Time has always been a precious commodity, and solutions that can cut the time we spend on manual tasks have always been seen as breakthroughs.

The intelligence of large language models (LLMs) and various other AI algorithms is reaching a significant level, and we can now confirm, based on experience, that AI can reliably automate many of the time-consuming tasks in the office.

In fact, there is no set-in-stone list of tasks that can be automated, and with the right prompts, you can set up automation wherever you see an opportunity within AI's capabilities, which leaves you with quite a lot of room for various automation.

According to a study by Accenture, companies that have deployed AI to assist employees in their daily tasks have reported an increase in productivity by up to 38%. This is no surprise since experienced employees are left with more room to creatively use their skills and focus on high-value work instead of manual and repetitive tasks that AI can easily handle.

While AI has generated a ton of hype, measuring its actual return on investment has proven difficult so far. The biggest impact seems to be making work easier and more streamlined, but workflow automation offers a clearer path to calculating ROI by slashing the time required for certain processes.

Let's explore in what areas AI assistance proved to be most useful and what tasks have already been successfully automated.

Customer Service and Support Automation

AI-powered chatbots and virtual assistants can handle common customer inquiries, troubleshoot issues, and even personalize responses based on customer data. These intelligent systems use natural language processing (NLP) and machine learning to understand customer queries and provide accurate responses from knowledge bases and CRM systems.

Platforms like Intercom, Zendesk, and ManyChat offer user-friendly interfaces for designing conversational flows, training chatbots, and integrating them with existing systems. For example, Intercom enables the creation of chatbots that handle queries across websites, mobile apps, and messaging platforms like Facebook Messenger and WhatsApp.

Customer service tasks that can be automated by AI include answering common questions through chatbots and virtual assistants, routing inquiries to the appropriate teams or agents based on context, analyzing customer sentiment to prioritize urgent cases, suggesting resolution steps by understanding the issue context and automating responses to frequently asked questions and requests.

Implementing AI-powered chatbots brings numerous benefits:

• 24/7 availability: Customers can get inquiries addressed promptly, even outside business hours.
• Improved response times: Chatbots can handle multiple conversations simultaneously, reducing wait times and improving satisfaction.
• Consistent experience: Unlike human agents, chatbots provide consistent and on-brand responses across all interactions.
• Cost savings: Automating common inquiries reduces the workload on human agents, leading to cost savings.
• Personalized support: By leveraging customer data, chatbots can personalize responses for a tailored experience.

For complex issues, chatbots can easily route conversations to human agents. But for common questions, AI is more than capable of presenting product specifications, explaining differences between similar models, telling which ones are on discount, or giving information about product availability and in which shops to find it (assuming that AI has access to such information).

Repetitive Data Entry and Processing

The jobs most affected by AI automation tools are those involving extensive data entry, document processing, and form handling. AI excels at such tasks because it can quickly process large volumes of information and accurately extract relevant data. Just give it proper instructions and see the magic happen.

Some of the specific tasks that can be automated include extracting data from emails, documents (invoices, receipts), web forms, and surveys, as well as populating this data into systems like CRM, accounting software, spreadsheets, and databases.

Just think about the manual effort previously required - employees looking over each email, invoice, and form and then copying relevant details into different systems and databases. With AI automation, a simple prompt or a specialized tool that has been trained for specific automation tasks can automatically extract and integrate data from these sources.

Take a look at AI-powered tools like Rossum, UiPath, and Zapier that can automate repetitive data entry and processing tasks. Rossum can extract data from emails and automatically populate CRM systems. UiPath can process invoices, receipts, and pretty much any other documents to extract relevant information and integrate it with analysis tools. Zapier connects various apps and services to automate workflows, such as populating Google Sheets from web forms or creating new records in a CRM system based on form submissions.

These are only some of the AI-powered tools that can automate certain tasks, and they themselves can also be integrated into a larger whole, like an enterprise search platform, and be part of chain automation that includes multiple apps and databases.

Assisting with Content Creation

AI has the capability to generate content like social media posts, product descriptions, email subject lines, and articles by leveraging natural language generation (NLG) models. These models can analyze provided data and generate human-like content tailored to specific requirements.

While this area of work will always require a degree of human assistance, at least in recognizing trends, suggesting appropriate tone, and similar tasks that require creative thinking, AI can still drastically cut the amount of manual work.

For example, an e-commerce business could provide AI with product details, target audience, and tone preferences, and the AI system could generate optimized product descriptions. Similarly, a marketing team could feed AI with campaign goals, target personas, and brand guidelines, and the AI could generate engaging social media posts and email subject lines.

To automate this, tools like Jasper.ai, Copy.ai, and Lately offer content automation capabilities. These platforms provide user-friendly interfaces where users input data and requirements, and the AI models generate relevant content based on the provided input.

Many tools offer templates for blog posts, social media captions, and code comments. Some specialize in marketing content like product descriptions and email subject lines, and they have also focused on automating social media content creation and scheduling.

Scheduling and Meeting Management

AI can make scheduling and meeting management processes much simpler by optimizing meeting schedules, sending invites, managing RSVPs, and transcribing meetings. 

Getting a group of people to the same place at the same time can sometimes be a real nightmare because there are times when everyone's schedule is packed.

AI analyzes calendars, availability, and preferences to intelligently suggest optimal meeting times and automate coordination.

For example, an AI scheduling assistant can analyze all attendees' calendars, including preferences and time zones, to find the best meeting time. It then sends invites, manages RSVPs, and sends reminders. During meetings, AI transcribes conversations in real-time to capture key points, action items, and decisions for easier review and follow-up.

Tools like x.ai, Calendly, and Otter.ai offer AI-powered solutions in this area. x.ai provides virtual assistants that can handle the entire scheduling process, while Calendly allows sharing availability for others to book meetings. Otter.ai specializes in AI-powered meeting transcriptions.

With these tools, you can eliminate significant effort in manual coordination and note taking, and easily organize meetings, knowing when other employees are available, when meeting rooms are available, as well as plenty of other handy features.

Reporting and Analytics

AI can automate a large part of the reporting and analytics process by gathering data from various sources, generating reports, identifying trends, and offering actionable insights and summaries. 

For example, an AI system can ingest data from multiple sources, such as sales records, customer surveys, social media mentions, and market research reports. It can then generate comprehensive reports that present the data clearly and concisely and highlight key trends, outliers, and potential areas of opportunity or concern.

Furthermore, AI can provide predictive analytics by analyzing historical data and identifying patterns that may indicate future trends or behaviors. This can help businesses make informed decisions about product development, marketing strategies, or resource allocation.

You can also set up custom automation that works best when you combine business data with generative AI. Simply feed the data in question to generative AI (this can best be done through an enterprise search platform) and prompt it to complete the task in mind.

Some tools offer AI-powered data exploration features that can automatically identify patterns, clusters, and outliers in the data. They also have natural language processing capabilities for generating insights and explanations. 

By automating reporting and analytics with AI, businesses can uncover hidden patterns and opportunities within their data, enabling more informed decision-making and a competitive advantage. AI's ability to handle large, complex datasets also allows for scalability in reporting and analytics efforts without compromising accuracy or efficiency.

Streamline Your Business with Custom and Chain Automation

Akooda's platform offers a comprehensive solution for chain automation across different tools by creating a unified database from all business data. 

Chain automation refers to the process of connecting multiple tools and systems to streamline workflows, ensuring that data flows seamlessly between them without manual intervention. This enables a more efficient and integrated approach to managing tasks and data across an organization.

Akooda integrates with various workplace tools and data sources, such as CRM systems, project management software, and internal databases. This integration allows Akooda to collect and normalize data from these disparate sources into a cohesive database.

Once data is integrated, Akooda's platform creates a unified data repository. This centralized database ensures that all relevant information is accessible in one place, making it easier to manage and analyze.

The unified data can be fed to large language models (LLMs) like ChatGPT or Gemini. These LLMs can process and analyze the data to generate summaries, insights, and predictive analytics. Employees can interact with these LLMs through natural language queries, asking specific questions and receiving comprehensive summaries and insights based on the integrated data​.

With the data integrated and accessible, employees can set up custom automation to handle repetitive tasks, such as data entry, reporting, and scheduling. The platform's predictive analytics capabilities allow businesses to identify trends, forecast outcomes, and make data-driven decisions.

Akooda not only integrates seamlessly with various tools that have AI automation features, but it also enables the creation of its own automation processes, facilitating chain automation across these tools. 

For example, consider a sales process where data from customer interactions in a CRM (like Salesforce) is automatically analyzed by Akooda to identify potential leads. This data is then fed into a marketing automation tool (like HubSpot) to trigger personalized email campaigns. Concurrently, project management updates (from Asana) ensure sales teams are aligned on lead follow-up strategies. 

Finally, Akooda aggregates insights from these activities, providing a comprehensive report on campaign effectiveness and sales performance. This demonstrates how multiple tools can be synchronized for ultimate workflow automatization.