Almost every product or solution business has internal and customer-facing needs.

A few businesses can say they have an efficient support system or seamless internal processes that lessen workloads on the agent.

SMBs are challenged to meet customer demands with their existing model.

While their counterparts are using Artificial Intelligence to scale customer support capacity, SMBs' approach to transforming CX has a mixed outlook.

25% of survey respondents with Freshbooks say they are testing or using Generative AI to outpace competitors.

Google claimed in its report that more and more SMBs are leveraging Generative AI for their customer-facing needs.

Small and medium businesses agree that Generative AI can automate business operations, but they fear the high cost of in-house solutions.

Besides high costs, there are other types of challenges restricting you from building your Gen AI chatbots.

Here comes a no-code platform to help you level up your Generative AI dreams for customer support.

Workativ provides solutions that minimize your administrative burden, including bottom-line expenses.

By adopting a no-code platform from Workativ, SMBs can drive tangible value for their businesses.

Let’s learn how to build your Gen AI chatbots without all the hassles efficiently.

Workativ offers a seamless way to build your workflows with Generative AI properties and gain the ability to maximize your chatbot’s performance by enabling your people to find answers to common to complex questions and solve everyday work-related problems.

Here are the steps to launch your Generative AI chatbot.

• Sign up with Workativ

The first step to start with your Generative AI chatbot is to sign up. Provide correct details and get access to the backend of Workativ.

• Choose Knowledge AI

Inside the Workativ Assistant platform, look for Knowledge AI at the bottom of the left-side panel. Click Knowledge AI to get the power of Generative AI or LLM for your upcoming chatbot.

Knowledge AI is highly efficient in using NLP, NLU, and deep learning to provide accurate responses for NLP queries.

Knowledge AI can effectively self-learn and distinguish patterns for each query and accurately produce summarized or synthesized responses by giving access to your domain-specific knowledge bases or articles.

• Build your use cases

Decide which use cases are practical for employee support or organizational operational efficiency. For example, if you want to augment your existing IT support tasks, you can choose to do that.

There are many mundane IT support tasks, i.e., password reset, account unlock, user provision, etc.

Besides IT support, HR operations can be repetitive, including Finance, Legal, Marketing or Sales.

Pick any of the business functions, and note down your use cases.

• Create your knowledge articles or knowledge base

Workativ gives you extreme flexibility in saving time on dialog development.

Chatbot dialog is essential for effective communication or coordination to solve a problem.

Knowledge AI applies LLM power and creates human-like responses instantly without the need for you to anticipate scenarios and develop specific predefined dialogs.

Knowledge AI can understand the context and intent of a query sent by a user and surface responses with its built-in ability to craft engaging and new content.

All you need is provide enough resources to support specific use cases so that Knowledge AI can fetch the right intent and context from a massive pile of knowledge articles and surfaces the correct answers.

• Integrate knowledge bases or third-party apps

With Workativ Knowledge AI, your knowledge bases can be widely diverse. Give Knowledge AI access to internal KB or Workativ KB, your website, and external KB such as SharePoint, Notion, DropBox, Confluence, Slack, and other resource platforms.

Let us know more data you have for your Generative AI platform or LLM so you can produce more enriching and accurate responses in minimal time.

• Upload your files:

Regardless of the type of file format, Workativ allows you to upload any files. Pick Excel, docs, PDFs, images, videos, GIFs, or anything to upload to the Knowledge AI ecosystem and train your bot.

• Customize your workflows

Now enable workflow automation with respect to your use case. For example, if you want to provide solutions for printer jams, create your flows for this use case. As the flows go, things you can do for your users:

• You can give an answer

• Jump to another scenario, such as ask your user, ‘was this helpful.’

• Create a ticket for agent handover

Decide which scenarios may come up and anticipate the subsequential flow.

• Test and trial

Experiencing indifferent conversation behavior or prompts reflecting otherwise is nothing short of unusual. Say you have a flow for the next step, but your bot doesn’t recognize it and surfaces, ‘’sorry couldn’t find what you seek’’.

Find a TRY ME button at the bottom of the Workativ platform, and check how your flows work.

You must test conversation flows in a test environment, fix if there is any issue, and ensure everything works perfectly.

• Deploy Generative AI or Knowledge AI chatbot

One last notable thing about the Workativ Knowledge AI bot is that it allows you to tailor experiences specific to your brand’s persona. You set the look and feel just as your brand maintains everywhere to build a consistent user experience.

When everything looks alright, embed your bot in your preferred channels. You can integrate your Generative AI chatbot inside your Slack, MS Teams, and Website as a chat widget.

• Build a feedback loop

Let your users have a way to connect with you and share their experiences about the bot. Send a survey form and collect insights. This is a great way to work on gaps and provide high-quality chat services.

A no-code platform such as Workativ helps SMBs meet their expectations for Generative AI and gain a competitive advantage.

If you think in-house development is still the best choice, let’s help you uncover the truth.

Can we say implementing a Generative AI chatbot equals building a bridge - with massive manpower, engineering intellect, and days of testing and reworks until the final opening?

Indeed yes.

To be honest, for SMBs, this kind of iteration is too much to pick up.

There are a couple of factors to look at carefully before you jumpstart.

Let’s find this significant roadblock for SMBs.

Model deployment or setup cost:

At the end of each month, AI bills probably cause you strain.

Open-source models:

Fine-tuning with proprietary data is ideal for your complete customization needs for questions and answers for internal use cases.

However, it urges you to take full responsibility for on-premise or cloud infrastructure for computing costs. For example, platforms such as GPT-4.3 and GPT-Neo are suitable, but computing costs are additional.

If you look deeper, the costs to build your GenAI models for your chatbot could be extremely high because they incur additional expenses for components and manpower, including,

• Hardware costs for platforms such as GPUs or CPUs and AI accelerators

• Cloud computing costs for platforms such as GCP, AWS, Azure

• Maintenance costs for hardware

• Integration and deployment costs

• Developer costs

• Data preparation costs

This is so much more for any SMBs.

Let’s not forget the costs to add to your balance sheet for recurring maintenance and more infrastructure units as you scale.

Closed-source models:

It is pretty good to go with closed-source models, provided you no longer need to take responsibility for computing costs.

Commercially available platforms or closed source models such as ChatGPT and more advanced versions can incur costs per usage basis.

All vendors charge based on tokens and characters used in inputs and outputs. These charges also vary by vendors and the kind of capacity they provide.

For example, PaLM 2 for Chat costs $0.0005/1000 characters for inputs and outputs. GPT-4 costs $0.03/1000 tokens and $0.6/1000 for inputs and outputs.

On top of these monthly or yearly AI bills, if you rely on vendors' data systems for data storage, be ready to shell out more pennies.

User experience:

Are you getting complete flexibility or maximizing total value from your Generative AI chatbots or interfaces?

This is a significant question one can ask oneself so as to determine one's level of satisfaction.

Customization with open-source model:

You can fine-tune models with your data by accessing the underlying infrastructure of open-source models. However, an extended level of customization to enable more accurate answers is only possible if you have highly skilled in-house AI talent.

Closed-source model:

They are available as ‘as is’ on the market. You need to stay compliant with whatever little built-in customization is available. Although there are upgrades from the company side, you should be ready to compromise on limited customization capacity. As a result, your Generative AI chatbot can underperform if it needs to interact with unfamiliar queries.

Ethical considerations

Generative AI is known to hallucinate, misinform, or demonstrate biased behavior. This is closely intertwined with how you train your model with data.

It is significant to ensure an outstanding effort while preparing your data.

With an inexperienced AI team and no human in the loop, Generative AI solutions tend to exhibit unexpected behavior.

With so much to ponder about implementing your Generative AI chatbots or solutions, your strategy to take the Generative AI initiative ahead might not take flight.

To your surprise, a no-code platform offers a convenient way to build your custom Generative AI chatbots— without all these hassles.

The advantage of working with Workativ is that they prioritize security on top of everything and help you comply with industry security standards such as GDPR, HIPPA, etc.

However, model security always comes as a huge priority for everyone. It depends on what stringent policies you implement while developing your training documents.

If you want to enhance efficiency for everyday employee challenges for common questions, a Generative AI solution can effectively help you.

Instead of relying on an ‘as is’ or open-source model, a no-code platform conveniently lets you launch your chatbots with Generative AI properties.

The advantages of no-code Generative AI chatbot builder:

• Scale only with a small internal or in-house team to handle Generative AI chatbot functions

• Incur no extra costs for hardware, server, cloud infrastructure, and licenses

• Take no responsibility for maintenance works

• Get instant access to the knowledge base or knowledge articles to build for known scenarios

• Build fast and launch readily with your domain-specific knowledge bases

• Improve with inputs from your teams or internal users

Workativ, which shows competencies to help businesses gain exceptional abilities to enhance employee support with conversational AI solutions, gives SMBs a remarkable opportunity to build their Generative AI chatbots.

Workativ Assistant is a no-code platform with embedded Generative AI properties with which business leaders can instantly build their chatbot interfaces and gain natural human-like responses for simple to complex queries.

Benefits of working your way with Workativ:

• Help adopt a new product like Generative AI instantly

Workativ’s no-code platform is easy to use. Regardless of whether you possess a massive tech team, Workativ makes it easy to get started with your Generative AI chatbot initiative.

• Create a personalized experience for SMBs

Create tailored experiences with business use cases specific to your needs. As such, you need to turn your HR tasks into automated functions, Workativ can do this efficiently.

• Seamless post-launch support

Get 24/7 help from our sales support post-launch. To schedule a demo, connect with us today.

• Flexible pricing model

SMBs can choose the pay-as-you-go model. Workativ’s subscription plan can be customized based on your needs.

Contact sales if you want to learn more about your Generative AI options for your chatbot.

One thing we should take note of is that we are living in a tightening economy. SMBs struggle to save on their bottom-line expenses while also aiming to drive significant revenue growth.

Generative AI holds promising potential for everyone to drive operational efficiency and gain tangible business values.

Adding to this, McKinsey confirmed that Generative AI use cases provide massive potential to add about $4 trillion annual value to the global economy.

SMBs can drive efficiency by automating customer support tasks with Generative AI, which results in cost savings and revenue growth.

If you think Generative AI can’t fit your balance sheet budget right at this moment, a no-code platform can help you drive your Gen AI dreams.

To learn more about how you can implement your Generative chatbots cost-effectively, connect with Workativ. Schedule a demo today.