In recent years, many businesses have become enamored with collecting as much data as possible and implementing artificial intelligence to simplify and automate processes.
As a project manager and an executive, I’ve personally supported the creation of dozens of innovative solutions for alternative lenders that leverage the latest technological advancements. Lately, it’s become increasingly clear that artificial intelligence is much more than a mere buzzword companies use to attract attention in their marketing campaigns.
AI technology has evolved to the point where it can make tangible impacts within management, support, marketing, and business analysis by automating processes.
Artificial Intelligence in Support and Marketing: Automating Data Analytics
Artificial intelligence fits best into business verticals that involve large amounts of data algorithms can learn from; support and marketing were the first sectors to start integrating AI solutions.
Let’s examine how three startups successfully implemented AI in their marketing and support departments.
Led by the goal of putting customer support on autopilot, DigitalGenius uses AI to make life easier for support staff and clients alike. The team created an AI solution called Conversational Process Automation. This platform offers both a fully automated support experience and semi-automatic process that escalates chosen use cases to real people.
This solution presents a fresh alternative to the standard robotic voice most people calling customer service hear instructing them to press a series of numbers to get connected to a real person. Companies can integrate these algorithms deep within their workflows to understand incoming requests, even if they use third-party solutions for billing and payment processing.
In the example above of a B2C use case, a user poses a query, "why is my flight delayed?" via text. The platform's functions listed on the right include labels for prioritization, sentiment, case phase, inquiry type, and case details.
DigitalGenius provides information about how to address queries. As the system learns from users’ historic support data and agent behavior, it will intuitively understand how to better serve a business over time.
There’s a compelling reason why Grammarly received $110 million in funding. This AI-driven platform quickly gained immense popularity after going live. Sophisticated algorithms brilliantly locate errors and analyze texts in-depth to help users improve their writing.
Even as I’m writing this article, a Grammarly icon in the corner of my Google doc is giving me hints, almost like the Microsoft Word’s spellchecker on overdrive.
Grammarly’s resounding success was made possible by the team’s unique method for meaningfully implementing an array of AI techniques within one tool.
Grammarly underlines words and phrases in red or yellow to distinguish critical and objective errors from more subjective or stylistic critiques. On the right, an overall score for the writing is paired with a breakdown of the categories that factor into the algorithm's assessment; spelling, grammar, punctuation, conciseness, fluency, vocabulary, formality, and clarity.
Grammarly uses Machine Learning (ML), deep learning, and natural language processing (NLP) to make your copy look and sound better. While it won’t write promotional content for you, it can certainly make your copy better and ensure that it’s error-free.
The AI team behind Grammarly’s solution is in the rare position of having more than enough data to process and teach to their algorithms. Combined with the talent of linguistic experts and engineers working behind the scenes, this expansive wealth of data drives the team’s success.
Salesforce didn’t settle for being the largest and arguably most comprehensive all-in-one CRM for sales, services, and marketing. The company recently rolled out its very own AI solution called Einstein AI.
Functionally, the software facilitates user-friendly data visualization for businesses.
Types of information it processes and presents include:
- Customer engagement statistics
- Data insights
- Sales deals
- Closing predictions
- Product recommendations based on customer behavior.
In practice, Einstein AI instantly completes analyses that would otherwise require countless hours of human labor. The image below depicts an example of what users see when they log in. The platform gives leads an "Einstein Score" based on predictive factors such as having a valid phone number, job titles, actions taken such as downloading content like a white paper, and demonstrated interest in a company's services.
Salesforce has historically offered a robust solution for collecting and processing data from virtually any department. The team took it to the next level by synchronizing and cross-referencing this data to generate optimal business opportunities at the ideal time.
Sales and marketing may have heralded in a new wave of AI applications, but the technology hasn’t stopped there. Many exciting new advancements are yet to come.
Artificial Intelligence in Finance: Simplifying Complex Processes
Everything in the finance-related domain has always been primed and ready for the rise of AI. Historically, financial processes have always taken too long and been overly complex.
The following companies are just a few examples of how AI is changing the financial landscape and nature of the industry’s workflows.
TurnKey Lender offers a platform that automates the entire lending process, from application and underwriting to servicing and collection.
Risk evaluation and making credit decisions are typically the most complex and time-consuming parts of the lending process. TurnKey Lender’s team decided to capitalize on the AI revolution to transform lending.
The platform features an origination module that comes with a built-in risk evaluator and decision-making engine powered by AI. It collects user data from synchronized open resources to pair with behavioral analysis.
Using proprietary algorithms, the algorithm then evaluates each individual borrower. TurnKey Lender’s platform distills a process that used to take days and sometimes weeks into a matter of mere seconds.
Self-learning algorithms adjust to each new company’s specific clientele to approve more of the right loans faster.
By freeing up lenders’ time and resources, TurnKey Lender empowers them to keep their focus on business development and marketing. Significantly reducing risks and human error is yet another value add.
Fyle leverages the power of AI to automates expense management. Algorithms take care of laborious tasks like following up on receipts, tracking expense entries, and reconciling them against the corporate cards to keep your business covered from the risk of non-compliance and fraud.
Fyle’s cloud solution efficiently extracts, analyzes, and manages expense data from any kind of receipts. The software can be integrated with G-suite, travel and accounting systems, and internal HR departments to make sure no money goes unaccounted for.
The AI applications currently used in finance not only solve designated problems but also serve as the ultimate proof-of-concept for compatibility within this incredibly conservative niche.
Artificial Intelligence in Business Management
Virtually any part of a business’ operation can serve to benefit from implementing artificial intelligence in one way or another. Some of the more universal examples are illustrated by the companies listed below.
DataRobot is a platform with functionality that fully revolves around machine learning. This software is best suited for large corporations. It analyzes business’ data and provides you with advanced predictive models.
The framework for building predictive models can be applied to several different niches. Common sectors include:
This screenshot depicts the workflow within the platform. Step by step, users prepare a dataset and drag and drop it into the system, which automatically builds and evaluates hundreds of models. The product monitors and manages all deployed models so it can ultimately make predictions and get insights.
In a nutshell, it equips businesses with tools that help them accomplish weeks worth of data science within hours.
LawGeex is a startup that makes legal documents accessible for the average person to understand, despite endless clauses and addendums.
The software they developed uses AI to analyze contracts and locate problems or irregularities. It can also explain the meaning of unclear sections in layperson’s terms.
Algorithms in this technology read between the lines of contracts. They reference implications with your internal policies to ensure you don’t sign up for anything outside of your abilities.
Problematic or questionable clauses are flagged and paired with suggestions to fix them. Since skilled lawyers have expensive hourly rates, this software preempts contract issues while saving your company a fortune in billable hours.
Cinnamon is working on a solution to save businesses worldwide millions of work hours on repetitive tasks that don’t require creativity. The team noticed that even in the modern environment, business relies heavily on templated documents which humans still need to analyze.
As a solution, they created Cinnamon AI to automate monotonous data extraction and processing tasks. With this tool in hand, your employees will save time extracting key points from contracts, registering invoices, and typing up hand-written text.
The platform enables you to delegate time-consuming and repetitive work to Cinnamon’s AI, which can understand and work with documents much the same way a human would.
AI’s applications for automation in business don’t stop here, but even the few examples we’ve discussed already illustrate the possibilities of AI are limitless.
The Present and Future of AI for Your Business
Artificial intelligence is just starting to expand beyond university campuses and large-scare R&D facilities to revolutionize small businesses.
In the years to come, AI will enable exponentially more automation in the marketing, sales, legal, finance, and management space.
Even though AI solutions are already incredibly useful for mitigating risks and saving time, they aren’t perfect. In the vast majority of use cases, they still require human control, no matter how advanced their algorithms might be.
At the present moment, AI is ushering in the dawn of a new technological era of advanced automation. Businesses that take a leap of faith and ride this wave will likely come out on top.