10 Tools for Building Smarter AI-Driven Business Processes

AI-driven business processes have taken the world by storm — and it’s just the beginning. With every month, there is a fresh set of methods and AI frameworks that companies can leverage to a competitive advantage.

Organizations can no longer ignore artificial intelligence and hope it’s a passing phase. As soon as your competitor has incorporated AI integration tools into their workflows, you fall behind.

It’s not just about downloading the latest tech and hoping it will spark an uptick in revenue. You need to know what is out there and how best to use it.

That’s why, in this article, we’re going to cover 10 AI business tools that can help improve your performance and agility in constantly evolving markets. We’ll cover some everyday applications, such as natural language processing models and robotic process automation systems, as well as more niche features like business intelligence systems, AI app creators, and deep learning environments.

1. Machine learning platforms to build and deploy models

AI-driven business processes rely on a bedrock of data to inform them of what works and what doesn’t. This all comes from machine learning platforms — environments where you can build, train, and deploy models even if you don’t have a background in coding.

Machine learning platforms allow you to build a model with a specific task that is an advantage for your company. Templates can get you started, but as you fill your model with information about your business, parameters to work within, and objectives, it becomes better attuned to what you want it to do.

Over time, your machine learning platform will learn how to carry out the tasks you need faster and more accurately. Companies that jump on the AI bandwagon successfully can therefore develop products and services in a much shorter time frame, scaling up models from prototypes to full production on a user-friendly interface.

2. Natural language processing (NLP) for more fluid human-to-computer interaction

Natural language processing is the most widely used tool for AI-driven business processes, and many people don’t even know they’re using it. Text suggestions on email platforms and predictive text are great examples of how NLP has become part of our lives in the blink of an eye.

Now, the applications of natural language processing have spread across all areas of business, from brainstorming ideas and refining the wording of emails to taking up a role as an intelligent customer service chatbot or virtual assistant. Able to understand the syntax, and sentiment of messages, NLP apps can carry human-level conversations, freeing up your team for more strategic tasks.

In meetings, savvy attendees have started using NLP to transcribe and analyze large volumes of texts to get neat, organized summaries they can act on later. International teams are even using the same tools to automatically translate written speech into different languages to keep everyone in the loop with very little effort.

3. Robotic process automation (RPA) software to complete routine tasks more productively

Robotic process automation may have been around for quite a while now, but it is becoming increasingly powerful with every software update.

In its simplest form, RPA takes control of a repetitive, rule-based task to free up time from a human employee’s day. Typical actions include setting recurring meetings in a calendar, moving files based on tags and categories, and filling in forms. The benefits don’t just stop at giving your team a bit more time in their day to focus on other areas. It also reduces the human error that is more likely in repetitive work like data entry.

For more complex AI process automation, project teams use dependencies to link interrelated tasks together. As an example, when task A is finished, task B will be assigned automatically. Project managers can therefore concentrate on client management and address risks, rather than spending time manually assigning tasks to their team.

4. AI-optimized business intelligence systems to make smarter decisions more confidently

It’s no surprise that data analysis would make an appearance on this list of AI-driven business processes. Intelligent business tools learn from the past and provide insights into decisions made today. They remove a vast amount of decisions based on hunches and guesswork and instead offer deep insights into why things happen.

The ability of AI business tools to absorb and analyze huge data sets allows companies to cross-reference the factors behind good or bad performance and learn from it — without months of research from a data science team.

Data is fed into the systems in real time, meaning you can pull out informative, customized reports in easily digestible visualizations. To give an example, a record of each sale goes into a central database, complete with metrics about the time to sale and the customer journey. When you need to see where most of your traffic comes from, pull out a bar chart on the source of your clients and you’ve got your answer in a flash.

5. AI development frameworks to launch AI applications in a fraction of the time

Businesses from all sectors have begun creating new apps or adapting existing ones to implement AI-driven business processes. This sounds like a lot of work for an expert team, but in reality, it’s not that hard.

Rather than building apps from scratch, AI integration tools have allowed even small businesses to include features like NLP in their applications. There are comprehensive libraries of pre-built models that only need to be branded before being released to users. It is far more efficient than building AI apps from scratch, and the ready-to-use components are often better tested than an in-house product would be.

With data integration tools, you can be fully flexible in how you design the functionality and appearance of your app. Connected to your CRM, it links up to login portals and provides a copy of the user’s profile instantaneously.

6. Predictive analytics tools that compare and contrast potential outcomes

Finance teams are now relying heavily on AI-driven business processes to make accurate budgets and forecasts for the future. Clearly, they can’t predict the future, but they can do the next best thing.

By looking at historical data from a range of different perspectives, intelligent business tools can make accurate predictions about future events. Risk management is one of the most important elements here, with companies now able to identify and mitigate potential risks before they even occur. Artificial intelligence measures the probability of an event happening and how much it will affect progress, allowing decision-makers to be more confident in choosing a future pathway.

The next step is to run a side-by-side comparison. Using machine learning models, past customer data, and fluctuations in supply and demand, you can compare two pathways against each other, analyzing the pros and cons of each. This gives you an unprecedented insight into the future health of your company to optimize your business decisions.

7. Data integration platforms to combine information and better understand your business

Artificial intelligence isn’t just about analyzing data and getting insights from it. It even helps you gather the data in the first place. Set parameters in plain, human language, and data integration tools will bring it all together for you.

For example, you could ask your customer relationship management (CRM) system to pull year-to-date figures on deals made, and your finance software to show how much you spent on sales efforts over the same period. The result would be a custom analysis on your desk in a matter of seconds.

You don’t need to stop there. Every element of your business has an impact elsewhere, so integrating information from every department can provide critical insights into the causes and effects across your company. Whether you’re looking to save money or invest more in profitable areas, the first place to start is with a united platform revealing all the inner workings of the organization.

8. Deep learning environments for extracting patterns that humans can’t see

It’s a common pitfall to think that, with all our training, experience, and best practices, we have most, if not all, of the answers to hand. This is obviously not the case, as deep learning has shown across a range of industries. By studying massive amounts of data, deep learning models are now able to identify health issues such as diabetic retinopathy, predict floods, and even map the brain of a fly.

So what does this mean in a business context?

Deep learning AI frameworks can be put to work analyzing millions of marketing efforts toward a specific demographic and spotting the common factors for success. The more data fed into the model, the higher the level of accuracy, just like an image recognition tool.

Companies that spearhead deep learning environments are sure to be the first to uncover new supply chain processes, more effective sales pitches, and better-targeted marketing efforts.

9. AI-optimized project management workflows to optimize time, resources, and human intervention

Purpose-built AI workflows have revolutionized project management by optimizing everything from the time it takes to complete a task, the tools and resources needed, and the people in charge of executing it.

AI process automation draws on information about your employees such as which areas they excel in, as well as past performance data, to plan out an accurate schedule with no overlaps in schedule. If the best-placed person is unavailable to lead the task, the system will assign the next-best option, making these AI-driven business processes not just great time savers, but a reliable way of working.

As people and practices change, these smart project management tools will record shifts in productivity and flag anomalies to those in charge. Managers can respond with training sessions for less-productive individuals or capitalizing on smarter, more efficient methods.

10. Decision management systems that help you stay consistent and productive

An automated decision-making process is a game-changer for modern companies, allowing them to drastically cut down on meeting time and all the conflict and frustration that goes with it. Egos are no longer a problem if you can rely on accurate algorithms to give you roadmaps to follow that are in line with your company goals, culture, and profitability targets.

Feed AI-driven business processes with predictive analytics and organizational rules to set the groundwork, then choose the most beneficial pathway based on the results. In practice, this is already widely used in the financial sector when approving loans to clients. If the risk of investing in someone is too high, the model will try to prevent you from offering the loan and losing revenue.

Decision management systems are all about maintaining consistency. As humans, we’re easily swayed by a confident client or salesperson who can draw attention away from the facts and toward the emotions. Automating the decision-making processes makes it far more likely that the decision will be based on evidence rather than feeling.

Bitrix24: Transforming organizations with AI-driven business processes

Incorporating AI-driven business processes has decisively moved from “nice-to-have” to “must-have”. If you want to maintain a competitive edge, you need to make use of the speed, accuracy, and scalability that AI business tools can offer.

At Bitrix24, we’re well aware of the power of AI, which is why we’ve introduced it across our entire platform for improved productivity, efficiency, and decision-making in all business functions:

• A CRM that predicts lead scoring, forecasts sales, segments customers into groups, and analyzes customer data and campaigns.

• Email marketing automation and smart lead generation, social media management, and market analysis.

• Project management tools that accommodate AI resource allocation optimization, task prioritization, and risk assessment.

• Communication channels with intelligence inquiry routing and automated response generation in chat and email.

• CoPilot, our AI assistant, writes and refines copy; transcribes, summarizes, and translates phone and video calls; and generates task descriptions, summaries, and checklists.

And much more!

So if you’re looking for an AI solution that gets you the jump on your competitors, sign up for Bitrix24 today.

FAQs

What are the top AI tools for business processes?

The top AI tools for business processes include:

• Machine learning platforms

• Natural language processing tools

• Robotic process automation software

• Predictive analytics tools

• AI-optimized business intelligence systems

• Data integration platforms

• Deep learning environments

• AI-driven project management tools

• AI development frameworks

• Decision management systems

How can AI improve business efficiency?

AI improves business efficiency by automating repetitive tasks, reducing human errors, providing predictive insights, enhancing decision-making, optimizing resource allocation, and improving customer interactions. This all helps you to operate more efficiently, reduce costs, and respond faster to market changes.

What is the role of machine learning in business process automation?

Machine learning plays a crucial role in business process automation by analyzing data, spotting patterns, predicting outcomes, optimizing workflows, and automating decision-making. It is the driving force behind intelligent automation that is adaptable and improves continuously.