What Are the Advantages and Disadvantages of AI?

With the rapid advancement of artificial intelligence, it has become embedded in daily life, from connected cars and smart home appliances to intelligent virtual agents that enhance customer support and help teams create effective sales pitches. As artificial intelligence continues to mature and its business applications continue to expand, more and more companies are embracing it. The promise of artificial intelligence (AI) is great, but its use has also raised ethical and environmental concerns. How can you reap the benefits of AI while avoiding the potential pitfalls of the technology? Let’s look at the advantages and disadvantages of AI - the benefits it offers across industries and the challenges it poses.

• What is AI?
• What are the advantages of AI?
• What are the disadvantages of AI?
• AI is here to stay
• Frequently Asked Questions (FAQs)

AI is a set of technologies that lets machines simulate human intelligence, allowing them to learn, reason and solve problems. AI systems offer a range of capabilities: They can be trained to understand complex and nuanced language, create new content based on existing data and analyse data for patterns in order to predict future behaviours and events. In these cases, human input is required, whether to train a large language model (LLM), a type of AI model that can generate human-like responses by processing natural-language inputs; write a prompt for a content creation tool powered by generative AI; or act on a sales forecast produced by predictive AI.

But some AI systems can operate independently — without direct human supervision. These autonomous AI agents can reason, plan and take action to execute specialised tasks or achieve specific goals and they can multitask, too. Best of all, they can adjust their actions based on new inputs, and they can learn from experience.

AI offers numerous advantages for teams such as marketing, sales, customer service, IT and operations. Here are some of the benefits of AI.

AI can streamline essential but repetitive manual tasks — like creating appointment reminders, sending emails and drafting sales quotes — which frees up teams to focus on more high-value work. Sales reps can ask assistive AI agents to present the top five sales leads and provide supporting details. But autonomous AI agents can do even more.
For example, they can proactively draft personalised emails for each prospect, using data from past interactions. Once a lead has been contacted, an autonomous agent can summarise the call and recommend next steps.

All this is great, but streamlined workflows aren’t helpful if they produce errors. Fortunately, AI can help to minimise human errors in basic tasks such as data entry and complicated ones like data analysis.

AI offers many benefits for commerce and customer service teams. For example, commerce teams can use AI agents to create personalised interactions and tailored product recommendations, based on a customer’s past purchases and browsing history. Service teams can resolve cases faster by using agents to provide personalised support 24/7 for complicated issues and by deploying AI-driven help centres that let customers find answers to common questions.

One of the greatest benefits of AI is that it helps you to make data-driven decisions. The process of grounding, which anchors AI systems in real-world data, lets AI analyse large volumes of data and quickly identify patterns and trends. By delving into data sources such as social media, customer feedback and sales records, AI tools can uncover insights and provide recommendations to help you improve your business.

While companies have long been able to access structured data — data that’s organised and formatted, usually in a spreadsheet or a relational database — valuable information also exists in unstructured data such as PDFs, emails, and audio, video, and image files. When you use a specialised vector database that shops, unifies, and indexes unstructured data and combines it with structured data, you get access to more complete customer information to help you make better business decisions.

Automation is at the heart of AI, allowing you to assign tasks typically performed by humans to an AI system. These tasks can be simple, like creating contact entries or more complicated, like arranging service calls. It’s in the latter category where AI agents shine, displaying their ability to reason, plan and take action.

For example, imagine an agent is autonomously setting up a service call to repair a washing machine. The customer engages with the agent on the website, using natural language, to arrange an appointment time. If the customer requests a Friday appointment but the agent identifies that the required parts won’t be available until the following Monday, the agent can proactively work with the customer to arrange a more appropriate service date. And if the customer asks a question that’s outside of the scope of the agent’s actions — predefined tasks that an agent can perform in response to a trigger or instruction — the agent can transfer the customer to another AI agent or a human agent that supports the issue.

You can use out-of-the-box agents or build your own agents with customised actions and use automation tools to integrate them with your customer relationship management (CRM) system.

AI lets you resolve service cases around the clock through the use of chatbots on self-service portals and messaging channels. AI agents can chat with customers using conversational language and sophisticated reasoning and can accurately answer questions about orders or customer accounts by retrieving information from your knowledge base and customer data.

Agents can also do things like close an account or mail a replacement card — tasks that typically require human intervention. And because AI agents can provide support over multiple channels, including email, chat, social media, and phone, customers get the service they need, no matter which contact method they use.

The always-on availability of AI means you can easily scale your service operations, whether you’re looking to handle increased seasonal volume or expand your overall business. As your case volume rises, you can adjust your AI agents to take on the extra load without missing a beat, keeping customers satisfied with reliable and personalised service.

While AI offers many benefits for your business, it’s important to be aware of its risks — and how to address them — as you move forward with your AI strategy.

Each new AI advancement brings new ethical concerns and the advent of autonomous AI is no exception. How do you collect, store and analyse personal data in a way that protects sensitive information and earns customers’ trust? Make sure the AI platform you choose implements guardrails that improve safety, accuracy and trust across all of its AI products. These guardrails should limit what actions an AI agent can take and require that humans are involved at critical moments in the AI experience. They should also disclose the presence of AI-generated content, ensure that AI systems don’t produce harmful or malicious content and limit the scope of what an AI can generate in order to reduce hallucinations.

In addition, make sure the platform supports the responsible use of AI, with guidelines that focus not only on accuracy, safety and transparency, but also accessibility and sustainability.

As per the latest Generative AI Snapshot Research Series by Salesforce, nearly two-thirds of employees surveyed across organisations in India where generative AI is used reported using banned or unapproved tools at work. This aligns with the global trend where workers acknowledge that using company-approved generative AI tools is the optimal, ethical and safe use of generative AI.

When it comes to data storage, using a method called zero copy lets you access and share data across multiple locations without needing to move or copy it. This reduces duplicate data and increases data security.

LLMs are the foundation for today's AI applications. They’re trained on massive datasets, which gives them a deep understanding of a broad range of information, allowing them to reason, make logical inferences, and draw conclusions. You can train your own LLM, but it’s a challenging and expensive task that takes weeks to months to complete and requires a significant amount of hardware, software, and engineering resources. Fortunately, you can skip all that by using an existing LLM with smart prompts grounded in your data. It’s the easiest and most commonly used approach to building LLM-powered applications.

Training LLMs is not only costly to businesses; it’s costly to the environment, too. For example, training an LLM generates around 300 tonnes of greenhouse gas carbon dioxide - that is around the same output as 125 return flights between New York and Beijing. With advancing models, this number is only set to increase. Once an LLM is trained, the energy needed to keep it running can be even greater, especially if it’s used at scale. One way to avoid this massive environmental footprint is to use a smaller, highly trained, domain- and task-specific model, which is more energy efficient than a general-purpose LLM. Several small language models are currently being developed specifically for CRM.

Since AI typically runs on cloud-based data centres, which also use considerable amounts of energy and water, choosing a low-carbon data centre is another way to reduce environmental impact.

Salesforce Delivers Next-Generation AI Models to Power Agentforce

You’ve probably heard that generative AI can be a rather confident liar — making things up or hallucinating, close to 30% of the time. Such confabulations can result in inaccurate analyses, negative biases and other missteps that erode customer trust. If you’re using an LLM, there are several ways to reduce AI hallucinations. For example, you can write more-specific prompts and follow-up questions for the LLM and you can tell it to not make up an answer if it doesn’t know it.

But all the great prompts in the world won’t matter if you’re not using a trusted LLM. Domain-specific models, which are built on your own knowledge base, offer more trusted, current, and relevant information than off-the-shelf models, which are trained on the public Internet and may not use the most recent data. The best way to get high-quality results from your LLM is to use retrieval augmented generation (RAG). This AI technique lets companies automatically embed their most current and relevant proprietary data — both structured and unstructured — directly into their LLM prompt. The result? Fewer hallucinations and more accurate outputs.

Whether people are buying clothing online, undergoing medical tests or applying for a job, they deserve to know when and how AI is using their data. But not all companies are as upfront about this as they could be — and this lack of transparency can erode consumer trust and, sometimes, even cross legal boundaries.

To become more transparent about AI, clearly explain the role of AI in your business and disclose how the technology is being used. Create data usage policies and explain how and why your AI system forms its responses. Clarify what tasks customer data will be used for, such as product recommendations and what it won’t be used for, such as marketing. When possible, offer tools that let customers see the type of data AI is using and allow them to opt out. Finally, perform regular bias audits to make sure outcomes are fair and nondiscriminatory.