Unlocking Business Insights with AI

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Making informed decisions quickly is crucial for staying ahead of the competition. One key factor in achieving this agility is the ability to analyse and extract insights from business documents efficiently. Filed accounts, in particular, contain a wealth of information about a company's financial health, operational efficiency, and strategic direction.

Afforai is an AI-powered tool designed to help "researchers manage, annotate, cite papers and conduct literature reviews with AI reliably", which means it can be fed documents and asked questions about them.

In 2019, I exited a highly successful company, Verastar. 5 years later, I wanted to check on their progress. I decided to test Logically.app by uploading the company accounts from 2019 to 2023 and asked for an analysis. Unfortunately it wasn't a positive report.

The Importance of Document Analysis

In today's fast-changing business world, organizations need to analyse and interpret documents efficiently to stay competitive.

With increasing volumes of data and the necessity for timely, informed decision-making, businesses must effectively extract insights from various documents, including reports, contracts, and financial statements.

Business document analysis not only enhances compliance and operational efficiency but also uncovers opportunities for innovation and growth.

Leveraging AI to Streamline and Improve the Analysis Process

Artificial intelligence (AI) is transforming the way businesses approach document analysis. By leveraging machine learning algorithms and natural language processing, AI can rapidly process and analyse vast amounts of data, identifying patterns, trends, and insights.

It has the ability to take a large amount of complex information and provide clear, insightful, analysis.

By employing AI technologies such as Afforai, or CrawlQ, organizations can research, analyse and generate insights at an unprecedented speed. With AI-powered tools, companies can also synthesize information from disparate sources, ensuring that leaders have a holistic view of business operations.

Introducing Afforai

Afforai is an innovative AI-powered research assistant and chatbot designed to revolutionize the way users analyse and extract insights from their documents.

This cutting-edge tool leverages advanced artificial intelligence technologies to streamline document analysis, making it an invaluable asset for professionals seeking to enhance their research and decision-making processes.

Overview of Afforai's capabilities

Afforai offers a comprehensive suite of features that cater to various document analysis needs:

• Multi-document analysis: The tool can process and analyse multiple documents simultaneously, allowing users to gain insights from a wide range of sources efficiently.
• Natural language processing: Afforai understands and interprets human language, enabling users to interact with the tool using conversational queries.
• Data extraction and summarization: The AI can quickly extract relevant information and generate concise summaries, saving users valuable time.
• Custom chatbots: Users can create specialized chatbots tailored to their specific document analysis requirements.
• Integration capabilities: Afforai can be seamlessly integrated with various platforms and workflows, enhancing its versatility and usefulness across different business contexts.

Application of AI in document analysis

Afforai applies artificial intelligence to document analysis through several sophisticated techniques:

1. Machine learning algorithms: The tool employs advanced machine learning models to recognize patterns, extract key information, and improve its analysis capabilities over time.
2. Natural Language Processing (NLP): Afforai utilizes NLP to understand the context and meaning of text within documents, allowing for more accurate and nuanced analysis.
3. Semantic search: The AI implements semantic search capabilities, enabling users to find relevant information based on the meaning and intent of their queries rather than just keywords.
4. Automated data categorization: Afforai can automatically categorize and organize information from various documents, making it easier for users to navigate and understand large volumes of data.
5. Sentiment analysis: The tool can analyse the tone and sentiment of text within documents, providing valuable insights into the emotional context of the information.

By leveraging these AI-powered capabilities, Afforai transforms the document analysis process, enabling businesses to quickly extract valuable insights, make data-driven decisions, and improve overall efficiency in handling complex document-based tasks.

Using Affori for Business Document Analysis

Affori was designed with scientific papers in mind. I've used another tool, Consensus, to quickly research and answer questions when writing blog articles, but that uses its own data sources.

Afforai is different in that you can add your own documents. In the intended use, this makes a lot of sense: you can upload specific research papers and have the AI help you with a multi-study analysis of the research.

As it's been 20+ years since I had to research and write about scientific subjects at university I decided to test the platform using a different type of complex documentation: company accounts.

I uploaded the individual files for 2019 - 2023 filed accounts into the system and gave the AI the following prompt:

Provide a strategist overview of the companies performance between 2019 and 2023. Ensure the explanation is in easy to understand terms. Finish with an analysis of if this is a) a good place to seek employment and b) a good company for people to invest in.

The AI Analysis

The following is the AI output: I've not changed any of the wording but have cleaned up the formatting:

Strategic Overview of Company Performance (2019-2023)

Financial Performance:

Turnover and Profit

• In 2019, the company had a turnover of ￡165,953k and a profit of ￡7,745k.
• By 2020, the turnover increased to ￡176,386k, but the company reported an operating loss of ￡1,158k.
• In 2021, the turnover decreased to ￡169,703k, but the company experienced a loss of ￡10,823k.
• In 2022, the turnover further increased to ￡182,213k, but the company reported a significant loss of ￡145,973k.
• In 2023, the turnover increased to ￡219,930k, and the company reported a loss of ￡41,826k.

Gross Profit and Administrative Expenses:

• Gross profit decreased due to changes in product mix, and administrative expenses increased as the company invested in staffing and infrastructure to support its customer base.

Net Assets:

• The company's net assets increased from ￡54,929k in 2019 to ￡69,950k in 2020 but decreased to ￡59,127k in 2021 and further to ￡86,846k in 2022.

Principal Risks and Uncertainties:

• The company faces risks such as changes in the regulatory environment, reduction in fixed line telephone services usage, and the impact of macro-economic factors like inflation.

Financial Risks:

• The company's exposure to financial risks, including credit risk, cash flow risk, and liquidity risk, is managed at the group level.

COVID-19 Impact:

• The company faced challenges due to the COVID-19 pandemic, with additional expenses and bad debt charges affecting its financial performance.

Analysis for Employment and Investment

a) Employment Perspective:

• Employment Stability: The company's fluctuating financial performance and operational losses in recent years may raise concerns about job stability.
• Investment in Infrastructure: The company's investments in staffing and wider infrastructure could indicate potential opportunities for career growth and development.
• COVID-19 Adaptation: The company's ability to maintain operations during the pandemic shows resilience and adaptability, which could be positive for job security.

b) Investment Perspective:

• Financial Performance: The consistent losses and decreasing net assets over the years raise concerns about the company's financial health and long-term sustainability.
• Market Competitiveness: The company's efforts to review commercial offerings and diversify into utilities show a proactive approach to remain competitive in the market.
• Risk Factors: The identified risks, such as regulatory changes and macro-economic factors, could impact the company's future performance and investment returns.

In conclusion, based on the fluctuating financial performance, ongoing losses, and identified risks, it may not be an ideal time to consider investing in the company.

From an employment perspective, while there are opportunities for growth and stability, the financial uncertainties and challenges faced by the company should be carefully considered before seeking employment.

Afforai isn't able to provide graphs, but it can provide tables, so as a follow-up request I asked it to provide a table illustrating the change in profit and turnover:

It's a simple request, but a time saving one.

I then asked what are the key reasons for the shift from profit to loss?

The shift from profit to loss in the company's financial performance can be attributed to several key reasons based on the information provided in the documents:

Impact of COVID-19:

• The company faced challenges due to the COVID-19 pandemic, which led to additional expenses and bad debt charges affecting its financial performance(n.d.-a, n.d.-b).
• The pandemic-related lockdown restrictions negatively impacted revenue, with decreases in variable elements of revenue, particularly in the use of utility services, as many customers experienced restrictions(Gillian Hill 3667643 No.1 Dovecote Old Hall Road Sale M33 2GS PricewaterhouseCoopers LLP Chartered Accountants and Statutory Auditors 1 Hardman Square Manchester M3 3EB Lloyds Bank 53 King Street Manchester M2 4LQ PricewaterhouseCoopers LLP Chartered Accountants and Statutory Auditors Gillian Hill 3667643 No.1 Dovecote Old Hall Road Sale, 1 Hardman Square, 53 King Street M33 2GS, M3 3EB, M2 4LQ Manchester, Lloyds Bank, Manchester, n.d., n.d.).

Change in Product Mix:

• Gross profit decreased due to changes in the product mix, with a higher proportion of revenue coming from utility products, which are recharged to relevant group companies holding regulatory supply licenses(n.d.-a, n.d.-b).

Exceptional Expenses:

• The company incurred non-recurring administrative expenses related to consultancy and advisor fees for strategic projects, acquisition-related salary costs, and additional expenses due to the pandemic, impacting its financial performance(n.d.-a, n.d.-b).

Operating Losses:

• Operating losses increased due to exceptional expenses, internal investments, and changes in product mix, leading to a significant shift from profit to loss in the financial statements(Gillian Hill 3667643 No.1 Dovecote Old Hall Road Sale M33 2GS PricewaterhouseCoopers LLP Chartered Accountants and Statutory Auditors 1 Hardman Square Manchester M3 3EB Lloyds Bank 53 King Street Manchester M2 4LQ PricewaterhouseCoopers LLP Chartered Accountants and Statutory Auditors Gillian Hill 3667643 No.1 Dovecote Old Hall Road Sale, 1 Hardman Square, 53 King Street M33 2GS, M3 3EB, M2 4LQ Manchester, Lloyds Bank, Manchester, n.d., n.d.-a, n.d.-b).

Revenue Impact:

• The company's revenue was impacted by changes in customer demand, reduction in customer collections, and delays in operational improvements, affecting its ability to generate profits(Gillian Hill 3667643 No.1 Dovecote Old Hall Road Sale M33 2GS PricewaterhouseCoopers LLP Chartered Accountants and Statutory Auditors 1 Hardman Square Manchester M3 3EB Lloyds Bank 53 King Street Manchester M2 4LQ PricewaterhouseCoopers LLP Chartered Accountants and Statutory Auditors Gillian Hill 3667643 No.1 Dovecote Old Hall Road Sale, 1 Hardman Square, 53 King Street M33 2GS, M3 3EB, M2 4LQ Manchester, Lloyds Bank, Manchester, n.d., n.d.).

Energy Price Shocks:

• Sustained higher energy prices and energy price shocks in the market had an adverse impact on the company's financial performance, leading to operational challenges and liquidity concerns(n.d.).

Bad Debt Charges:

• The company incurred additional bad debt charges related to legacy issues caused by the impact of the COVID-19 pandemic, further contributing to the shift from profit to loss(n.d.).

In summary, the shift from profit to loss in the company's financial performance can be attributed to a combination of factors, including the impact of the COVID-19 pandemic, changes in product mix, exceptional expenses, operating losses, revenue challenges, energy price shocks, and bad debt charges. These factors collectively influenced the company's ability to maintain profitability and resulted in financial losses over the specified period.

The system provided references in the response which are clickable (the text in bold) and take you to the relevant section of documentation (obviously these don't work outside the platform, I've left them in to demonstrate the full output provided).

AI Analysis use cases

Now you've seen the results, I'm sure you can imagine some of the possibilities this opens up for business analysis:

1. Rapid financial health assessment: AI can quickly analyse years of financial data to provide insights on trends, ratios, and overall financial health.
2. Competitive benchmarking: By feeding in industry data alongside company financials, AI can compare performance against competitors and industry averages.
3. Risk identification: AI can flag potential risks or areas of concern by identifying unusual patterns or deviations in financial data.
4. Forecasting and predictive analysis: Using historical data, AI can generate forecasts and predict future performance under various scenarios.
5. Strategic decision support: AI can provide data-driven insights to support strategic decisions like expansion, diversification, or cost-cutting measures.
6. Due diligence support: For mergers and acquisitions, AI can perform rapid financial and operational due diligence on target companies.
7. Customized reporting: AI can generate tailored reports for different stakeholders, from high-level executive summaries to detailed analytical breakdowns.
8. Fraud detection: AI could be used to identify potential fraudulent activities by detecting anomalies in financial transactions and reporting.
9. Investor relations support: AI can help prepare comprehensive, data-driven responses to investor queries and concerns.

This technology could revolutionize how businesses conduct financial analysis, making it faster, more comprehensive, and more accessible to a wider range of users within an organization.

Final Thoughts

It's been an interesting exercise checking in on a company I previously worked for. The entire exercise took less time than writing up this article!

I've found Affori to be easy to use, even for applications outside the primary focus of academic research projects.

If you'd like to give the platform a try you can do so by signing up on the Afforai website.