logic, and problem-solving skills independent of human oversight.
The wave of opportunities Generative AI brings is clear evidence that AI or GenAI would take very little time to become mainstream.
Companies want to train or build proprietary models for various business use cases.
Goldman Sachs predicts that Generative AI brings up huge economic potential by boosting labor productivity, which is expected to be raised by 1 percentage point annually.
That’s probably a growth driver for AI. After the release of ChatGPT, interest in AI has shot up. With that, Goldman Sach's research suggests that AI investment will likely grow by $100 billion in the U.S. and $200 billion Globally by 2025.
It is evident that companies are eager to implement their own GenAI or AI models—irrespective of their desired business objectives, whether they want to boost employee support or customer interactions, or it can be anything.
However, the agony is not all AI projects can successfully meet their fate. The reason is finding the right AI vendor is tough.
With so many AI vendors with lots of hype around their specializations, it takes time to make the right decision. One wrong step could incur losses of millions of dollars for a wrong-implemented AI project and prevent you from trying out a new opportunity.
Before having such a sour experience, let’s get a clear vision of how to get the right AI solution built up from your AI vendor.
As a business leader, digital transformation driven by Generative AI solutions or AI models is always desirable.
However, choosing the right AI vendor for your enterprise must involve evaluating the project's comprehensive sides and ensuring that the vendor aligns with overall business objectives.
Let’s know them.
Your business can have a significant thought process about a new project. Before you search for an AI vendor, make sure you have business-level considerations.
Businesses need 100% buy-in from senior management so as to go ahead with the AI project or anything for digital transformation successfully. If you can have your senior management on the same board, it can significantly reduce disagreement regarding any decisions taken. Also, with senior management buy-in, it is easy for a business to conclude on a project scope and facilitate financial support.
With Generative AI generating immense possibilities for various areas of business processes, such as automating dunning management for late payments, streamlining ITSM, augmenting agent efficiency for employee support, or improving enterprise search, you must identify the area where you want to apply the power of Generative AI and boost productivity and efficiency.
As you can gain so much with your AI model, it is critical that you have a clear picture of what workflows you want to automate for some specific business functions.
Know that the ‘one-size-fits-all’ can never align with your business objective. If an AI vendor specializes in building workflows for B2B digital payment processes for credit purchases, the workflows won’t fit your perspective if you want to automate HR operations.
If HR workflow automation is your key objective, it is critical to note down KPIs for each process.
For example, your KPIs can include onboarding workflows, leave request management, PTO inquiries, tax issues, etc. Having these KPIs ready helps you evaluate the success of AI implementation and vendor engagement.
The surge of Generative AI applications for repetitive processes results in business leaders adopting AI solutions at scale. This is a golden moment for vendors to apply Generative AI to their legacy products or rebrand them to drive maximum business profits.
This is essentially important for businesses to understand if a vendor has just emerged as an AI player or is an existing player, among many other critical things.
AI is a tough discipline. What seems easy to AI experts may seem perplexing to business executives because they possess expertise in business statistics, not complex technical language.
Things can be misleading if executives aren’t familiar with machine learning or NLP terminologies, such as hyperparameterization, algorithms, epochs, etc.
Simultaneously, various ML platforms have their specific language for features or processes, whereas they also consist of familiar language or concepts.
Business leaders struggle to find a common language to connect and feel comfortable.
This is where businesses need to bridge the gap between AI experts and business executives by simplifying complex AI language.
However, finding an interpreter who can help address this challenge is painful.
Also, this is an additional investment burden on top of your overall project spend.
It is crucial that your AI vendor keeps things simple for you or uses simple language so that you can understand and clear doubts.
Paying attention to simplifying complex language is also essential to having fewer errors and faster implementation.
The sudden influx of ChatGPT-like technology or Generative has companies emerging as AI vendors.
The list includes many non-AI companies. Surprisingly, database companies and even finance leaders consider themselves AI specialists.
This is a serious area to consider as you must ensure the expertise you need aligns with your business objectives.
From that perspective, real-world experience with AI project implementation is essential.
Things you need to ensure before collaborating are to—
With all these insights in your kitty, you can optimize your time and better understand who to choose.
Legacy or clunky architecture is less flexible to scale with evolving needs.
Also, as your needs change, your computing power changes.
Understand how on-premise architecture and private or public clouds can add to your computing billing.
The best idea is to look for a portable cloud, either with an independent cloud architecture or a combination of two—cloud and on-prem.
Let’s also take into consideration that if you change the number of use cases as your need evolves, your AI platform must support scalability. Let’s also take into consideration that if you change the number of use cases as your need evolves, your AI platform must support scalability.
Choose a platform that scales and supports as many use cases as you want to implement without the heavy lifting.
On top of that, choosing a platform that requires no tough learning curve to deploy and manage is always better.
You guess it right. We are talking about a no-code AI platform for AI solutions to streamline your business processes.
AI is hard. It is a simple theory. Working with new technology and workflows can be confusing and frustrating due to several glitches.
Maybe initially, your AI solutions can fail if bugs are persistent. Ensure your AI providers help you fix bug issues without doing this alone.
The other way is that AI solutions can degrade with time. It is essential to look into the details so your AI partner can support you even after months and help you improve underperforming models.
Not every business can hinge upon typical AI solutions. Your business needs unique solutions that typical AI solutions cannot meet.
The concern is that AI startups can tell you that their product can easily solve your problem.
Seek an AI vendor that can adjust to your problem and create custom solutions that suit your business objectives.
Aside from that, acquisition or emerging needs on your business front can sometimes force you to transition to a flexible platform.
Ensure you avoid falling prey to vendor lock-in, which can either ask for extra fees to move out of the present platform or cause you to lose control of your data in the existing platform.
Partner with an AI vendor committed to your success and helping you transition seamlessly.
Your AI solution can deliver outcomes only when it meets some of the essential technical requirements for your internal business processes. Let’s know them.
The AI solutions or workflows you want to implement for various business use cases must talk to your existing tech stack, build a unified connection, and offer seamless problem-solving flexibility.
For example, to streamline HR workflows, an AI platform must integrate with existing HR platforms, such as BambooHR, SAP SuccessFactors, ServiceNow, etc, to help build convenient workflows accessible via familiar communication platforms like MS Teams, Slack, etc.
Check with your AI vendor to see if they can enable you to create a flexible integrations with your existing tech stacks, handle configurations, and help you get started.
AI model training can include several factors, including data source, training responsibility, data authenticity, testing, etc.
Ask your vendor if it is your responsibility to train AI models. If it is your part of the job, you must build an engineering process with significant investment in human capital.
In addition to DIY model training, it can also involve additional work for your people if your vendor uses multiple application packages to build an AI model.
Ensure that the testing is part of your vendor’s job. On the other hand, data validation is a collaborative effort.
Generative AI and its related disciplines are known for their nuance behavior. They can hallucinate or demonstrate unbiased phenomena if data is scarce or inappropriate.
You must take care of the ethical side of your AI model development.
An AI model built on a transparent policy helps create a safe and secure environment for everyone.
Ask your AI vendor what methods they use to protect your data.
Data leakage of company policies, contract agreements, or details on new projects can be a treasure trove for scammers to unleash reputational threats and incur financial risks for your business.
Before you start, ensure your AI project is end-to-end encrypted, multi-factor authentication is implemented, etc.
An AI vendor that ensures data governance and compliance with local and international regulation systems can provide complete data protection.
Ensure that your AI vendor offers compliance with many regulatory systems, such as GDPR and other compliance standards.
What’s the return on your investment in AI solutions?
Ask your vendor this essential question.
By the time you launch your solution, your competitor will likely build a positive impact and drive significant growth.
Ensure you can have your project launched in the expected time.
Besides, what are the quantified and unquantified benefits for your business?
Whereas quantified advantages are concerned, check what percentage of repetitive work you can reduce and optimize productivity.
On the unquantified benefit side, ensure you can build user experience and improve efficiency.
If your vendor can help you gain these benefits, your AI will drive meaningful ROI for your business.
At Workativ, we are committed to helping you solve your business problems most effectively and effortlessly.
Although our no-code platform is easy to use and fast to train, we help you end-to-end, from model training to deployment.
Whether you need technical support or seamless customer onboarding, you can swear by our support.
We can guarantee the high performance of your AI models with our microcontainer deployment models.
We use microcontainer deployment models to provide computing flexibility while ensuring the high performance of the AI models you seek from a containerized and virtualized architecture.
On our security, we offer top-notch security assurance with multifactor authentication, SSH authentication, and SSL encryption.
Over the years, we have garnered proven track record of helping enterprises with their unique IT problems using our conversational AI chatbots, which now encompass the power of Generative AI to augment the existing level of automation and boost performance.
AI is definitely tough. But we make it easy to understand AI better and help you leverage it easily without confusion.
To start your AI project, refer to these essential tips.
If you want to learn about us and implement your AI solutions, talk to us today.