Turing Blog

Most AI tools are referred to as “assistants” and they’re intended to automate and facilitate daily tasks, especially the most repetitive ones. Tools like Calendly, Superhuman, Asana, and Slack help executives daily with calendar appointments, team collaboration, and email prioritization. Other AI tools like Siri, Google Assistant, or Alexa have conversational abilities and can help by making calls, reading texts, or setting up reminders. Sometimes, the support is so seamless that it leaves people wondering if AI will replace human assistants, like executive assistants (EAs). 

The answer is: Probably not. As an executive, the best approach is to avoid extreme postures. Being afraid of new technologies can leave EAs without the proper toolkit to perform efficiently. On the other hand, assuming that AI can do the job of a top-tier executive assistant is far-fetched. It’s crucial to strike a balance between the value of AI and the value of human support. 

How executive assistants are using AI 

Photographers use all sorts of digital tools in their trade. Most use a digital camera and software like Photoshop and Lightroom. These tools help them do their job faster and even better, but they still need a great deal of technical expertise, an eye for detail, and training to produce the best images. 

It’s a good example of how an executive assistant uses AI. EAs can use tools to record meetings and make transcripts or set automated replies or reminders for teams. They can leverage GenAI to build non-confidential decks or process playbooks. These are essential, useful tools. Yet, executive assistants need to show proactiveness, possess a high degree of intuition to make decisions, be ethical and trustworthy, and have ongoing training that will allow them to perform their duties efficiently.

The better we learn how to use AI tools and technology, the more benefit we’ll get from them. 

EAs’ diverse tasks, responsibilities, and audiences require an assortment of skills and tools. LLMs and automation are greatly appreciated and empowering for executive assistants and are starting to become tools of the trade. 

The following tasks are being documented and performed by the team of executive assistants at Viva. 

Building usage databases for the entire team

The team of executive assistants built a centralized database with the collective knowledge of AI and LLMs’ usage. Every team member has access to edit and add new information about how LLMs have worked for them. The two top LLMs they use are ChatGPT (multiple versions) and Copilot (Bing). 

The databases feature guides on usage like how to handle confidential information (and how to assess what is confidential), how to assess for ethical content, and how to confirm data. These guides also include prompt recommendations and basic structures for any team member’s use. 

Here’s an example of a basic prompt structure found in said database:

Use cases of AIs and LLMs by executive assistants

Executive assistants use LLMs in a variety of ways. Here are some common EA tasks where LLMs prove useful: 

Project management and vendor research

• Project management: Coordinate tasks and timelines, track progress, and ensure project objectives are met.
  • [Prompt example] Summarize these notes into a bulleted list overview, and write all the action items in a separate list.
  • [Prompt example] Create a detailed project plan for the [project name], including key milestones, deadlines, and task assignments. Ensure all tasks are clearly outlined and assigned to appropriate team members.

• Vendor research: Gather information on potential vendors, compare their offerings, and present options to the executive for decision-making.
  • [Prompt example] Compare the offerings of the identified vendors, including pricing, services provided, and any unique features or benefits. Present the findings in a comparative table
  • [Prompt follow-up]  Gather customer reviews and testimonials for each vendor to assess their reliability and quality of service. Summarize the feedback and include it in the vendor comparison report.

Meeting and presentation support

• Create pre-meeting briefs: Summarize key points, agenda items, and background information for upcoming meetings to ensure the executive is well-prepared.
  • [Prompt example] I’m meeting X for an {investor/sales/partnership/co-branding/etc} meeting. Create a pre-meeting brief about company XYZ (paste LinkedIn profile + webpage URL). Include these bullet points: the year it was founded, key information, industry, shared investors, business model, latest news, headquarters, fundraising stage, insights on anything else I’d need to know, and headcount.

• Create presentations (slide outline and content of non-confidential information): Develop slide decks with relevant content and visual aids to effectively convey information during presentations.
  • [Prompt example] Ask ChatGPT to create a PowerPoint presentation and export it. Afterward, ask it to give you the VBA code. It will give you a code which you can then input in PowerPoint, and it will create the slides for you.

Writing and content creation

• Generate content ideas for social media: Brainstorm and propose engaging content ideas for social media platforms to enhance brand visibility and engagement.
  • [Prompt example]  “Brainstorm 10 engaging content ideas for our company’s LinkedIn page to enhance brand visibility and engagement in the tech industry.”
  • [Prompt example]  “Propose a week’s worth of Instagram posts for our lifestyle brand, focusing on promoting our new product line and increasing follower interaction.Our URL is X”

• Create questions for lead generation, customer satisfaction surveys, and interviews: Formulate questions to gather valuable insights from leads, customers, or interviewees to inform decision-making processes.
  • [Prompt example] “Formulate 15 questions for a customer satisfaction survey to gather insights on our new service and identify areas for improvement.”
  • [Prompt example] “Create 5 interview questions to ask potential leads during initial sales calls to understand their needs and how our solutions can meet them.”

• Writing in your executive’s tone and style: Craft emails, memos, or other written communications in a way that reflects the executive’s tone.
  • [Prompt example] “Compose a memo from the executive to the team, encouraging them after a successful project completion and outlining the next steps, maintaining the executive’s tone of voice.”
  • [Prompt example] “Draft responses to comments on the CEO’s LinkedIn posts, ensuring the replies reflect the CEO’s professional and engaging tone. Here are 10 other previous responses she’s said as examples (attach screenshots).”
  • [Prompt example] “Create a template for responding to congratulatory comments on the CEO’s LinkedIn profile, expressing gratitude and maintaining their funny and appreciative style.”

Concept clarification

• Understand difficult concepts: Seek explanations for or simplification of complex concepts or terms.
  • [Prompt example] “Explain the concept of blockchain technology in simple terms suitable for someone with no technical background.”
  • [Prompt example] “What are the most significant challenges facing the implementation of technology in healthcare, and how are companies addressing these issues?”

Optimizing productivity and workflow

• Enhance productivity: Seek advice on optimizing personal workflow and time management strategies.
  • [Prompt example] “What are 5 ways to foster a collaborative environment as an executive assistant to a 20-person, Series A startup CEO based in the US?”

• Automate repetitive tasks: Explore automation tools or methods to streamline repetitive tasks and reduce manual workload.
  • [Prompt example] “Explore methods to automate data entry tasks for our healthcare CRM system to save time and minimize errors.”

• Avoid getting stuck: Seek guidance on how to approach unfamiliar tasks or situations to overcome obstacles and achieve objectives.
  • [Prompt example] “I’m an executive assistant, my executive just asked me to do {add task}. Where do I start?”
  • [Prompt example] “Provide guidance on handling {an unfamiliar software tool} that I need to use for an upcoming project.”

Personal queries

• Personal questions: Seek information or clarification on various topics of personal interest or curiosity to broaden knowledge and understanding.
  • [Prompt example] “What are wellness activities that I can add to my daily routine to avoid burnout?”
  • [Prompt example] “Make a 5-day meal plan for busy people. Include plenty of vegetables and meat recipes only twice a week.”

AI’s proven impact on EA automation goes beyond the hype

The use of LLMs or AI in the workplace is nothing new and is clearly gaining traction. Think Grammarly, Calendly, Magical, and others. Executive assistants are leveraging all of this technology to better support the executives they work with. The fast-paced environment of startups seems to be calling for it. 

Here are some examples of what we mean:

1. EAs use Grammarly to proofread and improve the quality of emails, reports, and presentations. It’s great for catching typos and ensuring the writing is clear.
2. Calendly allows users to determine specific time slots in their calendars for meetings. Stakeholders can quickly self-schedule an appointment. EAs can make sure that there are spots for customers always available and arrange meeting agendas whenever a new appointment is scheduled. 
3. EAs use Magical to automate repetitive tasks such as email templates, signatures, instructions, reminders, and greetings. Using a single command, they can easily retrieve these templates. 
4. Otter.ai is used to transcribe meetings and interviews. EAs use transcripts to extract action items, create workflows or processes, and document information. These transcripts are shared with the leadership team for further reference. 

Concluding thoughts

In conclusion, the landscape of artificial intelligence is evolving and its usage in the workplace is increasingly common. Companies that are looking for fast growth can find automation tools greatly beneficial for their efficiency.

Executive assistants can be great allies not only in using AI tools, but also in implementing their usage across the organization. Having an executive assistant who is trained in these technologies is a contributing factor to success. Since technologies are ever-changing and evolving, an executive assistant also needs to be proactive and curious, with critical thinking and analytical skills. 

These days, adopting extreme views on AI—whether idolizing it or fearing it—is not the most effective approach to new technologies. Instead, recognizing that AI tools are designed to complement human effort can lead to remarkable outcomes. We see the future as EAs + AI. Not EAs replaced by AI. If you’re curious to learn more about how we do that at Viva, let’s chat.

The threat of artificial intelligence (AI) replacing human workers in accounting careers is a common concern. But a closer look reveals a different story.

AI is poised to transform accounting careers, not eliminate them. It will free professionals from the shackles of repetitive tasks, allowing them to focus on more strategic and analytical work, ultimately making accounting careers more fulfilling and impactful.

This shift, however, necessitates a change in the skills required for career success. The days of mindlessly processing invoices are slowly fading. The future belongs to those who can leverage AI’s power to unlock valuable insights and contribute meaningfully to financial decision-making.

History repeats itself

Technology has transformed accounting before. The introduction of spreadsheets sparked similar anxieties about job losses. Yet, figures from Morgan Stanley show that bookkeepers didn’t become extinct. In fact, the number of accountants, auditors, and finance managers significantly increased.

The lesson? When technology automates tedious tasks, it creates opportunities for more analytical and value-added work.

Use case: How AI empowers AP teams

At its core, AI excels at automating repetitive tasks that consume a significant portion of an AP team’s time. Here are some examples of how AI is streamlining AP processes:

• Touchless expense reports: Imagine a world where expense reports are generated automatically. Generative AI can create them from a simple snapshot of a receipt, complete with AI-powered categorization, saving time and reducing errors. Employees can simply photograph their receipts with their mobiles, and the AI can extract relevant information like date, vendor, and amount, automatically populating an expense report. This not only frees up employees from tedious data entry but also reduces the risk of human error.
• Optical character recognition (OCR) for bills: AI-powered OCR technology can extract data from bills with remarkable accuracy, boosting efficiency in processing invoices and expenses. Previously, manually entering data from invoices was a time-consuming and error-prone process. OCR eliminates this burden by automatically capturing data points like vendor information, invoice number, and line items. This not only saves time but also ensures greater accuracy in data capture.
• AI-based receipt matching: No more manually matching receipts to transactions. AI can automate this tedious task, freeing up valuable time for AP professionals. Matching receipts to corresponding transactions can be a cumbersome process, especially for companies that process a high volume of invoices. AI can streamline this process by automatically matching receipts to the appropriate transactions based on predefined rules and data points.
• Automated vendor categorization: Gone are the days of manually assigning categories to vendors. AI can automate this process for physical card transactions, reducing the workload for AP teams. Manually classifying vendors into different expense categories can be a time-consuming task. AI automates this process by analyzing spending patterns and automatically assigning vendors to the appropriate categories. This not only saves time but also improves the accuracy and consistency of expense categorization.
• Seamless bill & PO matching: AI can streamline the process of matching bills to purchase orders, ensuring accuracy and timely payments. Traditionally, matching bills to purchase orders involved manual verification to ensure accuracy. Today, this can be automated by comparing data points between bills and purchase orders, flagging any discrepancies for review. Automation reduces the risk of errors and ensures timely payments to vendors.

Beyond automation, AI as an analytical powerhouse

While automation is a clear benefit, AI’s true potential lies in its ability to handle vast amounts of data analysis.

However, to unlock this potential, finance professionals need to develop the right skillset. Nicolas Boucher, founder of AI Finance Club, emphasizes how AI can elevate the value finance leaders bring to their organizations. When used correctly, AI can assist with functions like:

• Scenario analysis: Simulating different business scenarios to evaluate potential outcomes and make informed decisions. For instance, AI can be used to model the impact of changes in interest rates, currency fluctuations, or market conditions on a company’s cash flow. This allows businesses to make data-driven decisions and plan for potential risks.
• ROI analysis: Measuring the return on investment for various initiatives, helping businesses prioritize resources effectively. AI can analyze historical data and financial projections to calculate the potential ROI of different projects or investments. This allows companies to allocate resources strategically and maximize their return.
• Trend analysis: Identifying patterns and trends in financial data to gain insights into business performance. AI can analyze vast amounts of financial data to identify trends and patterns that might not be readily apparent to humans. This allows businesses to stay ahead of potential problems and capitalize on emerging opportunities.
• Variance analysis: Investigating discrepancies between budgeted and actual financial results to identify areas for improvement. AI can compare budgeted figures to actual spending and pinpoint variances. This allows businesses to identify areas where spending is exceeding budget and take corrective action.

New skills for success

As technology takes over the mundane tasks, the essential skills needed for success in accounting careers are changing. The emphasis on spreadsheets, programming, and data generation is giving way to a greater focus on financial planning and analysis (FP&A).

While some may find the need for Python proficiency daunting, Boucher offers a silver lining, “Ask ChatGPT to open the realm of possibilities, that is use Python. Python is the best tool to automate finance. And I mean, automate finance.” ChatGPT can use Python to read and analyze data from spreadsheets, as demonstrated by Boucher’s examples of uploading data and receiving AI-generated visualizations and recommendations.

Is AI a cure for burnout?

A record number of accountants are leaving the profession, with burnout cited as a major factor. A study by Avalara shows that over 80% of CFOs face a talent shortage in their teams. Many accounting professionals can relate to the feeling of being bogged down by endless manual tasks. Strategic tasks that leverage education and challenge thinking are much more engaging.

AI in accounting: partner, not a replacement

The world of accounting is welcoming a powerful new partner. Contrary to fears of robots taking over, AI is actually poised to make accounting careers more fulfilling and impactful.

According to the State of AI in Accounting Report by Karbon, “82% of accountants are intrigued or excited by AI, yet only 25% are actively investing in AI training for their teams.”

The results speak for themselves. Artificial intelligence is here to stay and the ripples it has created is undeniable to ignore. A strong commitment to AI training only means that the role of accountant becomes more varied, helped by the invisible accountant, AI.

Staying ahead of the curve

No one is being replaced, but change has arrived. Here are some tips for forward-thinking accounting professionals who want to thrive in the age of AI:

• Embrace the change: Explore the wealth of resources available, such as webinars, courses, and e-books on AI for accounting. Many professional organizations and online platforms offer training specifically designed to help accountants develop their AI skills.
• Communication is key: The effectiveness of many AI tools relies on clear and concise communication. Learn how to interact with these tools to get the most out of them. Understanding the capabilities and limitations of AI tools is crucial for maximizing their effectiveness.
• Data mastery: As the focus shifts to understanding data, develop your skills in pulling the right data for analysis. Finance professionals will need to be comfortable with querying databases, manipulating data sets, and using data visualization tools to extract meaningful insights.
• Become technologically proficient: Take the time to explore and experiment with AI-powered automation tools.

The future of accounting is bright

Many accounting professionals can relate to the feeling of being bogged down by endless manual tasks. The good news is that AI offers a solution. By automating tedious tasks, AI can free up time to focus on more strategic and analytical work. This, in turn, can lead to increased job satisfaction and a brighter outlook for the future of accounting careers.

While solutions like Airbase can play a role in AP automation with its AI-powered features like automated invoice processing and real-time payment tracking, it’s important to keep the broader impact of AI in mind. Staying on top of trends and being willing to embrace the changes created by AI.

In the rapidly evolving world of technology, AI is no longer just a buzzword; it’s the most disruptive technological innovation of the 21st century. According to a 2024 McKinsey report, 70% of companies are already harnessing AI to streamline operations and enhance decision-making processes, demonstrating its profound impact across industries.

Among those at the forefront of this revolution are data scientists. These modern-day alchemists turn raw data into golden insights, driving decisions that propel businesses forward. Sometimes even the wizards of data science need a little magic, and that’s where AI steps in. Let’s explore how data scientists are harnessing the power of AI to become more effective and efficient in their roles.

Automating the mundane

Data science is inherently complex and involves a multitude of tasks ranging from data collection and cleaning to analysis and interpretation. Traditionally, these tasks have been time-consuming and often tedious. However, AI has introduced a wave of automation that liberates data scientists from the drudgery of repetitive work, allowing them to focus on more strategic and creative aspects of their jobs.

Take data cleaning, for instance. This foundational step is crucial for ensuring the quality of insights but is often considered the least glamorous part of the process. AI-powered tools can now automate much of this task by identifying and rectifying errors, handling missing values, and normalizing data formats. A recent Gartner study revealed that data scientists spend up to 60% of their time on data preparation, but AI can reduce this effort by up to 40%, allowing them to focus more on analysis and strategy. This not only speeds up the process but also enhances accuracy, as AI algorithms are less prone to human error.

The future of predictive analytics

Predictive analytics is where data science truly shines, and AI is amplifying its power exponentially. Traditional statistical models have long been used to forecast trends and behaviors, but AI algorithms—especially those based on machine learning—offer a more robust and dynamic approach.

Machine learning models can process vast amounts of data at unprecedented speeds, learning and improving over time. This iterative learning process allows AI to uncover intricate patterns and relationships within the data that might elude human analysts. 

For example, in financial services, AI-driven predictive models can analyze market trends, customer behavior, and economic indicators to provide highly accurate investment forecasts. A Forrester report also found that companies leveraging AI for predictive analytics saw a 20% increase in forecast accuracy. This additional level of insight empowers data scientists to make more informed recommendations, driving better business outcomes and optimizing models for ROI

Natural language processing: Making sense of text data

A significant portion of the world’s data is unstructured, particularly in the form of text, and it’s being created quicker than you’d imagine. Emails, social media posts, customer reviews, and more hold valuable insights if one can store, clean, and decode them. Natural Language Processing (NLP), a branch of AI, equips data scientists with the tools to do just that.

NLP algorithms can parse through massive volumes of text data, extracting sentiment, identifying key themes, and even summarizing information. More advanced NLP models can even identify and correct coding errors, which allow data scientists to scale models with greater confidence. 

This capability is invaluable for businesses looking to understand customer sentiment, monitor brand reputation, gain insights into market trends, or drive operational clarity. For instance, a company launching a new product can use NLP to analyze social media feedback in real-time, enabling swift adjustments to marketing strategies based on customer reactions.

According to a 2024 IDC report, businesses utilizing NLP data insights experience a 30% improvement to customer satisfaction scores, as they can more effectively analyze and respond to customer feedback. 

Real-time data analysis

The ability to process and analyze data in real-time is a game-changer for many industries, and AI is at the heart of this capability. Real-time data analysis allows businesses to respond to events as they happen, providing a significant competitive edge. According to a recent Splunk report, 80% of companies have seen an increase in revenue due to the adoption of real-time data analytics, as it enabled faster decision-making and operational decision making. 

In sectors such as e-commerce, AI-driven real-time analytics can optimize inventory management, personalize customer experiences, and improve supply chain efficiency. For data scientists, real-time analysis tools mean faster and more accurate decision-making. They can set up automated systems that monitor data streams, trigger alerts for anomalies, and even take predefined actions without human intervention. This not only enhances operational efficiency but also ensures that businesses can capitalize on opportunities and mitigate risks promptly.

Enhancing model accuracy and robustness

Building accurate and robust models is a core responsibility of data scientists, and AI is playing a pivotal role in this area.

Advanced AI techniques such as deep learning can handle complex datasets with high-dimensional features, providing unparalleled accuracy in fields like image and speech recognition. Moreover, AI frameworks can perform automated machine learning (AutoML), which simplifies the model-building process, making it accessible even to those with less expertise. This democratization of data science tools means that businesses of all sizes can benefit from cutting-edge analytics, driven by AI-empowered data scientists.

Facilitating collaboration and knowledge sharing

AI is also transforming the way data scientists collaborate and share knowledge, with research from Stanford showing 25% average improvement in AI-enabled team productivity. Platforms powered by AI can facilitate better project management, version control, and knowledge sharing within data science teams. For instance, AI-driven code review tools can automatically check for errors, suggest improvements, and ensure adherence to best practices. This not only streamlines the development process but also enhances the overall quality of the work.

AI can also aid in the creation of more intuitive and interactive dashboards and visualizations, making it easier for data scientists to communicate their findings to non-technical stakeholders. By bridging the gap between complex data insights and business decision-makers, AI ensures that valuable information is not lost in translation.

The future of data science: continuous evolution with AI

As AI continues to evolve, its integration with data science will only deepen, bringing about new innovations and efficiencies. The future holds promise for more sophisticated AI models that can understand more nuanced context, learn from smaller datasets, and provide even more accurate predictions, driving unprecedented business value..

AI is not just a tool for data scientists; it’s a powerful ally that enhances their capabilities, allowing them to focus on what they do best: deriving actionable insights from data. By automating mundane tasks, enhancing predictive analytics, making sense of unstructured data, enabling real-time analysis, improving model accuracy, and facilitating collaboration, AI is transforming data science into an even more dynamic and impactful field. As we move forward, the synergy between AI and data science will continue to unlock new possibilities, driving innovation across industries.