AI vs. Machine Learning: How They Impact Workflows and Automation

Between switching apps, scanning lengthy documents, and lack of clarity about where and how to find information, you can easily miss key details. In fact, a Gartner survey revealed that 36% of digital workers miss essential updates and 32% make incorrect decisions due to the number of apps they use and the volume of information available to them.

But with AI tools powered by machine learning, companies can use technology to centralize and manage information, making it easier for employees to find what they need when they need it. Let’s explore how machine learning and AI empower companies to make faster, more accurate decisions.

Understanding machine learning and AI

While people frequently mistake these terms for the same thing, artificial intelligence and machine learning are different. In short, machine learning is a type of AI. But it’s a bit more involved than that. Here’s a detailed breakdown:

What is artificial intelligence (AI)?

AI enables machines to learn from data, adapt to new situations, and perform tasks typically requiring human intelligence, such as processing language, recognizing images, and making decisions. AI is used across technologies, from rules-based chatbots that automate simple requests to autonomous AI agents that can perform tasks on your behalf.

AI includes machine learning (ML), natural language processing (NLP), and generative AI — a subset of ML.

What is machine learning (ML)?

Machine learning is a subset of AI that enables systems to learn and improve from data without being explicitly programmed. This lets them make predictions or classifications based on identified data patterns. For example, when a shopper gets product recommendations based on their purchase history, that’s ML at work.

Machine learning and AI in the workplace: enhancing productivity with Slack

To understand how AI is reshaping the workplace, consider its role in automation. Slack’s AI-powered Workflow Builder, for example, empowers employees to automate tasks and potentially reduce errors by suggesting or helping build workflows and offering smart features like auto-summarization.

According to a Slack Workforce Lab report, AI adoption is paying off, with about four out of five desk workers who use AI tools reporting higher productivity. They also report feeling more satisfied and engaged than employees who don’t use AI. A key reason for this is that AI tools help people do their jobs with less manual and mental effort.

Think about how often you need information to finish a task. You might need to check your email or CRM, or message a co-worker to find it. AI removes barriers, helping you find answers quickly and alleviate information overload with tools like AI summarization.

In Slack, you can type “Summarize today’s key points from the marketing channel” or “Find the Q1 sales report” and get instant answers. You can also use Slack AI to create a workflow for you to customize using a simple prompt.

Automating routine tasks with AI

The ability to automate routine tasks that eat up valuable time is one of the great benefits of AI. For example, teams can enhance efficiency by building workflows to automate tasks like meeting scheduling, note-taking, and knowledge sharing.

One Fortune 100 financial planning company transformed operations by centralizing its support processes in Slack. Now the team troubleshoots everything in one channel, creating a vast knowledge base accessible to all. Employees use a search-first approach before submitting a ticket, typically resulting in faster, more accurate answers that reduce resolution time, helping them get back to meaningful work faster.

Using ML for data analysis and insights

You have only so much time in the day, which is why instant access to accurate information is key. Using Slack as your work operating system helps teams access vital insights from company libraries, connected services, and conversations without leaving the place your team is already working.

The sales team at a scenario planning and analysis firm has boosted customer success with Slack. By asking Slack AI questions, they can surface insights scattered across unstructured data — from DMs to threads to connected apps. This helps the team pivot quickly if they get an alert about missing a sales forecast or if data reveals an emerging trend.

Key differences and similarities between AI and ML

Although AI and ML share similarities, they aren’t interchangeable terms. AI helps machines manage complex tasks requiring human intelligence, whereas ML teaches them how to learn and improve from data. AI-powered tools connect your teams, information, and apps to help you make data-driven decisions faster. In comparison, ML systems collect data and find patterns related to specific tasks or departments.

Here are some of the key differences between AI and ML:

• Generalization vs. specialization. An ML model trains for one task. If it analyzes sentiment in customer support chats, it can’t monitor for fraud without additional training. Conversely, AI can apply what it learns in customer service to create marketing content.
• Problem-solving methods. AI can tackle a problem in many ways, like a chatbot that escalates an issue to a human rep or an AI-powered supply chain tool that adapts to real-time delays. ML models aren’t out-of-the-box thinkers. They solve problems using pattern recognition and historical data.
• Scope of support. AI systems provide broad support for your entire organization, from entry-level employees learning the ropes to executives planning strategic initiatives. ML provides operational insights about your existing processes but isn’t as flexible or cross-functional as AI.

Implementation: How AI and ML solve problems

Developers use various machine learning and AI models in software. Here are three approaches to automating tasks and uncovering insights:

1. Rule-based AI. Known as symbolic or old-fashioned AI, it follows predefined rules using if-then instructions to automate repetitive tasks. This is common in IT ticketing systems and self-service chatbots. This model processes high request volumes but can’t handle intricate tasks or understand complex sentences.
2. Deep learning. A subset of machine learning, deep learning uses artificial neural networks trained to process data and recognize patterns in unstructured formats, like social media messages, images, and PDFs. Deep learning powers applications like image recognition and medical diagnostic tools.
3. Supervised learning. This is a training method for AI and ML models. The system learns through labeled datasets, like spam vs. non-spam email. Though widely used, it can’t spot hidden patterns and doesn’t work well with unstructured data, whereas unsupervised machine learning can handle both.

 

Real-world applications of AI and ML

As demand for AI tools surges among business leaders, it’s vital to understand how machine learning and AI can solve workplace challenges. A Slack State of Work report shows that workflow automation frees up around 19 full workdays per employee per year. New opportunities continue to emerge as companies learn how employees can collaborate with AI via autonomous agents. Here are some practical examples of AI and ML in different industries:

Revolutionizing patient care

According to an “AI in Health & Life Sciences” Forrester study, respondents overwhelmingly believe AI offers the opportunity to drive value in both patient-facing and operational capacities. While over 80% believe that organizations that effectively adopt AI will be more efficient and agile, the majority also anticipate that it will improve not just the patient experience (79%) but also patient outcomes (75%).

One example is HIPAA-compliant systems, which can help medical teams transfer sensitive patient information faster, ensuring that providers and patients get secure and timely results. Another is AI-connected systems that speed up progress and reduce error. One population-scale health technology company faced downtime when robots in its clinical-grade lab switched processes. Scientists had to monitor and connect systems manually. However, with Slack, scientists are now able to track progress remotely and receive error alerts in real time, helping them run more experiments at once, get results faster, and reprioritize workflows quickly.

Transforming customer experiences

Retailers can tailor in-store and online customer experiences using AI and ML-driven tools. For example, using centralized customer data and automated reminders in Slack, one family-owned fashion retailer was able to enhance customer experiences in stores and online. Integrations with tools like Salesforce and Tableau give associates immediate access to clients’ shopping histories, style preferences, and size details, so they can provide more personalized service, recommendations, and support.

Advanced search capabilities and a centralized knowledge base enabled a footwear retailer with distributed teams to successfully manage its wholesale, digital, and retail divisions. Customer support reps use Slack channels and AI-powered search to share resources and quickly surface customer conversations and other details. Along with higher customer satisfaction scores, the retailer also reduced its complaint resolution by 80% using Slack.

Data-driven innovation through machine learning and AI

Machine learning and AI technologies can increase productivity while helping teams solve crucial business challenges. With Slack’s AI tools and integrations like Agentforce, teams can automate routine tasks, find information faster, and collaborate with human and AI teammates — all through a secure, enterprise-grade work OS.

Discover how to use AI and ML to your advantage with the right tools. Contact our sales team to learn more.

FAQs

How do AI and ML differ in their learning capabilities?

Machine learning improves over time through data-driven pattern recognition, while artificial intelligence can use machine learning methods along with rule-based, reasoning, or self-improving approaches.

Can small businesses benefit from integrating AI and ML?

With AI as a strategic advisory tool and ML to continually assess and improve processes, small businesses can reduce operational costs while increasing revenue.

What are the ethical considerations in AI and ML?

Privacy and data protection are significant concerns when using AI and ML, making it vital for businesses to understand various learning models, training systems, and regulatory policies before choosing an AI solution or tools vendor.