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Enterprise AI
Enterprise AI is the application of AI for large organizations, helping boost workforce efficiency and productivity. It includes the use of autonomous agents and a combination of different AI technologies at scale — including machine learning, natural language processing (NLP), deep learning, computer vision, and automation — to change how people work across industries and sectors. This started with the first wave of predictive AI in 2016, and continued with the second wave of generative AI and copilots. Now, we’re on the third wave of AI: AI agents.
When thinking about the AI tools that’ll help your business at scale, considerations should include data privacy and security, cost, and scalability. Look for ways for people and autonomous agents to work together to deliver better results at scale. With product.agentforce, the agentic AI layer of the Salesforce platform, you can have AI agents helping your employees do their best work.
While generative AI relies on large language models (LLMS) and small language models to create new content from the data you already have, autonomous agents can use this data to take action. Today, AI doesn’t just work for us. AI agents work right beside us like an extension of our team.
Why is enterprise AI important?
Enterprise AI has arrived at a crucial moment. 47% of digital workers are struggling to find the information or data needed to effectively perform their jobs, and 41% of employee time is being lost to low value tasks that stall productivity and lead to burnout.
Meanwhile, customer expectations continue to rise. Who hasn’t yelled “representative” into their phone at least once when on a customer service line? Customers don’t want to wait on hold. They expect immediate, personalized, and empathetic service from an expert. But right now, companies are limited in what they can offer, which opens the door for AI.
AI agents are the answer: Over the last decade, predictive AI has changed the way businesses analyze data and make decisions. The introduction of generative AI brought a whole new wave of use cases,and now AI agents promise to automate workflows end-to-end with little to no human intervention.
Examples of enterprise AI
Enterprise AI has the potential to improve productivity for every walk of organizational life, from small businesses and startups to large global corporations. All industries and sectors from marketing and human resources to finance and customer service to manufacturing and supply chain management can take advantage of AI agents. Here are some ways AI is being used for enterprise-wide success:
Sales: In sales, humans and agents can work together to make every step of the sales cycle even better. From creating a sales pitch or prepping for client meetings to creating email follow-ups and updating CRM records, this accelerated productivity and automation can help increase revenue and create lifelong customers. Enterprise AI can also help optimize sales opportunities by providing a summary of an account and forecast guidance as well as generate and send sales agreements around the clock.
Marketing: AI can build and launch personalized marketing campaigns in a way that increases productivity and customer engagement to improve performance across the entire customer lifecycle. Creating a campaign strategy, building and managing marketing campaigns across channels, and measuring the effectiveness of the campaign can all be achieved with the help of AI. More specifically, AI can reason through NLP to generate an email that’s grounded in the campaign brief so each response is unique to the customer.
Customer service: On the path to delivering instantaneous customer support at any time of the day or night, AI agents can dispatch and resolve service issues with increasing accuracy. This reduces the frequency of escalation so humans can move on to more strategic tasks. Consider these AI agents to be more advanced AI chatbots that can handle more complx issues and learn over time. Not only can they help resolve service issues, but they can find and summarize similar cases so you can prioritize what to focus on.
Commerce: AI is at the heart of personalization in retail and commerce, and it’s something customers expect and want. No longer is this a nice-to-have for retailers. It’s become an absolute must. AI can recommend products or suggest the next best one. Similarly, since the retail experience doesn’t end after clicking the “buy” button, AI inventory management helps foster a good post-purchase experience, which helps build customer loyalty.
Enterprise AI can give all of your teams a boost, improving productivity and helping representatives focus more on personalized customer care. With Agentforce, the tools you need most are integrated inside the system you already use, all with a trust layer at its core so you can be confident your data is secure.
Benefits of AI for an enterprise
Today, enterprise AI is critical to business success. As it becomes an integral part of most work tools and is expected by customers and employees alike, it’ll become a mainstay in the not-too-distant future.
A key benefit of AI — the ability to have an agent available at any time — will help businesses meet customer expectations in ways we’ve never seen before. Other benefits of enterprise AI include:
Automating tasks and optimizing operations
The most obvious, and often mentioned, benefit of enterprise AI is that it will increase productivity and reduce burnout by automating routine tasks. When employees are freed up to work on bigger-ticket tasks, they’re happier and more productive, which helps companies grow. Finding ways to automate simplifies operations in a way that contributes to cost savings as well as reduces employee burnout.
Efficiency and reduced costs
Enterprise AI means having a digital workforce that uses data to improve over time, works around-the-clock, and helps your team make more informed decisions. This leads to greater efficiency and decreases costs.
Analyzing large amounts of data and predictive capabilities
AI uses machine learning, NLP, and other techniques to analyze vast datasets to provide predictive analysis that improve operations. The predictive capabilities gained from being able to analyze large amounts of data are especially useful in the healthcare industry.
Instant monitoring and flagging issues
Advanced machine learning algorithms analyze data in real time to identify patterns and anomalies that pose cyber threats. The ability to flag issues or detect fraud is especially crucial for financial services,where banks and institutions depend on security.
How to implement enterprise AI
Implementing enterprise AI successfully hinges on having secure, high-quality data. But to adopt AI, several things need to be in place:
1. Know your business goals: Prioritizing what you want to achieve will help direct your AI strategy. Once your strategy is implemented, then measuring success is straightforward.
2. Assemble a team of stakeholders: Since AI is complex, having a diverse, cross-functional team in place to plan and oversee AI implementation helps reduce any blindspots when integrating AI.
3. Prep your data and build models — or select a vendor: Ensuring the safety of your high-quality data is paramount to AI success. Developing the right data strategy, or using Data cloud, is key for training your models and ensuring accuracy.
4. Communicate with and train employees: Having a well-trained workforce helps make the AI rollout smooth and eliminates any confusion or apprehension around working with an AI agent.
5. Start with a pilot program:. Experimenting with a small trial before a full rollout is always a good idea, because it helps identify bugs and avoids any significant disruption along the way.
6. Scale the integration: Once the pilot program is validated, starting to integrate it little by little throughout the organization comes next. Following a pre-determined schedule that everyone is aware of will help ensure there’s not much disruption as the technology rolls out.
7. Assess regularly to maintain, update, and adapt: Most technology, AI included, needs constant monitoring and updates. Developing a plan of continuous check-ins will ensure your AI keeps up with your business goals.
Challenges and risks of enterprise AI
Since enterprise AI is still developing, there are a few considerations that companies need to keep in mind as they move forward. The following areas are at the top of the list to address as progress is being made.
Responsible use and ethics
The ethical use of large-scale enterprise AI necessitates care and proper management. Ensuring that AI is thoughtfully designed to be unbiased and have guardrails is everyone’s responsibility, especially at the organization level. Ideally, each organization will adopt AI principles that hold businesses accountable and guide the ethical use of the technology. Being committed to building AI responsibly includes being transparent, training, and empowering the people using it, and respecting the societal values of those impacted by the technology.
Data privacy and security
Since AI consumes massive amounts of data, data privacy and security are at the heart of investing in enterprise AI technology. Protecting this data against breaches or misuse of any kind is necessary for maintaining trust. Make sure the enterprise AI platform you go with is trustworthy (this is a key tenet of Data Cloud) and can capably handle your valuable data.
Change management
It’s difficult to get employees to adopt AI solutions. One reason might be because they mistrust the technology. After all, this is all very new. A second, related explanation is that they don't have access to the right training tools — this is an opportunity to retrain and reskill employees.
Finally, their feedback might not be getting incorporated into workflows. Companies can address these issues and adopt best practices around transparency, training, and implementation so employees will embrace and benefit from enterprise AI.
Content safety and moderation
AI is notorious for its hallucinations, which can sometimes be difficult to spot and correct. Also, depending on the datasets it’s trained on, there could be cases of unintended bias and toxicity in AI outputs. Salesforce uses the Atlas Reasoning Engine to prevent hallucinations by prompting LLMs to share their thought processes and reasons for making the decisions that it does. This transparency requirement significantly cuts down on hallucinations. As this is adopted by more AI tools, more scrutiny is put on data, and more guardrails are established, this challenge will likely improve over time.
Intellectual property
If the model is trained using data that includes trade secrets or private information, it could leak data or leave you vulnerable to intellectual property infringement issues. Since models are trained on vast sources of data like the internet, there's a possibility of IP theft if the intellectual property is available online.
Trends for the future of enterprise AI
In just a short time, enterprise AI has evolved quickly — and the momentum doesn’t seem to be slowing down. AI is the standout technology of our time.
This third wave of agentic AI, with intelligent agents handling complex tasks autonomously, will soon give rise to the fourth wave of robotics, and eventually artificial general intelligence (AGI) that has human-like ability to learn, reason, and adapt, and so on. Multimodal AI will integrate sensory experiences like vision, touch, and speech to help autonomous agents interact with humans.
For now, existing technologies will only get better. For example, prediction capabilities for customer behavior and market trends will improve, which helps businesses and customers make better decisions. Personalization will become more fine-tuned to proactively enhance customer experiences and marketing efforts. For manufacturers, automation will continue to improve and expand — requiring less human interaction along the way.
Combining AI with IoT devices, the “internet of things,” means that devices can collect data, analyze it in real-time, and make decisions autonomously and efficiently. Using AI with blockchain technology will work on data security and transparency and also help improve efficiency. Finally merging edge computing and AI tackles latency and connectivity issues with cloud data centers, so time-sensitive applications like healthcare monitoring and manufacturing automation can work unhindered.
Certain industries like hospitality are using enterprise AI to offer unforgettable guest journeys with personalized experiences that keep travelers coming back. In healthcare, doctors and providers can use AI to connect data and trends to offer better care that improves outcomes, and pharma can speed up research and development to innovate quicker and more accurately.
Agentforce: The best path to AI enterprise
The benefits and inevitability of enterprise AI are well-known. The promise of boosting productivity and growth coupled with major cost reductions would be foolish to ignore, but the barrier comes down to implementation. Implementing it correctly takes some finesse.
Not sure where to start? Agentforce, a complete digital labor platform, can do that heavy lifting for you, integrating data, AI, and automation into your workflows. Agentforce helps your representatives put their focus back where it belongs — on your customers.
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