AI in the Enterprise: Top Five Trends
With the explosion in interest in AI (no small thanks to ChatGPT), businesses the world over are looking at how they can leverage AI to help grow their business. It has become a transformative force in the business world, reshaping the way enterprises operate and make decisions. The best part is this is not just a tech hype, there are actual tangible results in applying AI. From empowering product, technical, marketing and customer support teams internally to providing key data instantaneously to management and becoming an up-to-date customer interface, the business value of AI-driven processes is immense.
However, the degree to which enterprises invest in and leverage AI varies, as well as the degree of adoption. The type of business also influences the area of AI application. Digital native businesses and those with strong online presence and commerce are interested in customer sentiment, product personalization etc. More traditional businesses are keen on using AI in no-code application development, testing etc. Almost all businesses see value in the customer experience domain.
As with every other new tech, AI vendors entice the companies with promises of minimal upfront cost, PoC’s, pilots and fantastic outcomes. Many teams are goaded into starting PoC’s, without evaluating the long-term sustained costs and benefits. In this article, we will look at some top AI trends that we in DataLens, a born-in-the-data-cloud company, have had the opportunity to work on.
We will see below some domains which are common across businesses:
Top 5 trends in AI in Enterprise
The customer experience starts, rather obviously, with the customer, internal or external. Digital avatars, voicebots & chatbots are the top AI tools used by businesses. Some of the use cases we have seen include digital avatars representing HR department to serve internal employees, technical support bots that use chatGPT to search product manuals and server customers, etc. Customer service is an area where a lot of routine, repeatable tasks can be taken over by bots, letting humans take the occasional complex and critical tasks. AI can be used to triage initial contact calls, generate personalized solutions to common problems, and generate reports and summaries of customer interactions.
Surprisingly, many customers have ventured into a key IT function – Software (application) development with AI. Particularly for enterprises with large application teams, and custom development projects, using generative AI tools (such as ChatGPT Codex) is a great accelerator. Building coding and other technical skills has been a challenge for companies, and here is where AI in enterprise makes the most difference, and increases RoI. There will be a lot of exciting opportunities for people who have good ideas and a love of solving problems, but not necessarily hard technical skills.
From search engines like Bing and Google to productivity tools like Office, social media apps like Facebook, and industry-specific platforms (banking, travel, education), adding AI chatbot functionality is emerging as an effective strategy for driving next-generation customer experience. As AI becomes more adaptive to security, regulatory practices, the use of these add-ons to commonplace applications will vastly increase. For e.g., Adobe’s integration of generative AI into its Firefly design tools, trained entirely on proprietary data, to alleviate fears that copyright and ownership could be a problem in the future.
Augmented Employee Productivity
There are myriad roles in an Enterprise, many of which are not directly related to the business but play integral support roles. DataLens customers have invested in implementing AI for
- Sales teams outreach to customers (emails, automated calls).
- Process & product documentations and presentations by tech writers
- Routine legal documents
The rise of AI has implications for both cybersecurity threats and defences. On the one hand, cybercriminals are using AI to launch more sophisticated attacks. On the other hand, AI is a potent tool for detecting and mitigating security threats. Machine learning algorithms can identify anomalies in network traffic, potential vulnerabilities, and suspicious behaviour, helping businesses fortify their cybersecurity infrastructure.
While these trends showcase the incredible potential of AI in the enterprise, it’s important to note that responsible AI deployment is paramount. Ensuring ethical use of AI, addressing bias in algorithms, and safeguarding data privacy are critical considerations for any business integrating AI into their operations.
DataLens’ Dhee platform has modules for Data engineering operations, ML functionality, and Dhee Chat, a LLM-enabled search engine on internal data. Dhee Chat works on cdv, PDF files and Databricks datalake, helping customers securely query their own internal data sources. Dhee is a constantly-evolving platform, built from the DataLens teams’ field experience, and adding features built for customers, constantly. This ‘built-by-engineers-for-engineers’ is the real value that Dhee provides to customers.
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