What if you had an employee with superhuman data processing and output skills who could work around the clock for you, and was perfectly happy to do it for almost no compensation?
That’s what generative AI amounts to.
In this blog post, we’ll:
1) Explain what sets generative AI apart from traditional artificial intelligence
2) Explore how generative AI can revolutionise the effectiveness and profitability of your SaaS forecasting
3) Examine some of the best ways to boost your company’s cash flow with this new tech
Here’s what we cover:
- Understanding generative AI and how it compares to AI
- The importance of forecasting in SaaS finance
- 4 ways SaaS CFOs can boost cash flow with generative AI
- See the difference that generative AI can make
Understanding generative AI and how it compares to AI
Generative AI combines machine learning (ML) techniques with large language models (LLMs) to generate new text and other types of humanlike content.
In a general framework, that could be writing, images, video, or audio content. You can find plenty of examples of AI-generated content with a quick Google search.
In the context of SaaS finance, however, CFOs are using generative AI to seamlessly generate:
- Financial reports and statements
- Low-variance, long-range SaaS forecasts
- Deferred revenue burn down readouts
- Detailed capitalisation tables to track external funding
- Much more, without requiring any computer code
Let’s take a closer look at the benefits of generative AI for SaaS finance and accounting.
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Generative AI and SaaS finance: A perfect pairing
The benefits of generative AI for SaaS CFOs are many and varied.
To help you get a clearer picture of the rewards you can reap with generative AI capabilities, we’ve listed some of the major benefits your company can gain from switching to automation accounting.
Total visibility into your company’s cash inflows and outflows
The detailed financial reports created by generative AI provide end-to-end visibility into all your cash coming in and going out.
As a SaaS CFO, your ability to succeed is directly tied to your ability to track your cash.
Predictive analytics to inform your decision-making
Cloud accounting software with generative AI can use predictive analytics to perform sentiment analysis.
This helps you gauge which customers are at risk of churning and which are good upsell and cross-sell candidates.
Real-time integration with dozens of SaaS metrics
SaaS companies rely on a huge range of different metrics and KPIs to track their financial performance and cash flow.
Accounting automation gives you access to dozens of SaaS metrics that are continually updated in real time, so you never miss a beat.
Automated customer service via chatbot
Generative AI can even offer assistance with fielding customer questions and support tickets.
This is a great way of freeing up extra time for strategising at your organisation.
Now we’ll go deeper into SaaS forecasting. What’s the value of it, and how is generative AI uniquely suited for maximising your ability to predict the future?
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The importance of forecasting in SaaS finance
Accurate forecasting plays a crucial role in SaaS financial planning and analysis (FP&A).
By leveraging forecast models powered by ML and generative AI, you can better predict threats and opportunities that could impact your future revenue stream.
SaaS CFOs run forecasts to predict all kinds of things, including:
- Churn rates and customer sentiment: Forecasting on a regular basis helps you maintain a clear understanding of what direction your churn rates and overall user sentiment are trending in. This is incredibly valuable in facilitating early outreach and keeping the business of users who are about to churn.
- The impact of billing and pricing adjustments: Even minor adjustments to your product pricing and billing can have massive implications for your long-term profits. SaaS forecasting allows you to precisely gauge the downstream effects of different SaaS billing models.
- The best way to allocate company resources: SaaS budgeting is a complex process requiring the input of many different people. As a CFO, you can provide clarity around optimal allocations by forecasting the impacts of different budgeting choices.
Now that you’ve gotten a quick refresher on SaaS forecasting, we have another question to answer. How is generative AI the perfect companion for all your SaaS forecasting endeavours?
The role of generative AI in SaaS forecasting
Utilising advanced ML algorithms, generative AI identifies patterns in your historical accounting data to make highly accurate financial predictions almost instantly.
SaaS CFOs can leverage generative AI to simulate various scenarios, allowing for more accurate predictions across longer spans of time than legacy methods would be capable of.
This predictive capability optimises everything from resource allocation to pricing decisions and puts cost-effective and hyper-efficient forecasting at your fingertips.
And the best part of all is that forecasts created with ML and generative AI feature dynamic scenario planning.
This means that your forecasts will shift in real time to reflect the financial changes occurring in your environment.
This amounts to continuous, low-cost, highly accurate SaaS forecasting. Try beating that with legacy forecasting methods.
Won’t our company be liable for model documentation?
If you found yourself asking this question, that’s a great sign. It means you’re an attentive finance leader.
Yes, companies in the United States and elsewhere that use ML for forecast model assembly must keep detailed records of their model creation workflows.
Generative AI automatically takes care of process and assumption documentation, offering transparency and peace of mind in the event of an audit.
SaaS CFOs can easily access and review the comprehensive documentation produced by generative AI, ensuring total accountability and mitigating legal risks.
With detailed model documentation, SaaS finance teams can confidently navigate the regulatory landscape surrounding the use of AI and ML in SaaS forecasting.
As important as forecasting is, there are plenty of other ways finance leaders can boost SaaS cash flow with generative AI.
Let’s review some of them.
4 ways SaaS CFOs can boost cash flow with generative AI
As a SaaS CFO, maximising cash flow is your bread and butter. generative AI can help you do that by automating data entry, keeping detailed track of your cash flow, and so much more.
Let’s see what else this new technology is capable of.
1. Streamline your SaaS metrics integration
Maintaining up-to-the-second intelligence on your SaaS metrics has never been easier than it is today.
And that’s fantastic news because your KPIs underpin every process that impacts your cash flow, from budgeting to price-setting and countless others.
Accounting software equipped with deep learning and generative AI functionality gives you continuous access to dozens of SaaS metrics, all updated in real time.
Cloud accounting software centralises your company’s financial data, meaning other stakeholders can easily reference all the metrics they need to gauge departmental performance.
The software industry moves at breakneck speed.
But your SaaS metrics give you an opportunity to slow down, take stock, and make an informed plan that results in cash flow.
Very little in SaaS finance is more valuable than that, and generative AI can make it happen.
2. Eliminate revenue leakage
Manual revenue recognition for recurring revenue SaaS companies can quickly spiral into unmanageability.
This is a serious threat to your cash flow, and will quickly result in leaked revenue, which is revenue that you can’t report because it falls outside the purview of ASC 606, an accounting regulation operative in North America and elsewhere.
SaaS companies can leverage the power of generative AI to identify and address revenue leakage points and revenue recognition problems.
By analysing your revenue recognition data, generative AI detects process inefficiencies, enabling finance teams to implement strategies for rapid revenue recovery.
By eliminating revenue leakage, SaaS companies can quickly improve their bottom line and ensure a healthier cash flow.
3. Analyse customer sentiment for early churn intervention
Generative AI leverages its advanced natural language processing (NLP) capabilities to analyse customer sentiment and identify potential churn risks.
Through user sentiment monitoring, generative AI enables SaaS companies to intervene at an early stage of churn risk and implement proactive strategies for customer retention.
After all, your company won’t keep making money if it can’t hang on to customers for the long haul.
In addition to preventing churn, gauging user sentiment is crucial for maximising the success of your product design and software development workflows.
With generative AI’s powerful NLP algorithms, businesses can stay ahead of customer sentiment trends and take timely actions to ensure long-term customer loyalty.
4. Increase the reach of your products with recommender functionality
Business leaders in all kinds of domains have leveraged AI-based recommender systems at their companies. And you should give serious thought to it, too.
In a nutshell, it uses AI models to look at which products and services a user has purchased from you in the past, and nudges them toward your related offerings.
Recommender systems are a fantastic way to:
- Expand the reach of your products without increasing your marketing spend
- Improve user sentiment toward your brand–people tend to appreciate genuinely useful recommendations
- Increase the average subscription cash flow generated by each of your users
By identifying relevant products for individual customers, generative AI improves customer engagement and drives revenue growth.
See the difference that generative AI can make
Generative AI holds a world of promise for both large companies and smaller businesses in the SaaS sector.
In fact, we’ll probably look back on this moment in twenty years and all agree that these tools were the modern equivalent of the invention of the personal computer, or calculators.
They truly are that important, and their positive impacts on SaaS cash flow and forecasting really are that profound.
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