{"id":4668,"date":"2020-08-21T11:42:33","date_gmt":"2020-08-21T18:42:33","guid":{"rendered":"https:\/\/intuitblog.com\/?p=4668"},"modified":"2025-12-19T12:18:06","modified_gmt":"2025-12-19T20:18:06","slug":"how-ai-can-help-build-resiliency-for-small-businesses-in-a-global-economic-crisis","status":"publish","type":"post","link":"https:\/\/www.intuit.com\/blog\/innovative-thinking\/how-ai-can-help-build-resiliency-for-small-businesses-in-a-global-economic-crisis\/","title":{"rendered":"How AI Can Help Small Businesses (during a Global Economic Crisis)"},"content":{"rendered":"<p>In 2020, the economy is once again teetering on the edge of what some are calling a <a href=\"https:\/\/www.cnn.com\/2020\/07\/27\/economy\/economy-fiscal-cliff\/index.html\" target=\"_blank\">fiscal cliff<\/a>.<\/p>\n<p>In the midst of COVID-19, a global economic crisis is threatening the livelihoods of small business owners everywhere. In ordinary times, 50 percent of small businesses go out of business in the first 5 years. In today\u2019s extraordinary times, nearly 7.5 million (~25 percent) of small businesses in the U.S. alone have been at risk of closing permanently in a matter of months (Source:\u00a0<a href=\"https:\/\/higherlogicdownload.s3.amazonaws.com\/NMSC\/390e0055-2395-4d3b-af60-81b53974430d\/UploadedImages\/Resource_Center\/COVID_19\/NMSC57_MSA_COVID19IMAPCTSURVEY_F.pdf\" target=\"_blank\">Main Street America&#8217;s Small Business Survey 2020<\/a><a href=\"https:\/\/higherlogicdownload.s3.amazonaws.com\/NMSC\/390e0055-2395-4d3b-af60-81b53974430d\/UploadedImages\/Resource_Center\/COVID_19\/NMSC57_MSA_COVID19IMAPCTSURVEY_F.pdf\" target=\"_blank\">)<\/a><a href=\"https:\/\/higherlogicdownload.s3.amazonaws.com\/NMSC\/390e0055-2395-4d3b-af60-81b53974430d\/UploadedImages\/Resource_Center\/COVID_19\/NMSC57_MSA_COVID19IMAPCTSURVEY_F.pdf\" target=\"_blank\">.<\/a><\/p>\n<p>Among the all-time top contributing factors to small business survival? Cash flow management and access to capital. Whether in the midst of a pandemic or a devastating life event, business owners are more likely to successfully make it through tumultuous times if they can:<\/p>\n<ul>\n<li>Efficiently manage the inflow and outflow of cash<\/li>\n<li>Understand their cash position to qualify for government aid and\/or loans with flexible borrowing terms<\/li>\n<li>Use financial insights to map out revenue and spending scenarios<\/li>\n<\/ul>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-4819 \" src=\"https:\/\/intuitblog.com\/wp-content\/uploads\/2020\/08\/Screen-Shot-2020-08-21-at-11.18.19-AM.png\" alt=\"\" width=\"664\" height=\"348\" \/><\/p>\n<p>To that end, our company immediately took steps at the outset of the pandemic to help consumers and small businesses access desperately-needed capital through <a href=\"https:\/\/www.intuit.com\/blog\/news-social\/supporting-consumers-small-businesses-and-communities-as-we-face-covid-19-together\/\" >four key initiatives<\/a> and <a href=\"https:\/\/www.intuit.com\/blog\/news-social\/intuit-launches-new-innovations-to-help-consumers-and-small-businesses-with-u-s-government-aid-and-relief-programs\" >AI-driven innovations<\/a>, such as <a href=\"https:\/\/www.intuit.com\/blog\/technology\/knowledge-engineering-simplifies-cares-act-for-small-business-access-to-ppp-tax-credits\/\" >Intuit Aid Assist<\/a> and the <a href=\"https:\/\/quickbooks.intuit.com\/small-business\/coronavirus\/paycheck-protection-program\/\" target=\"_blank\">QuickBooks Capital<\/a>\u00a0for Paycheck Protection Program.<\/p>\n<p>As part of Intuit\u2019s Small Business and Self-Employed Group, it\u2019s my team\u2019s responsibility to apply artificial intelligence (AI) and large-scale machine learning (ML) to help small business owners build financial resilience in the face of economic fluctuations.<\/p>\n<p>Sadly, as data scientists we can no longer look at global economic crises as anomalies. Just a dozen years ago, we witnessed the worst economic disaster since the\u00a0<a href=\"https:\/\/www.thebalance.com\/stock-market-crash-of-1929-causes-effects-and-facts-3305891\" target=\"_blank\">Great Depression of 1929<\/a> with the <a href=\"https:\/\/www.washingtonpost.com\/business\/economy\/a-guide-to-the-financial-crisis--10-years-later\/2018\/09\/10\/114b76ba-af10-11e8-a20b-5f4f84429666_story.html\" target=\"_blank\">Great Recession of 2008<\/a>. And, the World Bank <a href=\"https:\/\/www.worldbank.org\/en\/news\/feature\/2020\/06\/08\/the-global-economic-outlook-during-the-covid-19-pandemic-a-changed-world\" target=\"_blank\">expects the pandemic to plunge most countries into recession in 2020<\/a>.<\/p>\n<p>The frequency of such crises has implications for AI and machine learning itself. Meaning, we can\u2019t just remove them from our models, smooth over the results, and assume that business as usual will prevail. We have to factor these events into our work, building them into a more realistic approach.<\/p>\n<p>With that in mind, my AI\/ML team has been working with Intuit development teams to create a cash flow planner to help our customers make better decisions about money-in and money-out by analyzing their historical patterns to project what\u2019s likely to happen. In this blog, I\u2019ll offer a glimpse into that work, the results we\u2019ve achieved so far, and the work still to come.<\/p>\n<p><strong>Solving the cash flow puzzle<br \/>\n<\/strong>When we first began work on our <a href=\"https:\/\/quickbooks.intuit.com\/learn-support\/en-us\/payments-account\/use-the-cash-flow-planner-to-predict-future-income-and-expenses\/00\/470980\" target=\"_blank\">QuickBooks Online Cash Flow planner<\/a>, we had a simple goal to enable our small business and self-employed customers to see their current cash balance, understand where it came from, and project it forward.<\/p>\n<p>Turns out it wasn\u2019t so simple.<\/p>\n<p>When you look at the money coming in and going out of a business, the patterns can be idiosyncratic and vary dramatically over time. Some cash streams are periodic. Some are random. Some are never repeated. Some streams are actually aggregates of other individual streams. And we\u2019re not just talking about cash. There are customer invoices payable at uncertain times, bills due on different schedules, and dependencies among income, taxes, sales, and payroll.<\/p>\n<p>All of which needs to be modeled out.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-4820 size-full\" src=\"https:\/\/intuitblog.com\/wp-content\/uploads\/2020\/08\/Screen-Shot-2020-08-21-at-11.20.56-AM.png\" alt=\"\" width=\"1812\" height=\"881\" srcset=\"https:\/\/intuitblog.com\/wp-content\/uploads\/2020\/08\/Screen-Shot-2020-08-21-at-11.20.56-AM.png 1812w, https:\/\/intuitblog.com\/wp-content\/uploads\/2020\/08\/Screen-Shot-2020-08-21-at-11.20.56-AM.png?resize=150,73 150w, https:\/\/intuitblog.com\/wp-content\/uploads\/2020\/08\/Screen-Shot-2020-08-21-at-11.20.56-AM.png?resize=300,146 300w, https:\/\/intuitblog.com\/wp-content\/uploads\/2020\/08\/Screen-Shot-2020-08-21-at-11.20.56-AM.png?resize=768,373 768w, https:\/\/intuitblog.com\/wp-content\/uploads\/2020\/08\/Screen-Shot-2020-08-21-at-11.20.56-AM.png?resize=1024,498 1024w, https:\/\/intuitblog.com\/wp-content\/uploads\/2020\/08\/Screen-Shot-2020-08-21-at-11.20.56-AM.png?resize=1536,747 1536w, https:\/\/intuitblog.com\/wp-content\/uploads\/2020\/08\/Screen-Shot-2020-08-21-at-11.20.56-AM.png?resize=740,360 740w, https:\/\/intuitblog.com\/wp-content\/uploads\/2020\/08\/Screen-Shot-2020-08-21-at-11.20.56-AM.png?resize=1440,700 1440w, https:\/\/intuitblog.com\/wp-content\/uploads\/2020\/08\/Screen-Shot-2020-08-21-at-11.20.56-AM.png?resize=720,350 720w\" sizes=\"auto, (max-width: 1812px) 100vw, 1812px\" \/><\/p>\n<p style=\"text-align: center;\"><strong>QuickBooks invoice prediction in action<\/strong><\/p>\n<p>So instead of simply aggregating a bunch of <a href=\"https:\/\/en.wikipedia.org\/wiki\/Time_series#:~:text=A%20time%20series%20is%20a,equally%20spaced%20points%20in%20time.&amp;text=Time%20series%20forecasting%20is%20the,based%20on%20previously%20observed%20values\" target=\"_blank\">time series<\/a> data points together to build a forecast, we discovered that we\u2019d need to construct individual models for every single transaction stream, identify the patterns in each of those streams, build models that generate predictions, and aggregate those predictions by type and time. Only then would we be able to answer a question like, \u201cWill I be able to make payroll this week?\u201d or \u201cCan I spend this money today?\u201d or \u201cIs my cash about to run low?\u201d<\/p>\n<p><strong>Pivoting quickly to support small businesses<br \/>\n<\/strong>At this distressing time for small businesses all over the world, our AI\/ML team looked closely at whether we were building the righ models and solving the right problems within Intuit\u2019s <a href=\"https:\/\/quickbooks.intuit.com\/learn-support\/en-us\/payments-account\/use-the-cash-flow-planner-to-predict-future-income-and-expenses\/00\/470980\" target=\"_blank\">QuickBooks Online Cash Flow planner<\/a> to address our customers\u2019 most pressing financial challenges.<\/p>\n<blockquote><p><em>\u201cIf the virus doesn&#8217;t kill me, the stress is gonna kill me. You&#8217;re afraid for your physical health, you&#8217;re afraid for your financial health.\u201d<\/em><\/p>\n<p><em>\u201cMy entire industry has had the rug pulled out from under it.\u201d<\/em><\/p>\n<p><em>\u201cMost of us don&#8217;t really have a lot of cash on hand to survive something like this where you have no revenue coming in.\u201d<\/em><\/p><\/blockquote>\n<p><strong>Invoices: turning payments due into cash<br \/>\n<\/strong>A key aspect of the larger cash flow forecasting problem is helping small businesses predict when payments due will turn into cash. We view this as a two-stage question. First, will this customer invoice actually be paid? Secondly, when will it be paid? From <a href=\"https:\/\/quickbooks.intuit.com\/online\/\" target=\"_blank\">QuickBooks Online<\/a> data, we noticed the tendency for small businesses that generate a lot of invoices to struggle with getting paid on time. Some customers pay late and others require extensive follow-up to pay at all.<\/p>\n<p>To address the <strong>first stage<\/strong> of the problem, we built a classification model to predict whether a given invoice is likely to be paid. We based it on a standard <a href=\"https:\/\/en.wikipedia.org\/wiki\/Gradient_boosting\" target=\"_blank\">gradient boosted tree model<\/a> with <a href=\"https:\/\/www.kaggle.com\/eikedehling\/exploring-class-imbalance-resampling-and-weights\" target=\"_blank\">class weight tuning to address label imbalance<\/a>. For the timing question, we needed to be able to predict when customers were likely to pay.<\/p>\n<p>This took a certain amount of nuance. We didn\u2019t want to err on the side of false positives, which could result in time-consuming follow-up with customers who were likely to pay anyway, and inadvertently eroding the trusted relationship between the business and its customers. On the other hand, we didn\u2019t want to let invoices linger any longer than necessary.<\/p>\n<p>So, to address the <strong>second stage<\/strong> of the problem, we used gradient boosted trees with quantile loss to model different <a href=\"https:\/\/en.wikipedia.org\/wiki\/Confidence_interval\" target=\"_blank\">confidence levels<\/a> for the difference between the due date and the payment date. This way, we were able to train, test, and validate the model using the company\u2019s own temporal data, build company and client temporal features, and apply the appropriate label to each invoice.<\/p>\n<p>Here\u2019s an example of what the final result could look like for a QuickBooks Online business owner.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-4821 size-full\" src=\"https:\/\/intuitblog.com\/wp-content\/uploads\/2020\/08\/Screen-Shot-2020-08-21-at-11.23.42-AM.png\" alt=\"\" width=\"1825\" height=\"1237\" srcset=\"https:\/\/intuitblog.com\/wp-content\/uploads\/2020\/08\/Screen-Shot-2020-08-21-at-11.23.42-AM.png 1825w, https:\/\/intuitblog.com\/wp-content\/uploads\/2020\/08\/Screen-Shot-2020-08-21-at-11.23.42-AM.png?resize=150,102 150w, https:\/\/intuitblog.com\/wp-content\/uploads\/2020\/08\/Screen-Shot-2020-08-21-at-11.23.42-AM.png?resize=300,203 300w, https:\/\/intuitblog.com\/wp-content\/uploads\/2020\/08\/Screen-Shot-2020-08-21-at-11.23.42-AM.png?resize=768,521 768w, https:\/\/intuitblog.com\/wp-content\/uploads\/2020\/08\/Screen-Shot-2020-08-21-at-11.23.42-AM.png?resize=1024,694 1024w, https:\/\/intuitblog.com\/wp-content\/uploads\/2020\/08\/Screen-Shot-2020-08-21-at-11.23.42-AM.png?resize=1536,1041 1536w, https:\/\/intuitblog.com\/wp-content\/uploads\/2020\/08\/Screen-Shot-2020-08-21-at-11.23.42-AM.png?resize=740,502 740w, https:\/\/intuitblog.com\/wp-content\/uploads\/2020\/08\/Screen-Shot-2020-08-21-at-11.23.42-AM.png?resize=1440,976 1440w, https:\/\/intuitblog.com\/wp-content\/uploads\/2020\/08\/Screen-Shot-2020-08-21-at-11.23.42-AM.png?resize=720,488 720w\" sizes=\"auto, (max-width: 1825px) 100vw, 1825px\" \/><\/p>\n<p style=\"text-align: center;\"><strong>QuickBooks Online invoice prediction in action<\/strong><\/p>\n<p>As you can see in this illustration, when they go into the Cash Flow planner to review a forecast of transactions, a light bulb will flag any invoices that are \u201clikely to be paid late.\u201d When they click on it, the original due date and two possible payment dates will appear\u2014one with a 50 percent likelihood of being paid on time, and another with an 80 percent likelihood of being paid. By offering a range of <a href=\"https:\/\/en.wikipedia.org\/wiki\/Confidence_interval\" target=\"_blank\">confidence intervals<\/a>, we instill confidence with our customers that they can act on the information we\u2019ve provided to better predict their cash flow. Moreover, we enable them to understand how we\u2019ve arrived at the recommendation. In customer interviews, we learned that \u201cshowing our work\u201d was an important feature.<\/p>\n<p>But, the invoice problem is just one challenge our AI\/ML team is tackling for our customers.<\/p>\n<p><strong>Modeling small business sales trends<br \/>\n<\/strong>When we launched Intuit\u2019s\u00a0 <a href=\"https:\/\/quickbooks.intuit.com\/learn-support\/en-us\/payments-account\/use-the-cash-flow-planner-to-predict-future-income-and-expenses\/00\/470980\" target=\"_blank\">QuickBooks Online Cash Flow planner<\/a> in January 2020, we couldn\u2019t have imagined what was ahead for small businesses around the world, in just a few short weeks.<\/p>\n<p>With the onset of COVID-19 in March, we witnessed a precipitous decline in our customers\u2019 small business sales. Many service businesses experienced a 100 percent drop in sales (e.g., beauty salons), while some product-focused businesses were able to gradually transition to online sales models.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-4822 size-full\" src=\"https:\/\/intuitblog.com\/wp-content\/uploads\/2020\/08\/Screen-Shot-2020-08-21-at-11.25.03-AM.png\" alt=\"\" width=\"1525\" height=\"704\" srcset=\"https:\/\/intuitblog.com\/wp-content\/uploads\/2020\/08\/Screen-Shot-2020-08-21-at-11.25.03-AM.png 1525w, https:\/\/intuitblog.com\/wp-content\/uploads\/2020\/08\/Screen-Shot-2020-08-21-at-11.25.03-AM.png?resize=150,69 150w, https:\/\/intuitblog.com\/wp-content\/uploads\/2020\/08\/Screen-Shot-2020-08-21-at-11.25.03-AM.png?resize=300,138 300w, https:\/\/intuitblog.com\/wp-content\/uploads\/2020\/08\/Screen-Shot-2020-08-21-at-11.25.03-AM.png?resize=768,355 768w, https:\/\/intuitblog.com\/wp-content\/uploads\/2020\/08\/Screen-Shot-2020-08-21-at-11.25.03-AM.png?resize=1024,473 1024w, https:\/\/intuitblog.com\/wp-content\/uploads\/2020\/08\/Screen-Shot-2020-08-21-at-11.25.03-AM.png?resize=740,342 740w, https:\/\/intuitblog.com\/wp-content\/uploads\/2020\/08\/Screen-Shot-2020-08-21-at-11.25.03-AM.png?resize=1440,665 1440w, https:\/\/intuitblog.com\/wp-content\/uploads\/2020\/08\/Screen-Shot-2020-08-21-at-11.25.03-AM.png?resize=720,332 720w\" sizes=\"auto, (max-width: 1525px) 100vw, 1525px\" \/><\/p>\n<p style=\"text-align: center;\"><strong>Small business sales have been significantly impacted by COVID-19<br \/>\n<\/strong>[Representative Mean of Weekly Sales for a sample companies selected<br \/>\nfrom major cities across industrial sectors hit by the pandemic.]<\/p>\n<p>Before the global pandemic, our AI\/ML team built models for small business sales, taking into account businesses with a steady stream of sales, as well as those with little data or sporadic sales patterns. For example, an ice cream shop with a high volume of sales throughout the year, or a Halloween shop with seasonal sales that peak annually and account for the bulk of the business.<\/p>\n<p>In the first scenario, it was relatively straightforward to build a time series model, working with a rich data set from a predictable sales curve. In contrast, the second scenario was much more difficult to model, due to sparse data from sporadic sales patterns or seasonal sales peaks.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-4834 size-full\" src=\"https:\/\/intuitblog.com\/wp-content\/uploads\/2020\/08\/Screen-Shot-2020-08-21-at-3.30.16-PM.png\" alt=\"\" width=\"1751\" height=\"1086\" srcset=\"https:\/\/intuitblog.com\/wp-content\/uploads\/2020\/08\/Screen-Shot-2020-08-21-at-3.30.16-PM.png 1751w, https:\/\/intuitblog.com\/wp-content\/uploads\/2020\/08\/Screen-Shot-2020-08-21-at-3.30.16-PM.png?resize=150,93 150w, https:\/\/intuitblog.com\/wp-content\/uploads\/2020\/08\/Screen-Shot-2020-08-21-at-3.30.16-PM.png?resize=300,186 300w, https:\/\/intuitblog.com\/wp-content\/uploads\/2020\/08\/Screen-Shot-2020-08-21-at-3.30.16-PM.png?resize=768,476 768w, https:\/\/intuitblog.com\/wp-content\/uploads\/2020\/08\/Screen-Shot-2020-08-21-at-3.30.16-PM.png?resize=1024,635 1024w, https:\/\/intuitblog.com\/wp-content\/uploads\/2020\/08\/Screen-Shot-2020-08-21-at-3.30.16-PM.png?resize=1536,953 1536w, https:\/\/intuitblog.com\/wp-content\/uploads\/2020\/08\/Screen-Shot-2020-08-21-at-3.30.16-PM.png?resize=740,459 740w, https:\/\/intuitblog.com\/wp-content\/uploads\/2020\/08\/Screen-Shot-2020-08-21-at-3.30.16-PM.png?resize=1440,893 1440w, https:\/\/intuitblog.com\/wp-content\/uploads\/2020\/08\/Screen-Shot-2020-08-21-at-3.30.16-PM.png?resize=720,447 720w\" sizes=\"auto, (max-width: 1751px) 100vw, 1751px\" \/><\/p>\n<p style=\"text-align: center;\"><strong>Modeling sparse data with hurdle models<\/strong><\/p>\n<p>So, how did we reconcile these two worlds?\u00a0 We used <a href=\"https:\/\/data.library.virginia.edu\/getting-started-with-hurdle-models\/#:~:text=The%20hurdle%20model%20is%20a,way%2C%20a%20hurdle%20is%20cleared\" target=\"_blank\">hurdle models<\/a>, which can handle \u201czero count\u201d events when no business is coming in (e.g., holidays, certain days when a business is closed, seasonal businesses only open during certain months of the year).\u00a0 This approach gives us an extra level of flexibility for smoothing out the data to effectively support cash flow planning scenarios for a range of business models.<\/p>\n<p>But, as we\u2019ve discovered all too well\u2014and especially in the times of COVID-19\u2014we no longer live in a world in which we can treat economic crises as once-in-a-generation events. What were anomalies in the past now need to be factored into our approach to AI\/ML models.<\/p>\n<p>So, that\u2019s what we\u2019re doing. Dealing with data from the past few months to model this out.\u00a0 We\u2019re exploring new algorithms that can handle sudden shifts and changes to help small business owners be able to say, \u201cOkay, I\u2019m going through this really tough time, and need to understand what my sales and revenue will look like in the future.\u00a0 I know that the rate and volume of sales won\u2019t be where it was before, but can my business survive through the end of the year as the economy begins to open back up?\u201d<\/p>\n<p>As data scientists here at Intuit, what we find most satisfying is tackling really big problems that make a difference in the everyday lives of people. That\u2019s why our AI\/ML team has been so pleased to hear from customers\u00a0 that our cash flow planner is doing just that\u2014and why we\u2019re so committed to seeing them through to a better day.<\/p>\n<blockquote><p><em>\u201cIt&#8217;s critical, because it lets us know how we&#8217;re doing, are we financially healthy, if the next few months are going to be difficult, if there are purchases that don&#8217;t need to be made.\u201d<\/em><\/p>\n<p><em>\u201cIt helped me tremendously to be able to juggle expenses with people. I was able to not have to lay off anybody, and it helped us to identify which people would be willing to stretch my payroll until orders get placed again.\u201d<\/em><\/p><\/blockquote>\n<p><strong>What\u2019s next?<br \/>\n<\/strong>As you can see in this blog, the cash flow problem is incredibly complex. When we set out on this innovation journey, we built our models for the US population. During the pandemic, we opened up the cash flow planner to reach a broader audience of small business customers in the UK and Australia, where we also serve small businesses with QuickBooks Online.<\/p>\n<p>There are so many different types of small business circumstances to take into consideration\u2014and distinct cash flow streams requiring finely-tuned individual models\u2014each with the possibility of making a huge difference in the financial health of small business owners.<\/p>\n<p>The same is true for consumers, like you and me.<\/p>\n<p>In the future, we\u2019ve set our sights on expanding the cash flow planner from small businesses to consumers. With access to intuitive AI-driven personal finance tools, more people will\u00a0 have the information they need to better manage the ebbs and flows of their cash flow. Even better,\u00a0 they\u2019ll be untethered from the tedium of manual spreadsheets for greater efficiency and accuracy.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In 2020, the economy is once again teetering on the edge of what some are calling a fiscal cliff. In the midst of COVID-19, a global economic crisis is threatening the livelihoods of small business owners everywhere. In ordinary times, 50 percent of small businesses go out of business in the first 5 years. In<\/p>\n","protected":false},"author":151418277,"featured_media":4826,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"rkv_browse_by_id":0,"rkv_cta_id":0,"rkv_optimize_for_pagespeed":false,"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"useModifiedDate":false,"customPublishDate":"","_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[237496403,237496405],"tags":[],"intuit_collection":[],"intuit_series":[],"coauthors":[237495827,237496056],"class_list":["post-4668","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-innovative-thinking","category-thought-leadership"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.1.1 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>How AI Can Help Small Businesses - Intuit Blog<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.intuit.com\/blog\/innovative-thinking\/how-ai-can-help-build-resiliency-for-small-businesses-in-a-global-economic-crisis\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How AI Can Help Small Businesses - Intuit Blog\" \/>\n<meta property=\"og:description\" content=\"In 2020, the economy is once again teetering on the edge of what some are calling a fiscal cliff. In the midst of COVID-19, a global economic crisis is threatening the livelihoods of small business owners everywhere. In ordinary times, 50 percent of small businesses go out of business in the first 5 years. 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