It may feel like automation has been the hottest tech topic for nearly a decade now, but when it comes to practical application we are only just beginning to scratch the surface. A few months ago, I had a chance to dive deeper on this topic with industry leaders from Benchling, Amplitude, and Genesys Cloud at our Generative AI: Future of Accounting Summit.
There is perhaps no industry in which this dynamic is more apparent than finance and accounting. 75% of accounting processes are largely manual, according to a recent Deloitte survey of finance and accounting professionals. This further demonstrates the potential impact that automation can have — and is already having — on the day-to-day lives of accountants. It also puts into context just how tremendous an opportunity we have in front of us here at Klarity to improve the lives of our customers.
Relatively speaking, adoption has been slow. Steady, but cautious. But it is only a matter of time until we hit an inflection point, one that sees adoption of automation increase exponentially almost overnight. As a former accounting pro myself, I’ve seen firsthand the transformative effect automation can have on both an organization and its people. It’s the primary reason I joined Klarity in the first place.
The bottom line is simple: corporate accounting involves a lot of manual, painstaking work performed by highly trained, often over-extended teams. Much of that work can already be automated today, at least in part, freeing up an organization’s most critical strategic resource — its people.
Today, it largely comes down to human subjectivity. It is a barrier we will have to cross as an industry to get automation to an extreme state in the world of accounting. For example, even within a single audit firm, different partners may have different expectations or draw different conclusions on how certain guidance is worded. They may have different outlooks on how a company is performing in line with that guidance. To date, the industry has not come together to agree on automation as the single best path forward.
At the end of the day, accounting will always require some degree of human touch. The question of how much has historically been a speed bump as we’ve evaluated both the potential and limitations of automation for our industry. It’s a question that we at Klarity have set out to answer in close collaboration with our users.
Regardless, we are seeing an undeniable uptick in adoption of automation in accounting, because it doesn’t just offer an easier or more efficient way forward. It offers a more accurate one, too. Early adopters, like our innovative first customers, have demonstrated that the greatest benefit of automation is risk avoidance.
Ask any auditor, and they will tell you the same thing — if it’s a repeatable process, then automation is inherently less risky than manual intervention. Automation has gotten to a place of accuracy that allows regulators to feel comfortable. This has already happened across other industries, but in the highly regulated world of accounting the threshold is simply higher. Now that the long term value of automation has been made clear to auditors, the regulators and the builders of automated solutions are communicating in a meaningful way for the first time. It is a new frontier, one that will lead to a cycle of increased innovation and adoption.
As we navigate that new frontier, we can expect to see additional accounting workflows automated either fully or in part. Let’s explore a few that I think will be particularly relevant over the coming 12-18 months, and highlight a few of the innovators best positioned to help.
Close and automation processes are rife with opportunity for automation, due in large part to the amount of remedial, manual work required. If an accountant downloads a bank statement or transaction register, for example, then transfers it to Excel, they may have to try to manually review both to ensure the transactions match. Another example would be reconciling a sub-ledger to the general ledger. These manual processes are highly prone to failure. Even if the data matches up, something as simple as dragging a formula down to the wrong cell can cause all of your transaction balances to show up wrong.
Automation removes this propensity for avoidable error. Imagine in the above scenarios if the data were automatically pulled from the bank statement, or automatically pulled from the general ledger and automatically surfacing any discrepancies for targeted human review. To go a step further, imagine if you have a system that could also automatically tell you why those discrepancies exist. The time savings would be significant, but the decrease in compliance risk could entirely alter the trajectory of an organization for the better.
Of course, some organizations run into roadblocks automating processes that involve sensitive or private personal data. To overcome this obstacle, tools need to be built with both the user and the nature of that data in mind. Built-in security features have become a table stakes requirement for any automation software, and it’s even more critical for vendors serving customers in highly regulated industries. That foundational work has to be done well in advance of any go to market motions to establish trust and credibility. Visibility into any automated processes is also a must for accounting pros. They need to be able to see and trust that what’s happening is happening correctly. Klarity has been architected to ensure that these user requirements are met at a foundational level, which makes the potential for automating close and reconciliation processes boundless.
As someone who has spent their entire career in corporate finance, I am intimately familiar with how much time can be saved by automating even a small part of your organization’s revenue accounting calculations. Take revenue waterfall calculation, for example. It’s a long, arduous process. It requires teams to load all of their policies into a system so that they are uniformly applied to each transaction. If a revenue team can automate 80-90% of those transactions, then the potential time savings alone is invaluable.
There can be pitfalls with some of the applications built to address this need. There are edge cases that can be time consuming, or require a significant human lift to validate the data coming through. But for the vast majority of revenue accounting teams, a significant portion of the manual work can be automated to save time and produce more accurate results.
One solution purpose-built to help organizations tackle accounting calculations is Zuora RevPro. I’ve overseen and participated in multiple RevPro implementations myself, and can personally vouch for how much time and pain it saves the average revenue team. Coupled with Klarity, which ensures the data flowing into your revenue accounting system is accurate, you can set up repeatable processes to build out your revenue waterfall that require little-to-no human intervention.
Procurement requires a great deal of manual information gathering across the various departments and functions within an organization. Let’s say an Accounts Payable team needs to find and deploy a new tool. Procurement then has to meet with that team, gather the requirements, prioritize features, put an RFP to multiple vendors, manually interview those vendors and assess the content they deliver. Typically, this involves manually comparing PowerPoint decks and feature tables to discern the different benefits and drawbacks, the expected outcomes, any discrepancies in pricing. From there, the team has to manually put together a presentation to relay that information back to the user and make a recommendation.
End of cycle procurement activities, like working with legal on contract negotiation, working on pricing or finding comparable softwares and services to ensure the best deal all require a significant manual lift that can — and should — be automated. This is an area where Generative AI and multi-modal applications, like Klarity, can have transformative effects. We can use text to describe what we want in a presentation, and AI will create it. We can input data and requirements, and AI will produce an RFP. Automating both steps — gathering the information, and packaging that information into something digestible — is a no-brainer application for accounting teams. There is also the potential to automate a significant portion of the negotiation processes — immediate access to relevant industry data that helps identify target pricing quickly and accurately, legal automation for contract redlines, and automated contract review for any terms or language that should be flagged.
Potential roadblocks for automating these types of processes essentially come down to the risk of AI running with bad or incomplete data. Effective automation is dependent on good data. If the requirements given to the system aren’t the right ones, the RFP it produces isn’t likely to find the right vendor or tool. And it’s clear that incorrect or incomplete pricing information could have an adverse impact on negotiation. It’s critical to remember that next-generation solutions will still require some degree of human touch to deliver full value.
First and foremost, today’s innovators need to focus on the user’s pain points. Think about how you can solve for each specific one. If you observe a trend across multiple companies, you know it's a problem with a sellable solution. Klarity is built around this principle, motivated largely by experiences like my own.
Second, understand your prospective customer’s current tech stack. How are they thinking about their existing workflows, which systems are already involved, and how do you fit into that larger stack? Many companies you talk to will say they already have a system that does what yours does, or something like it. You need to be able to articulate how yours is different, and how it drives a different outcome. That starts with understanding what they’ve already deployed. At Klarity, our engineering and sales teams are in constant communication to clearly define the requirements we encounter in the field.
Third, always consider user experience. You know the problem you’re solving, and understand where you fit into the existing workflows, but how is the user going to engage with your solution on a day-to-day basis? We built Klarity to be easy to use, intuitive, functional and most importantly, supportive of the user’s individual goals. This is the model that GenAI tools must follow to deliver true value to accountants and other non-technical end-users.
Additional considerations include always-present budgetary concerns, as well as implementation timelines. Does your customer have the budget for what you’re selling, and if not how are you going to save them enough money to pay for your solution? How long will it take for the customer to see value, and do they have the bandwidth for any required technical integrations? At Klarity, our goal is to provide Day 1 value that grows exponentially over time.
Ultimately, these are questions a prospective customer will ask in a sales call somewhere down the road, so it’s critical for vendors to ask themselves these questions at the foundational stages of product development.
Join your peers at our next Generative AI: Future of Accounting Summit in San Francisco on Wednesday September 20th. Along with accounting automation, this 1-day event will explore a host of topics on how generative AI’s impact could influence how accounting and finance professionals work in the future.
Visit our event page to learn more and lock in on early bird pricing before it expires.