AI In Enterprise: Everything You Need to Know
In business, the word enterprise is so often used as a business descriptor that it's easy to forget its greater meaning. In actuality, enterprise began as a way to describe a difficult task. And as such, the typical enterprise involved taking on a great deal of risk, venturing into uncharted waters, with a goal on the horizon that could make the whole venture worth it.
But as artificial intelligence (AI) flips the world on its head, to say those involved in bringing about AI in enterprise businesses face a difficult task is an understatement.
We've seen enough of AI's potential to sense the goal of AI adoption in the enterprise is worth it. But the waters ahead are certainly uncharted. That is, it's hard to tell exactly how this journey toward AI-fueled enterprise business is going to go.
However, understanding changes already well underway can give us at least a chance to divine how this journey will end.
Defining AI in the Enterprise
Let’s begin by defining artificial intelligence (AI) and its current role in enterprise businesses. AI (e.g., AI applications, AI platforms, bots, etc.) refers to computer systems that can perform tasks that, until recently, required human intelligence to accomplish. In the world of enterprise businesses, this often came down to some combination of problem-solving, perception, reasoning, learning, and the ability to understand language.
AI, originally referred to as expert systems in the business world, made its mark through fairly straightforward automation and optimization of simple, repetitive tasks. They were enablers, helping employees get more done at an expert level (hence the moniker).
Modern business AI still absolutely plays the role of an enabler. But thanks to advancements like deep learning, natural language processing (NLP), and generative AI, these tools can now play a much more substantive role in helping businesses increase efficiency, reduce costs, and maintain an edge on their competition.
How are AI Tools Being Used at the Enterprise Level?
As AI rapidly evolves, new use cases in enterprise businesses are popping up faster than can be documented in one article. But, as it stands, the transformative nature of AI is manifesting most obviously in five key areas:
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.
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.
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.
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:
- 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.
The Overall Benefits of AI in Enterprise Businesses
There are three main benefits to implementing AI in enterprise businesses:
- 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.
- 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.
- 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.
AI in Enterprise: Frequently Asked Questions (FAQ)
Q: Is it expensive to implement AI in enterprise businesses?
A: Costs of implementation will vary depending on the digital maturity of the enterprise and the complexity and scope of how the AI will be implemented. Other variables include how unique the needs of the enterprise are, whether or not a given AI-platform is open source, and the number of employees and departments that will need access to the tools and advantages it provides.
That said, one advantage of the speed at which AI for enterprise is evolving is that it continues to become more affordable and accessible. Meaning if the benefits don't currently outweigh the initial investment for a specific enterprise, they soon may.
Q: What do we do to make sure we use AI ethically and responsibly in our business?
A: In addition to understanding how a given AI platform is built and trained, clear company-wide guidelines and policies are key. Ensure that stakeholders are involved in their creation. Clarify to employees which projects can be AI projects and which cannot (and why). And make sure someone within the company is responsible for staying up to date on emergent AI ethics standards and business best practices.
Q: What are the risks involved in using AI in enterprise businesses?
A: Like the introduction of any new technology (e.g., enterprise AI applications, apps, the latest and greatest iteration of Microsoft Outlook, etc), implementing AI comes with some risks worth taking seriously.
These risks include job displacement, algorithmic biases, and privacy concerns, especially in highly regulated industries like finance and health care. However, these risks can be mitigated by updating (what should already be) robust data protection measures, comprehensive employee training, and regular audits of AI for biases.
Q: What's the best way to get my C-suite thinking about AI?
A: Business leaders like chief information officers (CIOs) focus on business value, so focus your efforts there. Identify use cases where AI could significantly impact your specific enterprise. Invest time to research and identify which AI solutions and platforms will best cater to your needs. And don't hesitate to reach out to experts, tapping industry-vetted subject matter experts (SMEs) to help you flesh out your AI proposals.
Enterprise AI: Today's Competitive Advantage, Tomorrow's Table Stakes
Crystal balls don't help much in predicting the future. Magic 8 Balls don't fare much better. But trends point to some specifics regarding the role of AI in enterprise businesses that are more likely than not to come true.
As AI pricing drops and accessibility skyrockets, thanks in part to the proliferation of cloud computing and cloud services, platforms like those discussed will play an increasingly pivotal role in the future of business. With this, large organizations that put in the hard work now, implementing AI technology into their daily operations, will be better positioned to thrive in an increasingly competitive landscape.
But remember, big changes often have small beginnings. Despite the size and scale of the average enterprise, a few employees who dive in and become well-versed in using AI tools for business will quickly learn to make a case that could revolutionize their entire organization.
Could one of those brave, proactive, forward-thinkers be you? Let's find out: Sign up for Jasper for free and explore the potential of AI in enterprise firsthand.