AI has become so accessible that the benefit of early adoption has effectively disappeared. Instead, tax leaders face the risk of falling behind their competitors if they don’t invest time and resources in cleansing their data and starting their own AI programs.
The survey shows that while decades-old tech, such as business intelligence tools, have been adopted by 70% of businesses, newer end-user data-cleansing tools have already been adopted by 51%. In another EY report, more than 80% of respondents said their tax teams will pivot from a technical tax focus to an emphasis on data, process and tech skills over the next three years.
Many of the algorithms that have an immediate impact on tax have been around for a long time and do not offer a competitive advantage, says Campbell. Tax groups that have been slow to embrace AI should use these tools to tackle “low-hanging fruit”—namely, document intelligence and classification.
Every time-poor tax practitioner knows the frustration associated with laboriously picking through invoices, looking for key tax data buried within documents. Document intelligence can now complete the process automatically, accurately, at speed, and at scale, thanks to powerful machine learning algorithms and pattern recognition. Having automatically retrieved key facts, AI can then fast-track classification.
“Tax teams spend too much time simply trying to classify things and put them into tax buckets,” says Campbell. “And those buckets determine tax treatments such as VAT, sales-and-use, whether something is taxable or exempt, and much more – the list is exhaustive. This is a serious drain on time and resources, but it’s straightforward for AI.”
Human tax teams and AI working together
Campbell cites a multi-part example in which AI triggered a dramatic increase in performance. The first part was a monthly compliance exercise related to sales-and-use tax in which a tax team was asked to categorize 15,000 transactions.
The bulk of the heavy lifting was carried out by an offshore shared services center. This took 30 hours before a three-hour final review back in the US. Overall, this 33-hour manual classification process was 95-97% accurate.
“At 97%, the AI model was just as accurate,” says Campbell, “but it completed the heavy lifting in five seconds rather than 30 hours.” Manual review time was also shortened. “AI classifications meant that human reviewers could target high-dollar amounts that had a lower confidence rating, making the review more focused, more effective and faster.”
The AI was also much more consistent than a human reviewer. If it found one incorrectly classified transaction, it was likely all other similar transactions would be wrong, so it was much easier to spot errors. This helped cut the human review period by half.
How COVID-19 strengthens the case for tax AI
As governments rush through tax stimuli to kick-start COVID-19-impacted economies, changes are being enacted in weeks that would previously have kept legislators busy for years. C-suites and visionary tax leaders are rapidly recognizing that this pace of change calls for a radical rethinking of tax tools.
Nowhere is this more pertinent than in the realm of tax forecasting, much of which has traditionally been achieved manually based on gut feeling rather than data. In the wake of a crisis such as COVID-19, however, it’s very difficult for companies to adjust their models because there’s so much manual work involved.
But with clean, structured data and an end-to-end data strategy for forecasting, businesses can input new data into their AI model and produce a fresh forecast in minutes. As a crisis or other unusual situation develops, companies can quickly and continually update forecasts with new data.
Prior to the pandemic, an EY report showed 84% of firms expected an increase in workload due to new digital tax filing requirements. Post-pandemic, it’s reasonable to expect the situation will be even more onerous.
AI’s proactive tax strategy recommendations
The tax benefits of AI go far beyond document intelligence and classification. Really exciting things start to happen when teams use AI to assist with their tax planning. This involves a range of data inputs—including regulatory law, company performance data and corporate strategy—fed into an AI model so that it can proactively make tax recommendations.
For example, the AI model might highlight a regulatory change in Spain that affects telecom companies and then offer suggestions on how to best manage the tax implications. Alternatively, a company might want to alter its tax strategy to free up more cash. In this case, the AI could offer a series of recommendations to achieve that goal.
“The promise of AI is that it will take care of mundane tasks,” says Campbell. “Businesses can then focus on developing business strategies and understanding the tax implications. To be successful, businesses will need to embed AI into everything they do. I think that’s the opportunity.”