As corporations wrestle with the implications of enterprise AI, tomorrow's business winners are being decided today. And here's everything you need to know.
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 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.
Enterprise AI is a category of business software that leverages AI to optimize your organization's workflow. Some enterprise AI platforms can even offer data-driven insights to help grow your business in addition to optimizing for efficiency.
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:
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:
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:
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:
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:
"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."
There are three main benefits to implementing AI in enterprise businesses:
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
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.
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.
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, productivity dropping from platform outages, 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. Downtime can also be mitigated by investing in platforms that are interoperable: not relying on a single LLM or model provider but instead using a wide variety of both to maximize reliability and personalization.
"Let’s say a model provider allows you to do a specific type of generation, for social media posts for example, without any interoperability," said Guhan Venguswamy, Jasper’s head of platform engineering. "Then they decide to tweak their privacy position and your data is no longer as secure as it was. You're now no longer able to use that functionality and you lose the ability to do a key portion of your job."
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.
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.
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