Customer service

Advancements in NLP, along with natural language understanding (NLU) are transforming customer service departments and contact centers into sources of profit. AI-powered chatbots and virtual assistants increasingly understand complex human conversations, including the syntax we convey so much unspoken information through.

Specific examples include:

• Chatbots: Bots from companies like Ada, Intercom, and LivePerson us AI to handle customer queries and reduce overall friction within customer experience (CX)
• Virtual Assistants: While sharing similar enterprise-grade conversational AI as chatbots, AI-powered virtual assistants like IBM's Watson, Amazon Connect, and Kore.ai offer additional ways for businesses to automate self-serve CX through voice and text.
• Sentiment Analysis: Tools like MonkeyLearn and Lexalytics analyze digital text to determine the sentiment of customer messaging, be it positive, negative, or neutral. 

Human resources

AI capabilities in human resource departments are helping streamline the corporate hiring process, sifting through millions of applications to identify those with the most potential while improving onboarding and employee engagement with personalized development programs and training.

Specific examples include:

• Recruiting: Tools like Pymetrics and Eightfold.ai use AI to accelerate the vital process of matching potential candidates to suitable job positions, streamlining the recruiting process for all involved.
• Onboarding: AI platforms like Talmundo and ChatGPT allow HR teams to personalize onboarding employee onboarding and accelerate training, leading to higher retention and employee satisfaction.
• Employee Engagement: Platforms like Humu and Peakon make employee voices heard, analyzing feedback and curating recommendations tailored to the specifics of the enterprise.

Data analysis

Using data science algorithms, data scientists are now directly impacting enterprise bottom lines. AI makes analyzing vast amounts of data relatively easy, which increases the time data scientists—and their business analyst counterparts—have to uncover key insights and strategic opportunities.

Specific examples include:

• Enterprise analysts and strategists tap into the power of big data using data visualization tools like Tableau, Google's Looker, and Power BI.
• AI-fueled platforms like Alteryx and RapidMiner leverage machine learning algorithms to make predictions that can make large organizations more resilient and agile. 
• Natural Language Processing: NLP has opened the door for platforms like Domo and AnswerRocket, offering augmented analytics (AA) that can learn and adapt to the changing behavioral patterns of metrics. 

Supply chain management

As a more specific benefit of enhanced data analysis, AI models and predictive AI are helping optimize supply chain business processes by analyzing historical data sets to identify patterns that can predict upcoming demand. In doing so, businesses can manage inventory much more efficiently, reducing costs and waste.

Specific examples include:

• Demand Forecasting: Tools such as Blue Yonder and RELEX Solutions use AI to forecast demand and to help keep inventory optimized over time.
• Supply Chain Optimization: Llamasoft and Kinaxis are examples of AI platforms that improve the performance of underlying supply chain operations, mitigating risks in the process. 
• Autonomous Logistics: AI tech like Clearpath Robotics and NVIDIA Drive enable autonomous robots and vehicles used for transportation and logistics. 

Marketing

Consumers increasingly demand personalized experiences from enterprise businesses. Seventy-three percent of respondents to a recent Salesforce survey said they expect companies to understand their unique needs. And marketing-focused AI solutions are creating personalized content, improving strategic targeting, and optimizing ad campaigns to meet these modern customer expectations.

Specific examples include:

• Tapping into company knowledge: House all your brand positioning, product information, and style guidelines in a central hub. Then, trust that all those insights are infused in every piece of content as you and Jasper, or you AI tool of choice, create.
• Team acceleration Get a clear sense of how each asset across your campaigns are evolving. See who is working on what and deadlines from a Kanban or calendar view. You can also take your content creation to the next level by remixing and repurposing existing content with a single click.
• Personalization: Enterprise businesses are tapping into the power of AI-driven marketing platforms like Adobe Experience and Salesforce Marketing Cloud to deliver increasingly personalized experiences and content. 
• Social media analysis: Brandwatch and Khoros are examples of tools that use AI to analyze social media data, track brand sentiment, and stay ahead of trends. 
• Content generation: Jasper for Business and Copysmith increase team collaboration and enable small- to medium-sized teams to produce engaging marketing copy and omnichannel content at scale. What’s more, many top-tier AI writing platforms also offer training opportunities for teams, providing additional paths for mastery and professional development as well. 

"Marketing has undergone another consequential shift as generative AI entered our workflows and changed the way people discover and buy online," said Jasper VP of Marketing Meghan Keaney Anderson. "At this point, Gartner estimates that the majority of marketing teams have begun experimenting with generative AI at this point. The case for piloting AI is a clear one: the ability to create more quickly means tangible efficiency gains for marketing teams facing heavy content demands."

The benefits of AI in enterprise businesses

There are three main benefits to implementing AI in enterprise businesses: 

1. Improved efficiency: As mentioned above, enterprises that leverage AI as part of their digital transformation initiatives quickly see efficiencies by automating repetitive, low-impact tasks. Once these tasks are automated, key employees and teams benefit from more time to focus on identifying and acting on strategic initiatives. Additional benefits for the enterprise include reducing human error and increasing overall productivity. 
2. Enhanced decision-making: As enterprises grapple with the size and scope of big data in business, the speed and thoroughness with which AI can collect, transform, and analyze it have become an operational must-have for stakeholders and decision-makers. Critically, modern AI's ability to uncover potentially game-changing patterns within disparate data sets is nearly impossible for human analysts to identify.
3. Lower costs, higher ROI: AI-powered personalization doesn't just benefit consumers. Behaviorally-targeted marketing is twice as effective as non-target marketing. This means, as AI platforms help marketers run their campaigns over time, marketing spend drops while return on investment (ROI) climbs. 

Be sure to check out A CMO's Guide to Responsible & Results-Driven AI to learn how to start a pilot, scale out the program, and achieve ROI