AI Research
Get ready for fracking, Reform UK tells energy firms
Political reporter
PA MediaTrapped in underground rocks, a potential energy resource has eluded generations of British politicians.
It’s called shale gas and the method of getting it out of the ground, known as fracking, has proved politically difficult.
Fracking, short for hydraulic fracturing, has been banned many times by different prime ministers since 2011 over concerns about earthquakes and environmental impacts.
And yet despite this, Reform UK – which is leading in national opinion polls – believe it’s worth going after the gas again.
“We’ve got potentially hundreds of billions of energy treasure in the form of shale gas,” Richard Tice, the party’s deputy leader and energy spokesperson, says.
“It’s grossly financially negligent to a criminal degree to leave that value underground and not to extract it.”
The party led by Nigel Farage is telling energy firms to get ready to “drill, baby, drill” if it gets into government after the next general election.
Reform UK says it’s serious about shale – but will its plans succeed where so many others have failed?
The history of fracking in the UK shows it won’t be easy.
Shale fail
Fracking has been going on in the British oil and gas sector for decades and largely flew under the political radar until about 2010, when shale gas extraction started taking off in the United States.
At the time, Charles Hendry was the energy minister and at first, he was cautiously optimistic about the prospects for a US-style shale gas boom.
But the abortive fracking efforts of the last decade or so have turned the former Tory MP into a sceptic.
He watched former Prime Minister David Cameron’s dash for the gas slow to a crawl after fracking projects were hampered by planning delays, minor earthquakes, legal challenges and persistent protests.
As a result, Cameron’s promised “shale gas revolution” never materialised and in 2019, tremors were recorded at a fracking site, leading to a ban.
“When ambition hit reality, it wasn’t what people had hoped for,” Hendry says. “It is much more difficult than it is in the US. It’s more expensive. It’s more polluting. It’s more disruptive.”
The former minister says there was simply less space for fracking in the UK than in the US and consequently more obstacles in the way.
Given the level of opposition to removing those barriers, Hendry says, Reform UK’s zeal for shale gas was mistaken.
“Even Reform voters will be up in arms about the idea,” Hendry predicts.
The most recent attempt to get fracking going again is another cautionary tale.
When former Prime Minister Liz Truss last lifted the fracking ban in 2022, opposition MPs forced a vote on the issue and although her government won, a major rebellion by Tory MPs shook confidence in her leadership and she resigned the next day.
Her successor, Rishi Sunak, reinstated the moratorium on fracking and now the Labour government says it intends to ban the practice permanently.
So, how would Reform UK navigate this political minefield?
PAFirstly, Tice says, Reform UK would lift any fracking ban immediately.
Secondly, he says, a Reform UK government would work with oil and gas companies using new extraction techniques to explore for shale gas at a couple of independently monitored fracking wells.
“That will confirm the quantity of gas available and satisfy people that it’s safe,” Tice says.
The British Geological Survey (BGS) has identified four areas where there’s potential for commercial shale gas extraction, with the largest spanning Lancashire and several countries in the Midlands.
In one prominent 2013 study, the BGS said more testing was needed “to prove that shale gas development is technically and economically viable” in the UK. Even then, the BGS said, “it is far from certain that the conditions that underpin shale gas production in North America will be replicable in the UK”.
Despite this uncertainty, Tice is bullish and says they’d know within two years of testing whether fracking for shale gas was worthwhile in the UK.
But unlike Labour, which promised to spend £28bn a year on green energy, Reform UK wouldn’t invest any public money in fracking.
“The government’s job is to create an attractive regulatory and tax framework,” Tice says.
Although the next general election is potentially years off yet, Tice and Farage have already been laying the groundwork for their fracking resurrection in meetings with oil and gas firms.
In one such meeting in Aberdeen, Scotland a few months ago, Tice and Farage told these companies to prepare applications for licences to look for shale gas.
“Don’t write off Britain,” Tice told them. “Keep us in mind, and in the run up to the next general election, you should be getting your ducks in a row and getting applications ready.”
GettyReform UK’s Andrea Jenkyns, the Lincolnshire mayor, is also a fracking enthusiast and recently met Egdon Resources, an oil and gas firm that has licences for targeting shale gas in an area known as the Gainsborough Trough.
The company has been touting analysis by accounting firm Deloitte, which estimates that gas in the trough could be worth £140bn to the UK economy and create 250,000 jobs.
The Deloitte assessment is not public and Egdon Resources would not share a copy with the BBC.
Mark Abbott, the CEO of Egdon Resources, says the company would look to invest millions in shale gas “if the regulatory environment allowed that”.
He says that “clearly Reform has a policy which is more supportive than we’ve seen for a while”.
Star Energy Group is another company that has interests in areas with potential shale deposits.
Its CEO Ross Glover has met Reform UK councillors in Lincolnshire, where the party controls the county council.
“We know there’s a world class resource there,” Glover says. “I believe the UK needs whatever indigenous energy it can get, be it wind, solar, geothermal.”
Energy transition
For Michael Bradshaw, a professor of global energy at the Warwick Business School, there’s a feeling of déjà vu.
He’s been studying fracking policies for years and says last time, the industry made no progress.
“The work carried out by the geologists in our research programme suggested that the complex geology of the shale basins in the UK would make it significantly more difficult and costly to extract,” Bradshaw says.
He says the estimated expense of producing British shale may mean it would not be able to compete with cheaper gas imported from Norway or elsewhere.
As a result, Professor Bradshaw says, energy bills would not be lower in the short term.
In any case, with Labour in government, fracking is a non-starter.
Labour is focusing on building out the UK’s renewable energy and is aiming for clean power to meet 100% of electricity demand by 2030.
Energy Minister Miatta Fahnbulleh says Labour was going all out to “seize the opportunities of the clean energy transition”.
“We intend to ban fracking for good and make Britain a clean energy superpower to protect current and future generations,” Fahnbulleh says.
“The biggest risk to our energy security is staying dependent on fossil fuel markets and only by sprinting to clean power by 2030 can the UK take back control of its energy and protect consumers from spiralling energy costs.”
Although fossil fuels are expected to be a feature of the UK’s energy and economic systems for years to come, their role is diminishing as countries turn to cleaner alternatives.
If a Reform government does pivot back to fracking in 2029, it might find itself out of step in a world that’s trying to go green.
Axis Communications announces the launch of 12 ruggedized indoor and outdoor-ready dome cameras. Based on the ARTPEC-9 chipset, they offer high performance and advanced analytics. AXIS P32 Series offers excellent image quality up to 8 MP. The cameras are equipped with Lightfinder 2.0 and Forensic WDR, ensuring true color and sharp detail even in near total darkness or difficult lighting situations. In addition, OptimizedIR technology enables complete surveillance even in absolute darkness.
Based on the latest platform from Axis, these artificial intelligence dome cameras offer accelerated performance and run advanced analytics applications directly at the edge. For example, they include pre-installed AXIS Object Analytics for detecting, classifying, tracking and counting people, vehicles and vehicle types. They also have AXIS Image Health Analytics, so users receive notifications if an image is blocked, degraded, underexposed or redirected.
Furthermore, some models include an acoustic sensor with AXIS Audio Analytics pre-installed. This alerts users even in the absence of visual cues by detecting shouts, screams or changes in sound level.
Key features:
- Outstanding image quality, up to 8 MP;
- Indoor and outdoor models;
- Variants with pre-installed AXIS Audio Analytics;
- Options with different lens types;
- Integrated cybersecurity through Axis Edge Vault.
These rugged, vandal- and shock-resistant cameras include both indoor and outdoor versions, with outdoor models operating in an extended temperature range from -40°C to +50°C.
In addition, Axis Edge Vault, the hardware cybersecurity platform, protects the device and provides secure storage and key operations, certified to FIPS 140-3 Level 3.
AI Research
Breaking Down AI’s Role in Genomics and Polygenic Risk Prediction – with Dan Elton of the National Human Genome Research Institute
While protein sequencing efforts have amassed hundreds of millions of protein variants, experimentally determined structures remain exceedingly rare, lagging far behind the number of unresolved structures.
The 2024 UniProt knowledgebase catalogs approximately 246 million unique protein sequences, yet the Worldwide Protein Data Bank holds just over 227,000 experimentally determined three-dimensional structures — covering less than 0.1% of known proteins.
De novo structure elucidation remains a prohibitively expensive and time-intensive endeavor. According to a peer-reviewed article in Bioinformatics, the average cost of X-ray crystallization is estimated at $150,000 per protein.
Even with an annual Protein Data Bank throughput exceeding 200,000 new structures, laboratory workflows struggle to keep pace with the relentless pace of sequence discovery, leaving critical drug targets and novel enzymes structurally uncharacterized.
By harnessing deep learning algorithms to predict three-dimensional conformations from primary sequences, AI-driven models like AlphaFold collapse months of crystallographic work into minutes, directly bridging the gap between sequence abundance and structural insight.
Emerj Editorial Director Matthew DeMello recently spoke with Dan Elton, Staff Scientist at the National Human Genome Research Institute, on the ‘AI in Business’ podcast to discuss how AI is revolutionizing protein structure prediction. Elton concentrates on AI-driven protein engineering and neural-network polygenic risk scoring, outlining a vision for how technology can compress R&D timelines and sharpen disease prediction.
Precision health leaders reading this article will find a clear and concise breakdown of critical takeaways from their conversation in two key areas of AI deployment:
- Enhancing polygenic risk stratification: Applying deep learning and neural networks to model nonlinear gene interactions, thereby sharpening disease-risk predictions
- Improving rapid structure elucidation: Employing AI-driven protein folding models to predict three-dimensional protein conformations from amino-acid sequences in minutes, slashing timelines for drug discovery and bespoke enzyme engineering
Listen to the full episode below:
Guest: Dr. Dan Elton, Staff Scientist, National Institutes of Health
Expertise: Artificial Intelligence, Deep Learning, Computational Physics
Brief Recognition: Dr. Dan Elton is currently the Staff Scientist at the National Human Genome Research Institute under the National Institutes of Health. Previously, he worked for the Mass General Brigham, where he looked after the deployment and testing of AI systems in the radiology clinic. He earned his Doctorate in Physics in 2016 from Stony Brook University.
Improving Rapid Structure Elucidation
Traditional structural biology methods have long constrained drug discovery and enzyme design workflows. Elton notes that determining a protein’s three-dimensional structure was an extremely difficult problem.
According to Elton, AlphaFold — an artificial intelligence system that predicts the three-dimensional structure of proteins from their amino acid sequences — bypasses these labor-intensive physics simulations by training deep neural architectures on evolutionary and sequence co-variation patterns. It ultimately collapses weeks of bench work into minutes on modern GPU clusters.
Elton explains that open-access folding databases now host over 200 million predicted structures, democratizing discovery by granting small labs the same AI-driven insights previously limited to large pharmaceutical R&D centers.
By collapsing months of laborious X-ray crystallography or NMR experiments into minutes on a modern GPU cluster, companies can now screen thousands of candidate molecules in silico, iterating designs with agility.
Elton emphasizes that this agility not only accelerates lead optimization but also reallocates experimental budgets toward functional assays and ADMET profiling.
Key AI data inputs include:
- Amino acid sequences paired with multiple sequence alignments to capture evolutionary constraints
- Deep learning models that predict residue-level confidence scores (pLDDT) and contact maps
- High-throughput in silico mutagenesis for de novo enzyme design and stability screening
Broadly, integrating AI predictions with targeted experimental workflows has slashed cost-per-structure metrics by orders of magnitude.
This computational acceleration proves particularly valuable for neglected diseases, where the Drugs for Neglected Diseases Initiative now maintains over 20 new chemical entities in its portfolio, partly through AlphaFold-enabled target identification.
DeepMind estimates that AlphaFold has already potentially saved millions of dollars and hundreds of millions of research years, with over two million users across 190 countries accessing the database.
However, Elton’s perspective acknowledges both the revolutionary potential and remaining limitations. While AlphaFold excels at predicting static protein structures, drug development increasingly requires understanding dynamic protein-protein interactions and conformational changes.
The recently released AlphaFold 3 addresses some of these limitations by modeling interactions between proteins and other molecules, including RNA, DNA, and ligands. Google claims in an interview with PharmaVoice that there was at least a 50% improvement over existing prediction methods for protein interactions.
Enhancing Polygenic Risk Stratification
Building on these structural breakthroughs, Elton next turns from folded proteins to the genome itself, where AI is poised to redefine risk prediction and gene-editing delivery.
Conventional polygenic risk-score frameworks rely on additive, linear regression models that perform well for highly heritable traits like height but fail to capture complex gene–gene interactions.
Elton explains that the way genes are associated with phenotypes is not simply linear. Nonlinearities exist as well, highlighting the limitations of sparse linear predictors.
Neural network and deep learning architectures offer a path to uncover epistatic effects, yet Elton cautions that such models demand unprecedented data and compute scales. He notes that to predict a condition like autism or even intelligence, researchers would need between 300,000 and 700,000 sequences, necessitating tens of trillions of letters or tokens.
In other words, matching the data scale of GPT-4 becomes a prerequisite — demanding robust cohort assembly, cross-biobank harmonization, and petascale compute infrastructure.
Elton candidly notes that the added value of using a neural net or a language model actually might be relatively small for some traits where linear models already capture most genetic effects. For heritable characteristics like height, for example, the added neural net value is relatively small because linear predictors explain all the heritability.
This honest assessment reflects the understanding required to prioritize which genetic traits and clinical applications justify the massive computational investment needed for neural network-based polygenic prediction.
Elton also warns that handling tens of trillions of tokens per project requires more than raw compute; it mandates rigorous data-management frameworks that ensure privacy, regulatory compliance, and security. Cloud architects and life-science IT leaders should therefore adopt:
- Encryption-at-rest
- Role-based access control
- Immutable audit trails to safeguard personally identifiable information
Beyond prediction, Elton mentions that AI is also transforming precision gene editing workflows. Elton describes ex vivo therapies — when blood is extracted, treated with genetic editing, and ultimately returned into the bloodstream.
In this way, AI tools can now fine-tune viral shells so they target the right tissues and optimize guide-RNA instructions to avoid accidental gene cuts.
Of the many kinds of technologies that professionals have encountered in recent years, artificial intelligence (AI) presents perhaps the greatest challenge. CPAs that do not become comfortable with AI and integrate it into their toolkit risk falling behind the technology curve. This article aims to demystify the AI concept for accountants and provide useful ways that CPAs and their organizations can use AI tools. This article also shares useful resources for those seeking to become AI savvy.
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Artificial intelligence (AI) has become prominent in business operations, company investments, budgets, and strategic plans, with corporate investment of approximately $252.3 billion in 2024(Artificial “Intelligence Index Report 2025,” Stanford University, https://tinyurl.com/23ssm6y9). The promise of AI is based on machines taking over activities once done by the human brain. With the release of AI technologies like ChatGPT, Copilot, Bard, and Dall-E seemingly making good on that promise as they introduce to society how AI could amplify human effectiveness, increase innovation, improve business efficiencies, and enhance customer service. As a result, many businesses have begun utilizing AI as part of business operations in content management, cybersecurity, fraud management, and customer support. According to Accenture (Reilly, et al., “AI: Built to Scale,” Accenture, November 2019, https://tinyurl.com/353pzsxp), an astounding 84% of business executives believe they need to use AI for business growth and to assist in achieving their strategic goals; however, 76% experience challenges with how to adapt and adopt AI effectively into their business practices. The impact of AI is not limited to large businesses. A United States Chamber of Commerce report found that one in four small businesses use AI to help enhance marketing and communications performance [“Empowering Small Business: The Impact of Technology on U.S. Small Business (Second Edition),” U.S. Chamber of Commerce, September 2023, https://tinyurl.com/yc4ut2eh]. While AI is increasingly an integral part of the business environment, integrating it into everyday practice remains a challenge.
AI and the Accounting Profession
Proponents predict that the accounting profession will leverage AI in several ways: 1) automating routine tasks such as data entry and transaction processing; 2) conducting financial analysis and forecasting due to its ability to process large financial datasets; 3) automating audit functions by analyzing financial transactions in real-time to identify discrepancies and generate reports; 4) improving tax compliance and planning by automating tax calculations and providing optimal tax saving opportunities and strategies (Jason Ackerman, “Artificial Intelligence May Be Coming Sooner than Expected,” The CPA Journal, May/June 2023, https://tinyurl.com/5ytxsryr).
The Big Four—Pricewaterhouse-Coopers (PWC), KPMG, Deloitte, and Ernst & Young (EY), have invested heavily in AI. In audit practice, the firms have developed AI tools to automate management of the audit process via systems like PWC Halo (“Audit of General Ledger with Halo,” PwC, https://tinyurl.com/3vrwejdz), KPMG Clara [“Bringing Clarity to the Audit with AI (Artificial Intelligence),” KPMG, https://tinyurl.com/3d5yea9u], EY Canvas (D. D’Egidio, et al., “Our Global Audit Platform, Powering Our One Global Audit, Is at the Heart of Our Digital Audit Offering,” EY, https://tinyurl.com/yj32zhm8) and Deloitte Omnia (Schmidt, et al., “Audit Innovation,” Deloitte, https://tinyurl.com/mr347vx3).
Additional AI tools that aid in fraud detection are PWC’s GL.ai (G. Rapsey, et al., “Harnessing the Power of AI to Transform the Detection of Fraud and Error,” PWC, https://tinyurl.com/3zszj8kn), and EY’s EY Helix (D. D’Edigo, et al., “EY Helix,” EY, https://tinyurl.com/mst26c4r). Both AIs are embedded on client platforms and can review billions of data points in milli-seconds and analyze the data to detect anomalies in the general ledger. Deloitte also has an array of AI tools (C. Oh, “Deloitte Drives the Power and Potential of Advanced AI and Generative AI to Internal Audit,” Deloitte Press Release, August 2024, https://tinyurl.com/3mjetx8x) including Argus, one of Deloitte’s oldest AI tools, which extracts accounting information from any type of electronic document to allow auditors to examine a large sample (T. H. Davenport, “The Power of Advanced Audit Analytics: Everywhere Analytics,” Deloitte, 2016).
The Big Four AI tools mentioned above are not comprehensive, but it should provide a roadmap for what is next as the firms continue investing in AI. In 2023, PWC indicated that they plan to invest approximately $1 billion over three years to train existing staff in AI, hire new AI staff, integrate AI platforms into their business operations, and provide consulting services for companies on how to incorporate AI into their business practices (A. Loten, “PricewaterhouseCoopers to Pour $1 Billion Into Generative AI,” Wall Street Journal, April 2023, https://tinyurl.com/2fh2u2p5). During the same period, KPMG announced an investment of $2 billion in AI and cloud services to enhance and automate their consulting, audit, and tax services to enable staff to utilize more time on providing advice to clients (M. Mauer, “KPMG Plans $2 Billion Investment in AI and Cloud Services,” Wall Street Journal, July 2023, https://tinyurl.com/3pef2f3e).
Similarly, EY invested $1.4 billion to launch EY.ai, which assists companies with an AI platform to perform business operations more efficiently in strategy, transactions, transformation, risk, insurance, and tax. EY has also invested in cloud and automation technologies. Even though EY had an existing platform, EY Fabric, this additional investment signals the significance of its investment in AI (“EY Launches Artificial Intelligence Platform ‘EY.ai’ and Invests US$1.4 Billion,” EY Press Release, September 2023, https://tinyurl.com/y3yen6ep). Finally, in April of 2024, Deloitte announced that they plan to invest $2 billion in Industry Advantage to provide industry-focused solutions to clients, including AI and cybersecurity (Deloitte Press Release, https://tinyurl.com/yncx3r44).
Fortunately, AI is not just for the Big Four, and small firms or individuals are not required to invest millions or billions. This article highlights AI tools that can be useful to CPAs and provides information for professionals just starting their AI journey.
What Is AI?
According to Accenture, “Artificial intelligence is a constellation of many different technologies working together to enable machines to sense, comprehend, act, and learn with human-like levels of intelligence” (“Artificial Intelligence,” Accenture, https://tinyurl.com/2mbcnhcw). This could include machine learning (“AI 101: What Is Machine Learning?” Accenture, https://tinyurl.com/2bcrpjpw) and natural language processing (“The Basics of Natural Language Processing,” Avathon, https://tinyurl.com/4cdesfws). According to a report by Forbes Advisor, the most common uses of AI are as digital assistants, chatbots, and machine learning (K. Haan, “How Businesses Are Using Artificial Intelligence,” April 2024, https://tinyurl.com/mzr2hj9d). This article will refer to AI in the broadest and most common usage.
As the use of AI has spread, many have questioned whether it could replace human accountants. According to KPMG’s Cliff Justice, AI “won’t replace the human at the human-to-human interactions level … AI tools are really good at pulling out information and making predictive choices, but they can’t replace human judgment” (S.J. Steinhardt, “Big Four Agree: AI Will Not Replace Accountants,” The Trusted Professional, August 2023, https://tinyurl.com/nzthbue7).
AI can make business processes more efficient and provide some technical support; however, only a live, skilled accountant can provide in-depth analysis and perspective as well as confirm that the information generated is correct (“Will AI or Automation Replace Accountants? A Critical Look at What the Future Holds,” Financial Cents, https://tinyurl.com/55epkkpf). Nevertheless, it is critical for CPAs to adapt and adopt AI skills to stay marketable and enhance business efficiency.
The ABCs of AI
The authors conducted an informal survey of smaller CPA firms about whether they used AI in their practice and their common responses were “No” or “AI?”. Thus, before delving into practical ways to integrate AI into an accounting practice, it’s helpful to review the phases of AI adoption and introduce some AI hands-on tools.
Assess one’s position on the AI journey.
Assessing where one falls in the AI journey is critical to determine what comes next. Shown in Exhibit 1, the AI Journey Roadmap from the RSM Play-book (Beyer, et al., “Here Is The Middle Market Artificial Intelligence (AI) Playbook,” RSM, https://tinyurl.com/mtepvftf), outlines five different phases of the AI adoption and implementation journey: Phase 1, AI education; Phase 2, AI strategy and assessment; Phase 3, AI preparation; Phase 4, AI execution; and Phase 5, AI support and maintenance. Most smaller CPA firms and sole practitioners may be in the AI education phase, but their goals may include AI playing a greater role down the road. On the other hand, larger firms could be between AI execution (Phase 4) and AI support and maintenance (Phase 5). (As a larger firm, RSM touts the wide variety of AI consulting services it provides to clients, https://tinyurl.com/cbvabamx.) It is important to note that although a firm could be in the AI support and maintenance phase, AI education is an iterative process.
EXHIBIT 1
Become familiar with basic artificial intelligence (AI) tools.
CPAs may want hands-on experience with AI to demystify the subject. For example, one could test a sample of basic online AI tools to determine which is preferable. Two common options are Gemini, powered by Google, and ChatGPT [S. Ortiz, “What Is Google’s Gemini AI tool (formerly Bard)? Everything You Need to Know,” ZDNet, February 2024, https://tinyurl.com/3av2fwsz]. The primary difference between the AI tools is that ChatGPT Premium and Enterprise use Microsoft Bing data and are no longer restricted to information before 2021 as previously (A. Pequeno, “Major ChatGPT Update: AI Program No Longer Restricted to Sept. 2021 Knowledge Cutoff After Internet Browser Revamp,” Forbes, September 2023, https://tinyurl.com/58664w8a), whereas Gemini uses Google data.
To test how each AI tool works, one of the authors signed up for both. First, I signed up for Gemini (https://tinyurl.com/2wnvdc45), agreed to the terms and conditions, and when the prompt screen was displayed, I typed “How can accountants use Artificial Intelligence?” The results (Exhibit 2) included automating tasks, data analysis, auditing, client communication, and budgeting. The caveat is that a disclaimer states, “Gemini may display inaccurate data.” This is highlighted because it is important to note that it is critical to verify the information provided by ChatGPT or Gemini.
EXHIBIT 2
Next, I signed up for ChatGPT (https://tinyurl.com/4hp4c9tm) and typed in “How can accountants use artificial intelligence?” Interestingly, ChatGPT states, “Don’t share sensitive info. Chats may be reviewed and used to train our models.” In the author’s experience, ChatGPT provided more content than Gemini. For example, ChatGPT shared eight ways accountants can use AI (Exhibit 3), whereas Gemini gave only five.
EXHIBIT 3
These tools are continuously being enhanced; for example, PWC formed a strategic alliance to enhance AI tools, including Gemini (C. Sedlak, “PwC and Google Cloud Announce Strategic Collaboration to Accelerate Enterprise Adoption of Vertex AI and Gemini Models,” PWC, April 2024, https://tinyurl.com/h684v7bv).
Practical AI Strategies—Where to Start?
Once one knows where they are in the AI journey and some of the available tools, it is time to explore practical ways to implement AI.
Keeping up to date with accounting trends and regulations.
Staying current with trends in AI is crucial. For example, Charles P. Myrick, a CPA firm owner in Washington, DC (https://tinyurl.com/7kx2sard), uses AI to research accounting trends and regulations. He emphasizes the importance of conducting additional research and analysis, as AI cannot be fully relied upon for accuracy. Even though AI tools are being utilized more than ever before, users should confirm that their information is based on the most recent available data instead of blindly relying on the AI query. For example, users can upload a newly issued Accounting Standards Update to their preferred AI tool and then query the AI to obtain a summary of the guidance.
Writing and researching information for reports or documents.
Writing and researching are an important part of business operations. Whether drafting emails, creating business memos, preparing reports for clients, or developing policies and procedures manuals, AI can significantly improve efficiency. For example, AI can be helpful for repetitive business tasks such as writing emails to clients (B. Oliver, “How Artificial Intelligence Can Help Save Accounting,” Journal of Accountancy, November 2023, https://tinyurl.com/y4v2mcn4) and reduce the time to respond to inquiries. In Outlook or Gmail, for example, the predictive text feature of AI automatically suggests phrases that can be written next with a swipe or pressing the tab, speeding up email communication and enhancing customer service. Grammarly (https://www.grammarly.com) is another tool that can assist in editing e-mails, Word documents, or PowerPoint presentations.
According to the Journal of Accountancy, writing a research memo can take up to “40 to 80 hours” (Oliver 2023). For example, tools like ChatGPT or Gemini can be used to query a specific topic. Although the information gathered would need to be verified, AI can provide a starting point. AI can also summarize large datasets, such as government regulations or research articles, saving time and resources.
Additionally, AI could aid business development by efficiently responding to Requests for Proposals (RFP) questions (Beyer). AI tools in this area include autorfp (https://autorfp.ai) and DataRobot (https://tinyurl.com/53jj-dv58). For example, if a company is considering submitting an RFP, AI tools have the capability to review data from its website and previous RFP content, then summarize information, and prepare responses to the RFP questions. This can increase efficiency and reduce operational costs.
Summarizing meeting notes.
Harvard Business Review estimates executives spend an average of 23 hours each week in meetings (L. Perlow, et al., “Stop the Meeting Madness,” Harvard Business Review, August 2017, https://tinyurl.com/bddnzth5). With AI, there is no longer a need to manually type meeting summaries from a voice recording. AI can assist with repetitive tasks by automatically delivering a written summary of the meeting minutes. Notetaking AIs such as Circleback (https://circleback.ai), Krisp (https://krisp.ai), and Granola (https://www.granola.so) offer seamless note-taking solutions, ensuring that all attendees are on the same page. These tools range from in-meeting bots to external notetakers (M. Peng, “The Best AI Note-Taking Tools for Meetings,” Charter, June 2024, https://tinyurl.com/4nfd5vja). Another example is the AI-powered Zoom (https://zoom.us), which can summarize the conversation during a meeting. Latecomers to the Zoom meeting can catch up by chatting with the AI assistant about what they missed. The Zoom AI assistant provides documented summaries in multiple languages (https://tinyurl.com/238m2csc). Whether for routine meetings or client discussions, having AI produce automatic notes documenting and summarizing saves time and money and automates routine tasks. The caveat is that, as with human notetakers, a summary must be reviewed for accuracy.
Enhancing customer service with chatbots.
According to a survey by Forbes Advisor, the most popular use of AI tools is customer service (https://tinyurl.com/yc5z7xv9). This trend is led by chatbots, which use AI and natural language processing to interact via text and voice with users. They are available 24 hours a day and can respond to common customer questions. Website service companies that cater to CPAs, such as Get Net Set (https://getnetset.com) or CPA Site Solutions (https://www.cpasitesolutions.com), can incorporate AI into a firm’s website. Another firm, PJC Group, LLC, uses AI through a chatbot to assist website visitors with questions (Exhibit 4).
EXHIBIT 4
Streamlining auditing, book-keeping, and consulting services.
Ackerman (2023) predicted that AI would impact accounting, particularly by automating repetitive tasks. AI-powered tools like optical character recognition (OCR) technology are already making a difference. For example, QuickBooks Booke AI can scan and extract data from invoices, bank statements, receipts, and other financial documents or reports, categorizing and summarizing them to assist with bookkeeping and reconciliations. It also provides a tool for fixing bookkeeping transactions and conducting reconciliations (https://tinyurl.com/72uaa5wj). This automation not only saves time but also improves accuracy by reducing errors. Excel users may consider using the DataSnipper (https://www.datasnipper.com) add-in, which integrates AI OCR to scan invoices, bills, or other documents into Excel. This provides a direct link to the source document, creating a clear audit trail when confirming the accuracy of financial data.
AI is also playing a role in detecting fraudulent transactions (https://tinyurl.com/cruyfemr), which can also help CPA firms delivering consulting services or performing an audit to conduct analytics to identify fraudulent transactions. AI tools such as Mind-Bridge (https://www.mindbridge.ai) can efficiently conduct an overview of the company’s financial data, determine risk areas, identify anomalies, and provide the auditor with a list of recommended areas to audit.
Professional branding and marketing.
In a media landscape driven by social media, building a personal brand is essential to stand out from the pack. AI tools can help create content, analyze customer engagement, and foster community. Platforms like Turbo Logo (https://turbologo.com), Looka (https://looka.com), and Canva (https://www.canva.com) can create eye-catching logos. AI tools such as Tugan.ai (https://www.tugan.ai) and Writesonic (https://writesonic.com) can help CPAs create content derived from marketing material already on the firm’s website, such as sales emails or newsletters.
Given the critical role of social media, an organization’s online presence is key to engaging users and building community. To this end, tools such as Social Pilot (https://www.socialpilot.co), Social Bee (https://socialbee.com), and Hootsuite (https://www.hootsuite.com) can help firms manage social media accounts through design, scheduling, and engagement services.
Another budget-friendly way to use AI in personal branding is through professional headshots for company websites or LinkedIn. AI-powered tools can do photo editing, background removal, virtual photo studios, and AI-generated headshots. For example, HeadshotsPro (https://www.headshotpro.com), AI Suitup (https://www.aisuitup.com), and Secta.ai (https://secta.ai) use AI to convert personal selfies into usable professional headshots.
Conducting firm training.
AI provides an opportunity to upskill sole proprietors, partners, managers, and staff across all levels. Training can be conducted by utilizing AI to query for solutions to an issue encountered during an engagement. For example, Deloitte employs an AI called DARTbot (Will Bible, “Generative AI in Accounting: Opportunities and Risks to Assess Today,” Deloitte, https://tinyurl.com/56ck4h96) to support its employees with daily tasks, answering accounting questions, and conducting research, enabling employees to focus on higher-level workflow. For smaller firms, AI tools such as Synthesia (https://www.synthesia.io), and InVideo (https://invideo.io) can transform textual training lessons into engaging videos. Employees can access the material on-demand, and it can be tailored to individual skill level. In addition, chatbots can provide on-going support to employees by answering routine questions or guiding them to available resources.
Getting Prepared
Although there have been large investments in AI, according to a PWC survey, 88% of companies struggle to realize quantifiable value from AI (“PwC Pulse Survey: Focused on Reinvention,” PwC, https://tinyurl.com/yc4ckcz2). AI promises to be a critical element in business strategy, today and into the future. Integrating AI is an enormous task, and CPAs would do well to solicit the assistance of consultants that can guide an organization through the process. Exhibit 5 presents a list of technology skills, resources, and pricing that can help CPAs beginning their AI journey.
EXHIBIT 5
Artificial Intelligence Skills, Resources, and Pricing
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Tools & Platforms3 weeks ago
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