The FinanceAI™ Dossier

Platforms powered by Generative Artificial Intelligence (GenAI) can review and analyse data, identify gaps and suggest ways to fix them, and provide leaders with on-demand insights.

Issue/opportunity

Finance work often includes repetitive tasks like pulling reports and reconciling data, much of which is manual and often in spreadsheets. There remain few resources and little time left to focus on the why behind the data or explore multiple what-if scenarios. A Generative AI-powered insights platform could serve as a digital analyst, allowing finance professionals to ask questions in plain language, explore unlimited datasets, and receive custom reports that reveal business performance.

How Generative AI can help

Data consumption at scale:

Generative AI opens the potential for leaders to leverage data at a depth and speed far beyond today’s possibilities. Operational data and financial data are often inconsistent across an organisation and lack a uniform structure. Even key economic indicators like inflation, consumer spending, or interest rates can vary substantively across geographies, sources of truth, or interpretations. Generative AI could quickly reconcile disparate data, analyse against company data, and deliver real-time, insight-rich content that drives strategy.

Faster analysis and performance reporting:

Finance professionals could leverage a Finance Insights Engine to support, supplement, and accelerate their work. The engine might identify variances between plan and actuals and explain why they exist—eventually learning to tell more complicated stories deep into the financials. For example, when labor expense comes in higher than forecast, Generative AI can go multiple layers down in detail—considering geography, operational performance, seasonality, special projects, and more—to identify the root cause. Explanations could then be offered immediately in multimodal formats, including text, graphs, charts, or video.

More productive strategy sessions:

Imagine holding a planning session to identify needle-moving opportunities for the upcoming year. Today, analysing core financial metrics for multiple time periods and business lines is a time consuming and subjective process. With Generative AI-enabled technology at the table, leaders could request and receive ad hoc analyses of operational and financial data from the engine in real time to gain retrospective and prospective insights.

Managing risk and promoting trust

Reliability:

The Generative AI model is susceptible to erroneous outputs delivered with complete confidence, even with hallucinated data points or conclusions. Before conducting any analysis, datasets should be confirmed and reviewed for errors.

Transparent and explainable:

Confidence in Generative AI outputs requires stakeholders to understand how and why the machine reached its conclusions. Human validation and regular audits of Generative AI outputs remain essential.

Potential benefits

Enhanced decision-making:

A Finance Insights Engine powered by Generative AI can dramatically reduce the manual effort to analyse data and deliver consistent, accurate, and up-to-date insights for human analysts to leverage.

Reduced latency:

With its ability to analyse data instantly, Generative AI can provide on-demand, actionable financial information to guide leaders’ business strategies. 

Generative AI could create a true “lights out” close process by improving leader visibility, minimising rote work, and ultimately managing and completing tasks.

Issue/opportunity

A consistently timely, accurate, and efficient financial close is a challenge. It requires a lot of human power. Short bursts of activity take place throughout the year, but this limits visibility into the close process and often prevents the finance department from focusing on more strategic initiatives.

Generative AI can help eliminate the scramble to get the books closed on time and without errors. It can do the grunt work—categorising transactions, making journal entries, and generating financial statements—so that finance teams can focus on the bigger picture. With time, GenAI might take a bigger role in managing the close process and provide commentary on how the company performed.

How Generative AI can help

Smart reconciliation:

Generative AI could reconcile unstructured or inconsistent journal entries or take on more complicated accounts that require supporting thoughts or significant estimates to reconcile. Conversational, Generative AI-powered chatbots might also enable users to input exceptions for remediation at the source, run through next steps, update reconciliations, and consolidate financials.

Perceptive task management:

Generative AI could create integrated, automated closing checklists and, in time, it could centrally track and manage all close activities. It could also use prior history to anticipate how journal entries impact others, recognise issues to the close, and proactively reduce or eliminate delays.

Improved variance analysis:

Instead of relying solely on quantitative data, human analysts could leverage Generative AI to weave in unstructured data, like meeting notes, news stories, and interviews, to gain a deeper understanding of variances between actuals and forecasts.

Interpretative reporting:

Finance teams might set up templates from which Generative AI could produce initial accounting reports. As the technology develops logic to monitor and interpret new or changing regulations, it might start to provide impact assessments and produce more advanced accounting treatments in response.

Managing risk and promoting trust

Robust and reliable:

Generative AI is moving from an enabler of human work to a potential copilot, but work still remains to ensure accurate, reliable results.

Transparent and explainable:

When it comes to the closing process, Generative AI-driven processes and content must be clearly understood by finance teams and decision-makers.

Potential benefits

Process efficiency:

Generative AI can accelerate the close timeline with reduced effort and increased transparency. In time, Generative AI might learn to anticipate barriers to close, predict next steps, and ultimately take a larger role in the close process, allowing finance teams to focus on strategic initiatives.

Cost savings:

Passing off rule-based processing of routine transactions to Generative AI technology can save time by handling repetitive tasks.

AI, including Generative AI, will continue to elevate risk assessments, driving a streamlined and value-added integrated risk management approach that could transform today’s periodic risk assessments into a state of continuous monitoring.

Issue/opportunity

Risk management is critical for an organisation’s success—from business transformation to ongoing operations. Sophisticated approaches require extensive analyses of processes and data from qualitative and quantitative sources. The work can be complex, time consuming, and susceptible to human error or unintentional bias.

During risk assessments, leaders in various functions are often interviewed to gain risk-driven insights. However, interview capture and reporting are often performed manually, which could lead to missed or misinterpreted insights and a slow process. Further, new metrics like indicators of cyber risk are emerging that can be more difficult for leaders to grasp.

Risks are also often highly interconnected across organisations, which makes monitoring impacts more complex. AI, including Generative AI, could help leaders effectively sense and assess risks to strategy, operations, and other areas in a more dynamic and real-time manner.

How Generative AI can help

Key risk indicators and continuous monitoring:

Generative AI may enhance risk management processes by enabling unlimited, simultaneous, and continuous anomaly detection and analysis. The technology could analyse transactions and other enterprise wide risk indicators in real time and generate immediate reports and insights on potential discrepancies and outliers, allowing for timely risk response and mitigation.

Enhancing risk interviews:

Generative AI can analyse unstructured data sources, like interviews, to uncover specific takeaways, themes, and insights. Leaders can then rapidly identify and respond to existing and emerging trends.

Cyber risk monitoring:

Organisations can leverage Generative AI to develop an aggregated depiction of cyber risk. With near real-time data that ranges across various dimensions, leaders could better align their thinking and address critical gaps, threats, and opportunities. With time and development, Generative AI-enabled systems might also activate security measures, such as creating action reports, providing recommendations, and notifying users who may be impacted and need to take immediate action.

External risk sensing:

Predictive, AI-powered analytics could analyse massive amounts of intelligence—from open sources such as social media, blogs, forums, website reviews, industry newsletters, survey data, and news sources—and then formulate actionable insights. Companies could gain advance notice of emerging risks, knowledge of potential loss events, and increased awareness of potential threats to their business or industry.

Managing risk and promoting trust

Reliability:

Work remains to ensure that Generative AI produces accurate, reliable content. Today, Generative AI might confidently produce incorrect output, known as hallucinations.

Accountable:

Continued risk management requires identifying decision-makers for technology use and the decisions derived from the responses.

Privacy:

Interviews and surveys of business leaders may need to be kept anonymous; in which case, it will be crucial to ensure that data privacy is maintained.

Potential benefits

Value creation:

Generative AI can support an integrated approach to risk management, which includes teaming with the business to help maximise ROI and enabling better business performance through effective controls and governance.

Process efficiency:

Business units can receive more timely reports that draw upon massive quantitative and qualitative datasets to inform decisions and strategy.

Accelerating insights:

Leveraging Generative AI solutions throughout the risk assessment life cycle can lead to data-powered insights through end-to-end digital enablement and allows organisations to evolve toward continuous assurance.

New discovery:

Companies can identify emerging risks and predict organisational impacts in advance of the marketplace through advanced capabilities of capturing and analysing massive internal and external datasets.

Issue/opportunity

Cash flow forecasting is often a labor-intensive process. And despite the work associated with it, many companies struggle to achieve a reliable forecast. This can lead to companies taking on higher borrowing costs for operations and potentially missing investment opportunities. Generative AI offers the potential to reduce the manual effort of data aggregation and increase the accuracy of the forecast output—ultimately saving costs and enhancing returns.

Datasets often reside across multiple systems in structured and unstructured formats. A Generative AI-enabled solution can aggregate all sources into its analyses. It might also begin to own part of the process. When gaps or inconsistencies in the data arise, the technology might research and resolve issues by following a set workflow (e.g., prompting sales representatives with requests for sales forecast confirmation) or leveraging historical trends and probabilities.

Finance teams could access unlimited scenario-based insights and predictions, allowing them to focus less time on generating reports and more time on analysing potential impacts.

How Generative AI can help

Exponential data consumption:

Generative AI can process and interpret data at unprecedented scale and speed. It can ingest and analyse historical company data as far back as it dates and can also factor in external data from various sources, in multiple formats. Collectively, richer data forms the foundation for the cash flow forecast, leading to more robust analyses and more accurate forecasts.

Predictive analyses:

Generative AI can identify the biggest drivers of cash flows and utilise a larger sample of parameters to forecast future cash flows more accurately.

For accounts receivable, this might include factoring in customer trends, such as average delay, percentage of payments delayed, average number of invoices per payment, total open amounts, and time between payments. Additionally, it could consider invoice factors, such as previous payment times, month due, day of the week due, invoice value, and total current invoice value. It could also keep a pulse on public data and extract economic patterns and customer activities that might affect future cash flows. This additional level of granularity and ability to predict with precision can offer business leaders more confidence in their plans.

For accounts payable, this might include projecting expected trade payables factoring in specificities related to vendors, based on importance and payment terms. For larger cash outflow drivers, such as taxes or payroll, this could involve correlation of data from other sources (e.g., financial statement projections for taxes or Human Resources (HR) information for payroll) to enhance forecast accuracy.

Foreign exchange assessment:

Generative AI can continually monitor international markets, factor volatility into its forecasting, and develop hedging strategies. Armed with this information, leaders can gain more confidence that their associated decisions are rooted in reliable data.

Variance reduction:

With manual processes, forecasting relies on different perspectives to provide, review, and analyse historical financial data. Generative AI can streamline and standardise the process, leading to a significant reduction in potential for error variance to actual results. Forecasts could be further enhanced with integrated visualisations to improve interpretation and confidence, quickly and with less overall effort.

Managing risk and promoting trust

Transparent and explainable:

Important decisions are made from cash flow forecasting; therefore, it is critical for decision-makers to have visibility and accountability into how Generative AI works. Forecasts will also improve over time, as the models have more opportunities to run larger datasets.

Safe and secure:

The financial information that will form the basis of the data models for Generative AI must be invulnerable to unauthorised access or unintended uses outside of the intended purpose for which the model is built.

Robust and reliable:

Generative AI will require early manual input and tuning of data and tools to realise the benefits of automation. Companies will need to identify how granular to get, as well as guidelines and guardrails.

Potential benefits

Timely market analyses:

Generative AI can conduct real-time, ongoing reviews of multiple media sources and internal data that inform forecasts and potentially improve accuracy and reliability.

More accurate forecasting:

The more data that Generative AI can leverage, the greater the possibility for reliable, accurate information for planning purposes.

Reduced borrowing costs:

Better visibility into cash flows and more confidence in forecasts could reduce the need to tap into revolving credit lines and reduce associated borrowing expenses.

Enhanced investment returns:

Companies with a strong cash position can confidently take advantage of longer-term, higher-yield investment opportunities.

A mix of AI can fundamentally transform traditional order-to-cash processes. AI, Generative AI, and machine learning (ML) can automate and improve tasks and workflows across the order-to-cash cycle, resulting in cost savings and faster collections.

Issue/opportunity

Order to cash is the backbone of a business and a critical component of the working capital value chain. The order-to-cash cycle is made up of several subcycles, many of which are highly manual today. This workflow is ripe for Generative AI-powered transformation, through which companies can better understand customer credit risk, shorten sales cycles and days sales outstanding, and increase overall process efficiencies.

How Generative AI can help

Automated orders:

AI and ML can eliminate most of the manual tasks across the order-to-cash cycle. Automated data collection, collation, and interpretation can reduce the time spent on customer onboarding, data management, and deal closing. ML-driven smart quote generation can significantly reduce processing time on quotes and renewals. Once a sale has been approved, AI can create an invoice and order fulfillment request based on customer contract terms and standard policies and procedures.

Customer credit risk analysis:

Businesses want to know who they are selling to and how likely that person is to pay on time, with accuracy. Generative AI can evaluate credit risk by analysing customer data and credit history to help identify high-risk customers, improve credit decision-making, and reduce costs associated with bad debt. Based on the risk analysis, Generative AI can tailor sales offers based on the risk category of customers.

Faster collections:

Collections today is labor-intensive—phone calls and emails with invoice questions, overdue reminders, and other dispute intervention, often repeatedly. Leading organisations are already leveraging AI-enabled virtual assistants that use natural language processing (NLP) to enable self service customer payments and collection activities by phone and chat, in some instances pairing it with ML-enabled recommendation engines to offer customised offers and payment plans. Generative AI and ML are likely to further expand the capability of these virtual assistants in the near future by tracking collections and work lists, automating dunning letters and calls, making and documenting collectors’ calls, providing collections agents with recommended next actions in real time, running potential discount analyses, and automating cash postings. They could also understand payment trends and predict exceptions to get in front of them proactively.

Managing risk and promoting trust

Robust and reliable:

As the heart of the business and cash flow generator, it is important that order-to-cash technology produces consistent and accurate outputs and withstands errors. And since this technology is in front of customers, potentially around sensitive subjects like collections, it is important that the agent script is carefully curated and on brand to avoid reputational risk.

Accountable:

Finance professionals will continue to be in the loop for reviews and exception processing. Policies will be necessary that determine who is responsible for the decisions made or derived with the use of order-to-cash technology.

Fair and impartial:

Particularly as it relates to credit decisions, sales terms, and discounts, the technology must be designed and operated inclusively for equitable application, access, and outcomes.

Potential benefits

Accelerated time-to-value:

Integrating Generative AI across the order-to-cash cycle can expedite orders by reducing processing time and improve days sales outstanding through faster collections. The efficiencies gained across the cycle can improve working capital.

Reduced collections efforts:

Digitisation and predictive analysis can help create a better understanding of customer credit risk, allowing companies to make smarter decisions around credit limits and increasing the likelihood that payments will be made in full. This reduces the effort to collect payments or give up accounts receivable in disputes.

Enhanced accuracy:

Automating processes and operations can improve accuracy and help reduce the risk of human errors. Humans will remain in the loop for exception processing but can spend more time focused on strategic activities.

Issue/opportunity

Despite having historically been at the forefront of technological disruption, many sourcing and procurement functions continue to struggle to optimise efficiency, manage risk, and manage costs. Generative AI can make the procure-to-pay process simpler, cheaper, smarter, predictive, and more accurate—lowering the cost of doing business and unlocking growth opportunities.

How Generative AI can help

Enable efficiencies across procurement:

Generative AI can enable efficiencies across procurement, with the greatest potential in process automation, proactive risk and compliance management, and strategic decision-making and negotiations around suppliers and pricing. In an increasingly uncertain world, instant access and ability to process information is vital for mitigating and managing risk and empowering organisations.

Touchless invoicing and strategic supplier management:

Generative AI accelerates the drive toward touchless invoice processing. Today’s automation is smart enough to process, match, and pay—acting as a “digital employee.” “Traditional employees” will likely need only to intervene upon exception and can shift their focus to more strategic, value-adding tasks. Additionally, Generative AI can help manage suppliers, interacting directly through a chatbot feature that could, for instance, answer questions about payment timing, or clarify disputes in payments received. It can also develop supplier payment strategies based on things like the likelihood of the supplier to deliver on time, given any term changes.

Automated insights and growth driver:

Generative AI unlocks the ability for insights, reducing the effort for knowledge-based, value-add work. AI can now create models that are learning and predictive in a manner that can give companies the first cut of insights, giving employees a kick-start into their analyses, their “so-whats.” Companies can get smarter about managing inventory by leveraging Generative AI to analyse historical fulfillment rates. They can better understand what they ordered, received, and paid for to plan more accurately and know when to place orders. Companies can know when they need to have product to help generate revenue and be in a better position to grow.

Managing risk and promoting trust

Accurate:

The procure-to-pay process starts by initiating a financial commitment and ends with cash leaving the company. Errors in amounts or otherwise could be detrimental and, as such, it is critical that any automation around these processes is accurate.

Reliable:

Using a Generative AI-powered predictive model can enable organisations to make fact-based and data-driven decisions. Organisations can compare products and services and rationalise them across their supplier base, based on factors that drive value for the company. Supplier performance becomes defendable, rather than just opinion based. The analysis can involve complex trade-offs, strategic considerations, and tacit knowledge that the AI models may not fully capture. As such, human judgment and validation is central to the interpretation and augmentation of Generative AI outputs.

Potential benefits

Optimise efficiency:

Automating creation, risk management, and strategic analysis across the procure-to-pay cycle helps reduce costs and improve overall operational efficiency.

Hold onto cash longer:

Generative AI can develop supplier and payment strategies that extend payments out as far as possible.

Increase profitability:

In addition to process efficiencies, the insights Generative AI can provide around inventory management can help companies plan better to be in an effective position for growth.

Generative AI can help companies keep a pulse on their working capital by continuously monitoring asset efficiency and identifying opportunities for a company to free up trapped capital and create shareholder value.

Issue/opportunity

Efficient working capital management is central to an organisation’s financial and operational health. Companies are often challenged when cash gets tied up in operations and look to improve their working capital by pulling levers across the value chain. Generative AI can help companies continuously monitor their working capital and drive efficiencies across working capital cycles to optimise cash.

How Generative AI can help

Data prep and real-time monitoring:

Today, even the most sophisticated treasury management systems are burdened by data. Data is pulled from multiple systems across the organisation and the prep required to standardise the data to run analysis is a significant undertaking. But Generative AI can change that. Generative AI can ingest data from multiple sources in various formats and standardise it instantly. Future treasury management systems can be linked directly into accounts payable, accounts receivable, and inventory systems. And with automatic data standardisation, Generative AI could continuously feed working capital management dashboards for real-time continuous monitoring and enhanced cash visibility.

Continuous insights and alerts:

With a continuous pulse and connection across all parts of the working capital value chain, Generative AI-powered management systems can generate insights and alert companies to anomalies, risks, and opportunities to improve working capital efficiencies. These future systems can be trained on industry benchmarks; ingest contracts and understand terms; monitor inventory, billings, collections, and payments; and help companies improve efficiencies across the entire working capital value chain by alerting them when risks (e.g., noncompliant processes and leakages) and opportunities arise. Imagine receiving an alert indicating a large payment is due that might require a credit line drawdown but can be avoided by moving inventory or incentivising customers for early payment at a cost less than short-term borrowing.

Automated reporting:

During set periodic cadences, automatically generated reports can provide an overall view of working capital and suggest levers to pull to increase cash through working capital efficiencies (e.g., prioritising collection efforts, monitoring inventory purchases versus company performance, and payment frequency) and reduce increased costs associated with inefficient processes. These reports can also break the boundaries of traditional formats and can deliver insights through video with visualisations that are easier to digest and increase transparency of operations.

Managing risk and promoting trust

Robust and reliable:

Professionals generally expect their technology to be consistent, accurate, and adaptable. While this is often the case with Generative AI, the models are susceptible to erroneous outputs delivered with complete confidence, known as hallucinations. Leaders should seek to mitigate risks of inaccurate or false Generative AI-derived insights influencing decision-making and leading to poor outcomes.

Explainable:

Confidence in Generative AI outputs requires that stakeholders understand how and why the machine reached its conclusions. Human validation of Generative AI outputs remains essential, and associated models must be explained to a range of stakeholders.

Potential benefits

Operational efficiencies:

Automating data preparation, insight generation, and reporting can save time and resources to produce insights faster with lower costs.

Unlock trapped capital:

Cash released from effective working capital programs can be instrumental for companies in fueling growth, transforming their operating model and technology, or—alternatively—as needed for survival.

Understanding what matters most:

Working capital improvements are usually made up of many little things that can be hard for those not deeply engrained in the processes to understand. Generative AI can help synthesise all the small factors that move the needle along with the financial impacts so executives can understand where to focus to improve their cash position.

Generative AI can enable tax professionals to access, analyse, and gain insights from their tax data by automating the process of data extraction, transformation, and loading. This can reduce the time spent on routine tasks, thus allowing professionals to focus on deriving insights from the data.

Additionally, Generative AI can help compare organisational tax data against publicly available industry data for benchmarking. This comparative analysis can provide valuable insights to assist with strategic decision-making.

Issue/opportunity

Tax data users rely on structured and unstructured data and face challenges in accessing, cleansing, reconciling, and getting data fit for purposes for tax analysis. These steps add lead time and knowledge requirements necessary to locate and prepare the data.

Generative AI can offer tax professionals real-time, efficient insights into their tax data and deliver a more personalised experience by integrating with existing systems. User-friendly interfaces provide nontechnical users with means to understand complex tax data. It also enables standardisation of operational tax data, which can make tax processes more efficient and provide a basis for strategic advice to other business areas.

How Generative AI can help

Streamlined data access:

Generative AI can automate the process of data access through chatbots and applications. It can quickly locate specific data points or reveal key gaps, like undetected research and design (R&D) credits or nexus states, within the company’s vast tax databases, ensuring that users get accurate information without the need for manual searching. Leveraging AI to rapidly process vast amounts of data and knowledge reduces the time required to go from question to answer.

Comparative analysis:

By linking Generative AI with a company’s tax data, it can run analyses like flux or period-over-period provision in seconds, while simultaneously linking into publicly available competitor tax data from public financial statements to help guide internal financial strategy adjustments. AI can highlight disparities, trends, and opportunities, providing valuable insights for strategic decision-making.

Draft tax memo generation:

AI can generate initial draft tax memos like controversy responses, tax-planning memos, and provision footnotes by automating research of relevant tax precedents, regulations, and standards; analysing and considering tax sensitivity; and summarising findings with citations and documentation. This can result in expedited writing processes, time savings, and improved consistency between documents as it can adhere to predefined guidelines.

Managing risk and promoting trust

Responsible:

When it comes to governance and control, while granting more data access to a wider segment of the workforce, organisations may face a more complex challenge of restricting who in the organisation is permitted to access sensitive business data.

Privacy:

When dealing with sensitive and proprietary information, the organisation must contend with securing the data, remove or obscure it in training and testing sets, and evaluate the model to determine whether protected information could be leaked, either due to faulty function or a targeted attack.

Categories of uses cases: Tax reporting and analytics

1. Research and development tax credit analysis
2. Withholding tax/exemption
3. Account flux analysis
4. Trial balance/tax sensitivity
5. Provision footnote disclosure drafting
6. Transfer pricing country-by-country reporting
7. Multistate nexus studies
8. Cost of performance vs. market sourcing tax apportionment
9. Reporting packages across tax processes (analytics)
10. Refund recovery analysis
    

Potential benefits

Accelerate informed analysis:

AI can rapidly access and process knowledge when generating responses, resulting in reduced time going from question to answer and better leveraging the knowledge and data that your organisation has access to.

Proactively analyse and respond:

Real-time automated data analysis allows AI to process vast tax data, identify anomalies, and generate immediate alerts for tax professionals, enabling proactive response to tax trends and issues.

Issue/opportunity

Investor relations (IR) is complex and dynamic. Beyond just earnings call preparation, IR teams prepare for investor conferences, field calls from institutional investors and analysts, assist with corporate strategy and public relations, and more.

The future of IR with Generative AI is spending minimal time drafting communications—instead focusing on getting the message right for the audience.

How Generative AI can help

Pull in more data, quickly:

Generative AI can process and index large volumes of financial data to identify and extract crucial key performance indicators, such as a particular fund manager’s moves in or out of your stock versus your peers. It can then recognise key topics to emphasise, create a storyline flavored with public data like SEC filings and economic reports, and produce materials that convey intended messages.

Ensure consistency:

Drawing from historical communications and guidelines, Generative AI can produce investor communications that maintain a company’s style and tone. Packages might include draft scripts for a company’s quarterly earnings calls, investor day presentations, analyst responses, SEC filings such as 8-K and 10-K forms, annual reports, or strategic announcements that follow a consistent narrative.

Predict analyst and market responses:

As Generative AI advances, it might be used to gather intelligence from the market. The technology might identify influencers, examine the types of questions they tend to ask, and prepare draft responses. A Gen AI-enabled digital assistant might be used to listen live to analyst questions and suggest a response in real time, complete with a visual such as a chart or graph to visualise the answer.

Prepare targeted messages:

Today, teams of analysts pore over data to prepare leaders for investor presentations. Generative AI could assist and tailor messaging for key audiences by continually scanning publicly available sources. The result? Leaders arrive prepared with up-to-the-minute, customised, detailed materials.

Stay ahead of the curve:

Business moves quickly. It is important that leaders monitor their companies’ and their competitors’ investor bases—over time and in the moment. Are activist investors seeking influence? How best to protect market share? Generative AI could increase the capacity to scrub transcripts and quarterly releases, identify trends, and produce objective insights.

Managing risk and promoting trust

Fair and impartial:

Since communications affect public perception of a company, Generative AI should be designed with an eye toward ensuring inclusive and equitable application, access, and outcomes.

Robust and reliable:

Content created must be consistent and accurate, free from errors, and able to recover quickly from unforeseen disruptions and misuse.

Potential benefits

Minimise manual labor:

Generative AI can create basic narratives that human teams edit and review. As the technology learns and gains access to more data, it will increasingly create more robust, final-stage content—freeing up teams to focus on strategy.

Ensure standardisation:

Investor communications should maintain consistency in tone and style, across all media types and channels. The model could also adapt communications for cultural or language differences.

Increase scalability:

With Generative AI’s ability to produce a volume of content with ease, IR teams could ramp up their communications to key audiences without needing to consider resource constraints.