FAQs
FAQs
How does Spindle compare to other classes of tools?
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Spreadsheets
(Excel, Google Sheets, etc.)
1. Why do spreadsheets make it hard to adapt to changing financial models?
Once you commit to a specific model architecture, structure, or schema in a spreadsheet, making changes (“performing surgery”) can be so difficult that it’s often easier to start over from scratch.
This is especially problematic for Strategic Finance problems, where the true nature of the challenge often becomes clearer only as you work through it.
Instead, Strategic Finance teams benefit from tools that can scale with the complexity of the problem and pivot with the needs of the model.
With Spindle AI, teams no longer need to choose between the power of planning tools or the flexibility of Excel: they can have both.
2. Why do spreadsheets limit scenario modeling?
While it's (usually) practical to model 3-5 scenarios in spreadsheets, finance teams often need to evaluate hundreds (or thousands) of scenarios to understand risk and potential outcomes.
However, doing so in spreadsheets requires so many hours that the exercise might no longer be useful — and that's before the time it takes to evaluate and compare these scenarios.
This constraint limits decision-making agility and forces teams to make choices based on incomplete insights.
Instead, it's more practical (and efficient) to use Spindle AI, which can generate the full scenario landscape and help analysts select the best scenarios from the bunch.
3. Why are spreadsheets risky for financial decision-making?
For high-stakes financial decisions, spreadsheets lack the reliability and auditability required for precision. Spreadsheets introduce hidden risks and delays due to:
Human error – Small mistakes in formulas, references, and inputs can lead to major downstream miscalculations.
Lack of governance – Spreadsheets contain no structured approval process, which means it's easy to accidentally make errors.
Fragmented logic – When multiple users apply different rules, discrepancies emerge.
Dedicated planning tools and solutions like Spindle AI solve these problems.
4. Why is AI-driven forecasting impossible in spreadsheets?
Spreadsheets don’t support AI-driven forecasting (at least, not today), meaning finance teams are stuck manually crunching numbers and running their own forecasts.
But with the power of a generative business analytics platform like Spindle AI, teams can generate smarter, faster insights automatically, eliminating the need for time-consuming manual calculations and guesswork.
5. How do spreadsheets lead to delayed decision-making?
1. Finance teams spend as much as 75% of their time cleaning data, fixing errors, and maintaining models instead of analyzing insights.
2. Running scenarios requires manual input, slowing down responsiveness. This manual input is often inexact or requires derivation: what are your levels and drivers, and what do they affect? What are your sensitivities? Have you tested this model? Does it make sense to use for a realistic scenario?
3. Spreadsheets aren’t built for speed — large files can take minutes or hours to recalculate, delaying critical decisions.
For real-time, high-impact financial strategy, spreadsheets simply don’t move fast enough. But a purpose-built AI-powered tool like Spindle AI does.
FP&A
(Anaplan, Workday Adaptive Planning, PIgment, OneStream, etc.)
BI Tools
(Tableau, Databricks, Looker, etc.)
1. Why don’t BI tools support scenario modeling?
BI tools are built for reporting, not forecasting or simulation. They're backward-looking tools; great for figuring out “what happened” but not for “what's next.” Moreover, BI tools:
Can’t handle multiple “what-if” scenarios or model interdependent variables.
Require manual data adjustments instead of allowing dynamic exploration.
Don’t support real-time trade-off analysis to test different strategies.
Finance teams need tools that enable fast, adaptive scenario planning — not static reporting.
2. Why is it difficult to use BI tools for strategic finance planning?
Finance teams need forward-looking, dynamic models, but BI tools are static and backward-looking.
Because BI tools focus on aggregated, high-level summaries, whereas finance teams need granular, detailed modeling for scenario planning, you get a classic mismatch where BI tools simply aren't designed for the problem you need them to solve.
But a Generative Business Analytics Platform like Spindle AI was built specifically for strategic finance, not just historical reporting.
3. Why do BI tools struggle with real-time decision-making?
BI tools rely on batch data updates, meaning insights are often outdated by the time they’re reviewed. They:
Lack real-time modeling capabilities, preventing finance teams from adjusting assumptions on the fly.
Require manual dashboard updates for any new insights, slowing down strategic agility.
Make financial scenario testing inefficient, since teams can’t instantly model different outcomes.
Spindle AI provides real-time, AI-powered financial modeling, enabling instant adjustments to changing conditions.
4. Why do BI tools struggle to connect financial data with operational drivers?
BI tools don’t integrate financial models with operational metrics like sales performance, churn, or capacity planning in real time.
This makes it difficult to see the relationship between business actions and financial outcomes.
Moreover, BI tools often require separate reports for different business units, rather than providing a single integrated view that connects financial and operational data.
Spindle AI bridges this gap by connecting finance with operational realities, allowing for a more accurate and holistic view of the business.
5. Why do BI tools make financial scenario planning inefficient?
Scenario planning requires real-time assumption adjustments, which BI tools don’t support. Moreover, BI dashboards require manual updates every time a variable changes.
All this means running multiple interdependent scenarios is nearly impossible — and that's before considering that BI tools don’t allow for dynamically linking variables across different reports, leading to fragmented analyses that require a lot of manual piecing together.
Spindle AI lets finance teams test multiple future scenarios instantly, without manual rework.
Chat GPT & LLMs
(ChatGPT, Anthropic, DeepSeek, etc.)
1. Why do ChatGPT and similar LLMs struggle with accuracy in financial modeling?
While LLMs (Large Language Models) like ChatGPT are known to hallucinate, meaning they:
Generate plausible but incorrect financial insights that are difficult to detect.
Misinterpret complex financial concepts, leading to unreliable analysis.
Struggle with precision and factual consistency, both of which are critical for Strategic Finance.
Even experienced finance professionals may not immediately recognize errors, making LLM-generated insights risky for financial decision-making.
While LLMs are suitable for basic number-crunching…you'd rather use a spreadsheet for that, wouldn't you?
2. Why doesn’t ChatGPT perform well with numbers and financial calculations?
ChatGPT doesn’t do math natively — it recognizes patterns rather than performing real computations. As a result, it struggles with.
Complex financial equations and optimizations.
Multi-step financial projections.
Scenario modeling that requires precise numerical accuracy.
LLMs also lack true numerical reasoning — they don’t understand the relationships between numbers the way a finance professional (or any other human) does, making them unsuitable for quantitative analysis.
ChatGPT and other LLMs can help you explain the results of your analysis to your colleagues and win them over — but that's a language and communication challenge, not a numerical one.
3. Why is ChatGPT’s analysis limited in strategic finance?
While ChatGPT can summarize general finance concepts, it struggles with real-world enterprise finance. It:
Lacks domain-specific financial logic tailored to Strategic Finance teams.
Can’t dynamically model trade-offs, constraints, or evolving financial conditions.
Doesn’t understand cause-and-effect relationships in financial decision-making beyond generic insights.
Unlike dedicated financial modeling tools, ChatGPT can’t build or refine strategic finance models — it only provides static responses based on broad patterns.
4. Can ChatGPT and other LLMs be used for financial decision-making?
No — ChatGPT and other LLMs should not be relied on for making actual financial decisions. While they can provide conceptual insights and basic financial explanations, they are unable to:
Validate assumptions
Quantify trade-offs,
Analyze live financial data
Finance teams need dynamic, structured decision-making tools, not just narrative responses. ChatGPT is useful for brainstorming but fundamentally unsuitable for enterprise finance decisions.
5. Why does ChatGPT struggle with constrained financial optimization?
Finance teams often work with real-world constraints like budget limits, signed contracts, and limited operational capacity.
ChatGPT can’t solve mathematical optimization problems, making it useless for capital allocation, portfolio optimization, or resource planning.
For these problems, Spindle AI can run thousands of constrained scenarios in the time it takes you to reheat your lunch at the office, while ChatGPT requires manual input for each individual case — slowing down strategic decision-making.
And even then, ChatGPT can hallucinate, so you can't trust that it actually applied the constraints at all.
Spreadsheets
(Excel, Google Sheets, etc.)
1. Why do spreadsheets make it hard to adapt to changing financial models?
Once you commit to a specific model architecture, structure, or schema in a spreadsheet, making changes (“performing surgery”) can be so difficult that it’s often easier to start over from scratch.
This is especially problematic for Strategic Finance problems, where the true nature of the challenge often becomes clearer only as you work through it.
Instead, Strategic Finance teams benefit from tools that can scale with the complexity of the problem and pivot with the needs of the model.
With Spindle AI, teams no longer need to choose between the power of planning tools or the flexibility of Excel: they can have both.
2. Why do spreadsheets limit scenario modeling?
While it's (usually) practical to model 3-5 scenarios in spreadsheets, finance teams often need to evaluate hundreds (or thousands) of scenarios to understand risk and potential outcomes.
However, doing so in spreadsheets requires so many hours that the exercise might no longer be useful — and that's before the time it takes to evaluate and compare these scenarios.
This constraint limits decision-making agility and forces teams to make choices based on incomplete insights.
Instead, it's more practical (and efficient) to use Spindle AI, which can generate the full scenario landscape and help analysts select the best scenarios from the bunch.
3. Why are spreadsheets risky for financial decision-making?
For high-stakes financial decisions, spreadsheets lack the reliability and auditability required for precision. Spreadsheets introduce hidden risks and delays due to:
Human error – Small mistakes in formulas, references, and inputs can lead to major downstream miscalculations.
Lack of governance – Spreadsheets contain no structured approval process, which means it's easy to accidentally make errors.
Fragmented logic – When multiple users apply different rules, discrepancies emerge.
Dedicated planning tools and solutions like Spindle AI solve these problems.
4. Why is AI-driven forecasting impossible in spreadsheets?
Spreadsheets don’t support AI-driven forecasting (at least, not today), meaning finance teams are stuck manually crunching numbers and running their own forecasts.
But with the power of a generative business analytics platform like Spindle AI, teams can generate smarter, faster insights automatically, eliminating the need for time-consuming manual calculations and guesswork.
5. How do spreadsheets lead to delayed decision-making?
1. Finance teams spend as much as 75% of their time cleaning data, fixing errors, and maintaining models instead of analyzing insights.
2. Running scenarios requires manual input, slowing down responsiveness. This manual input is often inexact or requires derivation: what are your levels and drivers, and what do they affect? What are your sensitivities? Have you tested this model? Does it make sense to use for a realistic scenario?
3. Spreadsheets aren’t built for speed — large files can take minutes or hours to recalculate, delaying critical decisions.
For real-time, high-impact financial strategy, spreadsheets simply don’t move fast enough. But a purpose-built AI-powered tool like Spindle AI does.
FP&A
(Anaplan, Workday Adaptive Planning, PIgment, OneStream, etc.)
BI Tools
(Tableau, Databricks, Looker, etc.)
1. Why don’t BI tools support scenario modeling?
BI tools are built for reporting, not forecasting or simulation. They're backward-looking tools; great for figuring out “what happened” but not for “what's next.” Moreover, BI tools:
Can’t handle multiple “what-if” scenarios or model interdependent variables.
Require manual data adjustments instead of allowing dynamic exploration.
Don’t support real-time trade-off analysis to test different strategies.
Finance teams need tools that enable fast, adaptive scenario planning — not static reporting.
2. Why is it difficult to use BI tools for strategic finance planning?
Finance teams need forward-looking, dynamic models, but BI tools are static and backward-looking.
Because BI tools focus on aggregated, high-level summaries, whereas finance teams need granular, detailed modeling for scenario planning, you get a classic mismatch where BI tools simply aren't designed for the problem you need them to solve.
But a Generative Business Analytics Platform like Spindle AI was built specifically for strategic finance, not just historical reporting.
3. Why do BI tools struggle with real-time decision-making?
BI tools rely on batch data updates, meaning insights are often outdated by the time they’re reviewed. They:
Lack real-time modeling capabilities, preventing finance teams from adjusting assumptions on the fly.
Require manual dashboard updates for any new insights, slowing down strategic agility.
Make financial scenario testing inefficient, since teams can’t instantly model different outcomes.
Spindle AI provides real-time, AI-powered financial modeling, enabling instant adjustments to changing conditions.
4. Why do BI tools struggle to connect financial data with operational drivers?
BI tools don’t integrate financial models with operational metrics like sales performance, churn, or capacity planning in real time.
This makes it difficult to see the relationship between business actions and financial outcomes.
Moreover, BI tools often require separate reports for different business units, rather than providing a single integrated view that connects financial and operational data.
Spindle AI bridges this gap by connecting finance with operational realities, allowing for a more accurate and holistic view of the business.
5. Why do BI tools make financial scenario planning inefficient?
Scenario planning requires real-time assumption adjustments, which BI tools don’t support. Moreover, BI dashboards require manual updates every time a variable changes.
All this means running multiple interdependent scenarios is nearly impossible — and that's before considering that BI tools don’t allow for dynamically linking variables across different reports, leading to fragmented analyses that require a lot of manual piecing together.
Spindle AI lets finance teams test multiple future scenarios instantly, without manual rework.
Chat GPT & LLMs
(ChatGPT, Anthropic, DeepSeek, etc.)
1. Why do ChatGPT and similar LLMs struggle with accuracy in financial modeling?
While LLMs (Large Language Models) like ChatGPT are known to hallucinate, meaning they:
Generate plausible but incorrect financial insights that are difficult to detect.
Misinterpret complex financial concepts, leading to unreliable analysis.
Struggle with precision and factual consistency, both of which are critical for Strategic Finance.
Even experienced finance professionals may not immediately recognize errors, making LLM-generated insights risky for financial decision-making.
While LLMs are suitable for basic number-crunching…you'd rather use a spreadsheet for that, wouldn't you?
2. Why doesn’t ChatGPT perform well with numbers and financial calculations?
ChatGPT doesn’t do math natively — it recognizes patterns rather than performing real computations. As a result, it struggles with.
Complex financial equations and optimizations.
Multi-step financial projections.
Scenario modeling that requires precise numerical accuracy.
LLMs also lack true numerical reasoning — they don’t understand the relationships between numbers the way a finance professional (or any other human) does, making them unsuitable for quantitative analysis.
ChatGPT and other LLMs can help you explain the results of your analysis to your colleagues and win them over — but that's a language and communication challenge, not a numerical one.
3. Why is ChatGPT’s analysis limited in strategic finance?
While ChatGPT can summarize general finance concepts, it struggles with real-world enterprise finance. It:
Lacks domain-specific financial logic tailored to Strategic Finance teams.
Can’t dynamically model trade-offs, constraints, or evolving financial conditions.
Doesn’t understand cause-and-effect relationships in financial decision-making beyond generic insights.
Unlike dedicated financial modeling tools, ChatGPT can’t build or refine strategic finance models — it only provides static responses based on broad patterns.
4. Can ChatGPT and other LLMs be used for financial decision-making?
No — ChatGPT and other LLMs should not be relied on for making actual financial decisions. While they can provide conceptual insights and basic financial explanations, they are unable to:
Validate assumptions
Quantify trade-offs,
Analyze live financial data
Finance teams need dynamic, structured decision-making tools, not just narrative responses. ChatGPT is useful for brainstorming but fundamentally unsuitable for enterprise finance decisions.
5. Why does ChatGPT struggle with constrained financial optimization?
Finance teams often work with real-world constraints like budget limits, signed contracts, and limited operational capacity.
ChatGPT can’t solve mathematical optimization problems, making it useless for capital allocation, portfolio optimization, or resource planning.
For these problems, Spindle AI can run thousands of constrained scenarios in the time it takes you to reheat your lunch at the office, while ChatGPT requires manual input for each individual case — slowing down strategic decision-making.
And even then, ChatGPT can hallucinate, so you can't trust that it actually applied the constraints at all.
Spreadsheets
(Excel, Google Sheets, etc.)
1. Why do spreadsheets make it hard to adapt to changing financial models?
Once you commit to a specific model architecture, structure, or schema in a spreadsheet, making changes (“performing surgery”) can be so difficult that it’s often easier to start over from scratch.
This is especially problematic for Strategic Finance problems, where the true nature of the challenge often becomes clearer only as you work through it.
Instead, Strategic Finance teams benefit from tools that can scale with the complexity of the problem and pivot with the needs of the model.
With Spindle AI, teams no longer need to choose between the power of planning tools or the flexibility of Excel: they can have both.
2. Why do spreadsheets limit scenario modeling?
While it's (usually) practical to model 3-5 scenarios in spreadsheets, finance teams often need to evaluate hundreds (or thousands) of scenarios to understand risk and potential outcomes.
However, doing so in spreadsheets requires so many hours that the exercise might no longer be useful — and that's before the time it takes to evaluate and compare these scenarios.
This constraint limits decision-making agility and forces teams to make choices based on incomplete insights.
Instead, it's more practical (and efficient) to use Spindle AI, which can generate the full scenario landscape and help analysts select the best scenarios from the bunch.
3. Why are spreadsheets risky for financial decision-making?
For high-stakes financial decisions, spreadsheets lack the reliability and auditability required for precision. Spreadsheets introduce hidden risks and delays due to:
Human error – Small mistakes in formulas, references, and inputs can lead to major downstream miscalculations.
Lack of governance – Spreadsheets contain no structured approval process, which means it's easy to accidentally make errors.
Fragmented logic – When multiple users apply different rules, discrepancies emerge.
Dedicated planning tools and solutions like Spindle AI solve these problems.
4. Why is AI-driven forecasting impossible in spreadsheets?
Spreadsheets don’t support AI-driven forecasting (at least, not today), meaning finance teams are stuck manually crunching numbers and running their own forecasts.
But with the power of a generative business analytics platform like Spindle AI, teams can generate smarter, faster insights automatically, eliminating the need for time-consuming manual calculations and guesswork.
5. How do spreadsheets lead to delayed decision-making?
1. Finance teams spend as much as 75% of their time cleaning data, fixing errors, and maintaining models instead of analyzing insights.
2. Running scenarios requires manual input, slowing down responsiveness. This manual input is often inexact or requires derivation: what are your levels and drivers, and what do they affect? What are your sensitivities? Have you tested this model? Does it make sense to use for a realistic scenario?
3. Spreadsheets aren’t built for speed — large files can take minutes or hours to recalculate, delaying critical decisions.
For real-time, high-impact financial strategy, spreadsheets simply don’t move fast enough. But a purpose-built AI-powered tool like Spindle AI does.
FP&A
(Anaplan, Workday Adaptive Planning, PIgment, OneStream, etc.)
BI Tools
(Tableau, Databricks, Looker, etc.)
1. Why don’t BI tools support scenario modeling?
BI tools are built for reporting, not forecasting or simulation. They're backward-looking tools; great for figuring out “what happened” but not for “what's next.” Moreover, BI tools:
Can’t handle multiple “what-if” scenarios or model interdependent variables.
Require manual data adjustments instead of allowing dynamic exploration.
Don’t support real-time trade-off analysis to test different strategies.
Finance teams need tools that enable fast, adaptive scenario planning — not static reporting.
2. Why is it difficult to use BI tools for strategic finance planning?
Finance teams need forward-looking, dynamic models, but BI tools are static and backward-looking.
Because BI tools focus on aggregated, high-level summaries, whereas finance teams need granular, detailed modeling for scenario planning, you get a classic mismatch where BI tools simply aren't designed for the problem you need them to solve.
But a Generative Business Analytics Platform like Spindle AI was built specifically for strategic finance, not just historical reporting.
3. Why do BI tools struggle with real-time decision-making?
BI tools rely on batch data updates, meaning insights are often outdated by the time they’re reviewed. They:
Lack real-time modeling capabilities, preventing finance teams from adjusting assumptions on the fly.
Require manual dashboard updates for any new insights, slowing down strategic agility.
Make financial scenario testing inefficient, since teams can’t instantly model different outcomes.
Spindle AI provides real-time, AI-powered financial modeling, enabling instant adjustments to changing conditions.
4. Why do BI tools struggle to connect financial data with operational drivers?
BI tools don’t integrate financial models with operational metrics like sales performance, churn, or capacity planning in real time.
This makes it difficult to see the relationship between business actions and financial outcomes.
Moreover, BI tools often require separate reports for different business units, rather than providing a single integrated view that connects financial and operational data.
Spindle AI bridges this gap by connecting finance with operational realities, allowing for a more accurate and holistic view of the business.
5. Why do BI tools make financial scenario planning inefficient?
Scenario planning requires real-time assumption adjustments, which BI tools don’t support. Moreover, BI dashboards require manual updates every time a variable changes.
All this means running multiple interdependent scenarios is nearly impossible — and that's before considering that BI tools don’t allow for dynamically linking variables across different reports, leading to fragmented analyses that require a lot of manual piecing together.
Spindle AI lets finance teams test multiple future scenarios instantly, without manual rework.
Chat GPT & LLMs
(ChatGPT, Anthropic, DeepSeek, etc.)
1. Why do ChatGPT and similar LLMs struggle with accuracy in financial modeling?
While LLMs (Large Language Models) like ChatGPT are known to hallucinate, meaning they:
Generate plausible but incorrect financial insights that are difficult to detect.
Misinterpret complex financial concepts, leading to unreliable analysis.
Struggle with precision and factual consistency, both of which are critical for Strategic Finance.
Even experienced finance professionals may not immediately recognize errors, making LLM-generated insights risky for financial decision-making.
While LLMs are suitable for basic number-crunching…you'd rather use a spreadsheet for that, wouldn't you?
2. Why doesn’t ChatGPT perform well with numbers and financial calculations?
ChatGPT doesn’t do math natively — it recognizes patterns rather than performing real computations. As a result, it struggles with.
Complex financial equations and optimizations.
Multi-step financial projections.
Scenario modeling that requires precise numerical accuracy.
LLMs also lack true numerical reasoning — they don’t understand the relationships between numbers the way a finance professional (or any other human) does, making them unsuitable for quantitative analysis.
ChatGPT and other LLMs can help you explain the results of your analysis to your colleagues and win them over — but that's a language and communication challenge, not a numerical one.
3. Why is ChatGPT’s analysis limited in strategic finance?
While ChatGPT can summarize general finance concepts, it struggles with real-world enterprise finance. It:
Lacks domain-specific financial logic tailored to Strategic Finance teams.
Can’t dynamically model trade-offs, constraints, or evolving financial conditions.
Doesn’t understand cause-and-effect relationships in financial decision-making beyond generic insights.
Unlike dedicated financial modeling tools, ChatGPT can’t build or refine strategic finance models — it only provides static responses based on broad patterns.
4. Can ChatGPT and other LLMs be used for financial decision-making?
No — ChatGPT and other LLMs should not be relied on for making actual financial decisions. While they can provide conceptual insights and basic financial explanations, they are unable to:
Validate assumptions
Quantify trade-offs,
Analyze live financial data
Finance teams need dynamic, structured decision-making tools, not just narrative responses. ChatGPT is useful for brainstorming but fundamentally unsuitable for enterprise finance decisions.
5. Why does ChatGPT struggle with constrained financial optimization?
Finance teams often work with real-world constraints like budget limits, signed contracts, and limited operational capacity.
ChatGPT can’t solve mathematical optimization problems, making it useless for capital allocation, portfolio optimization, or resource planning.
For these problems, Spindle AI can run thousands of constrained scenarios in the time it takes you to reheat your lunch at the office, while ChatGPT requires manual input for each individual case — slowing down strategic decision-making.
And even then, ChatGPT can hallucinate, so you can't trust that it actually applied the constraints at all.
Spreadsheets
(Excel, Google Sheets, etc.)
1. Why do spreadsheets make it hard to adapt to changing financial models?
Once you commit to a specific model architecture, structure, or schema in a spreadsheet, making changes (“performing surgery”) can be so difficult that it’s often easier to start over from scratch.
This is especially problematic for Strategic Finance problems, where the true nature of the challenge often becomes clearer only as you work through it.
Instead, Strategic Finance teams benefit from tools that can scale with the complexity of the problem and pivot with the needs of the model.
With Spindle AI, teams no longer need to choose between the power of planning tools or the flexibility of Excel: they can have both.
2. Why do spreadsheets limit scenario modeling?
While it's (usually) practical to model 3-5 scenarios in spreadsheets, finance teams often need to evaluate hundreds (or thousands) of scenarios to understand risk and potential outcomes.
However, doing so in spreadsheets requires so many hours that the exercise might no longer be useful — and that's before the time it takes to evaluate and compare these scenarios.
This constraint limits decision-making agility and forces teams to make choices based on incomplete insights.
Instead, it's more practical (and efficient) to use Spindle AI, which can generate the full scenario landscape and help analysts select the best scenarios from the bunch.
3. Why are spreadsheets risky for financial decision-making?
For high-stakes financial decisions, spreadsheets lack the reliability and auditability required for precision. Spreadsheets introduce hidden risks and delays due to:
Human error – Small mistakes in formulas, references, and inputs can lead to major downstream miscalculations.
Lack of governance – Spreadsheets contain no structured approval process, which means it's easy to accidentally make errors.
Fragmented logic – When multiple users apply different rules, discrepancies emerge.
Dedicated planning tools and solutions like Spindle AI solve these problems.
4. Why is AI-driven forecasting impossible in spreadsheets?
Spreadsheets don’t support AI-driven forecasting (at least, not today), meaning finance teams are stuck manually crunching numbers and running their own forecasts.
But with the power of a generative business analytics platform like Spindle AI, teams can generate smarter, faster insights automatically, eliminating the need for time-consuming manual calculations and guesswork.
5. How do spreadsheets lead to delayed decision-making?
1. Finance teams spend as much as 75% of their time cleaning data, fixing errors, and maintaining models instead of analyzing insights.
2. Running scenarios requires manual input, slowing down responsiveness. This manual input is often inexact or requires derivation: what are your levels and drivers, and what do they affect? What are your sensitivities? Have you tested this model? Does it make sense to use for a realistic scenario?
3. Spreadsheets aren’t built for speed — large files can take minutes or hours to recalculate, delaying critical decisions.
For real-time, high-impact financial strategy, spreadsheets simply don’t move fast enough. But a purpose-built AI-powered tool like Spindle AI does.
FP&A
(Anaplan, Workday Adaptive Planning, PIgment, OneStream, etc.)
BI Tools
(Tableau, Databricks, Looker, etc.)
1. Why don’t BI tools support scenario modeling?
BI tools are built for reporting, not forecasting or simulation. They're backward-looking tools; great for figuring out “what happened” but not for “what's next.” Moreover, BI tools:
Can’t handle multiple “what-if” scenarios or model interdependent variables.
Require manual data adjustments instead of allowing dynamic exploration.
Don’t support real-time trade-off analysis to test different strategies.
Finance teams need tools that enable fast, adaptive scenario planning — not static reporting.
2. Why is it difficult to use BI tools for strategic finance planning?
Finance teams need forward-looking, dynamic models, but BI tools are static and backward-looking.
Because BI tools focus on aggregated, high-level summaries, whereas finance teams need granular, detailed modeling for scenario planning, you get a classic mismatch where BI tools simply aren't designed for the problem you need them to solve.
But a Generative Business Analytics Platform like Spindle AI was built specifically for strategic finance, not just historical reporting.
3. Why do BI tools struggle with real-time decision-making?
BI tools rely on batch data updates, meaning insights are often outdated by the time they’re reviewed. They:
Lack real-time modeling capabilities, preventing finance teams from adjusting assumptions on the fly.
Require manual dashboard updates for any new insights, slowing down strategic agility.
Make financial scenario testing inefficient, since teams can’t instantly model different outcomes.
Spindle AI provides real-time, AI-powered financial modeling, enabling instant adjustments to changing conditions.
4. Why do BI tools struggle to connect financial data with operational drivers?
BI tools don’t integrate financial models with operational metrics like sales performance, churn, or capacity planning in real time.
This makes it difficult to see the relationship between business actions and financial outcomes.
Moreover, BI tools often require separate reports for different business units, rather than providing a single integrated view that connects financial and operational data.
Spindle AI bridges this gap by connecting finance with operational realities, allowing for a more accurate and holistic view of the business.
5. Why do BI tools make financial scenario planning inefficient?
Scenario planning requires real-time assumption adjustments, which BI tools don’t support. Moreover, BI dashboards require manual updates every time a variable changes.
All this means running multiple interdependent scenarios is nearly impossible — and that's before considering that BI tools don’t allow for dynamically linking variables across different reports, leading to fragmented analyses that require a lot of manual piecing together.
Spindle AI lets finance teams test multiple future scenarios instantly, without manual rework.
Chat GPT & LLMs
(ChatGPT, Anthropic, DeepSeek, etc.)
1. Why do ChatGPT and similar LLMs struggle with accuracy in financial modeling?
While LLMs (Large Language Models) like ChatGPT are known to hallucinate, meaning they:
Generate plausible but incorrect financial insights that are difficult to detect.
Misinterpret complex financial concepts, leading to unreliable analysis.
Struggle with precision and factual consistency, both of which are critical for Strategic Finance.
Even experienced finance professionals may not immediately recognize errors, making LLM-generated insights risky for financial decision-making.
While LLMs are suitable for basic number-crunching…you'd rather use a spreadsheet for that, wouldn't you?
2. Why doesn’t ChatGPT perform well with numbers and financial calculations?
ChatGPT doesn’t do math natively — it recognizes patterns rather than performing real computations. As a result, it struggles with.
Complex financial equations and optimizations.
Multi-step financial projections.
Scenario modeling that requires precise numerical accuracy.
LLMs also lack true numerical reasoning — they don’t understand the relationships between numbers the way a finance professional (or any other human) does, making them unsuitable for quantitative analysis.
ChatGPT and other LLMs can help you explain the results of your analysis to your colleagues and win them over — but that's a language and communication challenge, not a numerical one.
3. Why is ChatGPT’s analysis limited in strategic finance?
While ChatGPT can summarize general finance concepts, it struggles with real-world enterprise finance. It:
Lacks domain-specific financial logic tailored to Strategic Finance teams.
Can’t dynamically model trade-offs, constraints, or evolving financial conditions.
Doesn’t understand cause-and-effect relationships in financial decision-making beyond generic insights.
Unlike dedicated financial modeling tools, ChatGPT can’t build or refine strategic finance models — it only provides static responses based on broad patterns.
4. Can ChatGPT and other LLMs be used for financial decision-making?
No — ChatGPT and other LLMs should not be relied on for making actual financial decisions. While they can provide conceptual insights and basic financial explanations, they are unable to:
Validate assumptions
Quantify trade-offs,
Analyze live financial data
Finance teams need dynamic, structured decision-making tools, not just narrative responses. ChatGPT is useful for brainstorming but fundamentally unsuitable for enterprise finance decisions.
5. Why does ChatGPT struggle with constrained financial optimization?
Finance teams often work with real-world constraints like budget limits, signed contracts, and limited operational capacity.
ChatGPT can’t solve mathematical optimization problems, making it useless for capital allocation, portfolio optimization, or resource planning.
For these problems, Spindle AI can run thousands of constrained scenarios in the time it takes you to reheat your lunch at the office, while ChatGPT requires manual input for each individual case — slowing down strategic decision-making.
And even then, ChatGPT can hallucinate, so you can't trust that it actually applied the constraints at all.
Palantir for Strategic Finance & Business Analytics
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