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Cost Estimation Guidance for AI Software Development Projects

Arlene Minkiewicz

BOEHM CSSE Forum November 2025

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Agenda

Introduction
Artificial Intelligence – history in software development
Traditional Cost Estimation – challenges for AI projects
Key Factors Influencing Cost and Effort in AI Projects
Real World Examples of Cost Related Challenges
Proposed Framework for Cost Estimation in AI Projects
Future work
Wrap Up

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Introduction

Powered by increasing technology and processing power, the use of Artificial Intelligence (AI) in Software Development projects is increasing
Traditional Software Cost Estimation methods fall short with AI Software development projects in several areas
This paper proposes a multi-phased framework intended to addresses these short falls
Key factors impacting cost and effort will be discussed
Emphasis is placed on the iterative and experimental nature of AI development

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Artificial Intelligence in SW Development Projects

In the 1950’s – AI centered around rule based systems and symbolic reasoning
Early efforts stymied by limited computation power and lack of real world data
In 80’s and 90’s - expert systems emerged as new applications of AI

Demonstrate potential of AI
Limited in adaptability and failed to provide generalized intelligence

In the 90’s and 2000’s – computing power and large amounts of data – ML techniques flourished
ML seems to be a significant game changer for AI

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Artificial Intelligence - the Journey

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Traditional Software Cost Estimation

Cost Estimation is an important aspect of program management for SW development projects
Software has become ubiquitous in almost every important system
Consider the F-35 Joint Strike Fighter military aircraft compared to the F-16

177x more computer code
300x software development effort/costs
90% of functionality is delivered by software

Software cost estimation relies on traditional size and complexity measures
Good estimation is critical for project success

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Where traditional software estimation methods fail…

Cutting edge technology
Unpredictable nature of data quality and availability
Unpredictable nature of modeling training and tuning– iterative process
Tasks such as data acquisition, cleaning, labeling and preprocessing – depend on the nature of the data not the type of project
High variability from project to project – model development tuning.
Specialized skill requirements – in addition to SME knowledge, skills such as data science, Machine Learning, Big Data, NLP, etc

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Where traditional software estimation methods fail…

Ongoing maintenance including monitoring, retraining and model drift
Lack of relevant historical data
Iterative development experimentation in AI projects
Addressing ethical and regulatory requirements
Traditional size and complexity measures are only part of the equation
Complexity of testing and validation
High computational costs and hardware needs

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Key factors Influencing Cost and Effort in AI Projects

Data Preparation – acquisition, cleaning and preparation
Algorithm Selection – choosing the right algorithms
Model Training – Resource requirements and time considerations
Deployment and Maintenance – challenges and ongoing costs in AI systems
Team expertise – role of skills and experience (high priced people)
Tool Selection and Maturity – effect of tools on development efficiency and cost
Application Platforms – considerations for platform specific costs
Integration Challenges – issues related to legacy systems and compatibility

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Real World Example - IBM Watson Health�

Project Goal

IBM Watson Health aimed to use AI to revolutionize healthcare by analyzing medical data, assisting doctors in making more accurate diagnoses and providing tailored treatment recommendations

Challenges

Project faced significant cost issues due to the complexity integrating AI into clinical environments
Need for constant data updates
Healthcare data is messy – often inconsistent or incomplete – training is costly and time consuming
High operational and maintenance costs
Data interoperability and integration technical limitations

Lessons Learned

AI projects in highly regulated fields like healthcare face substantial costs for data handling, compliance and customization to clinical workflows

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Real World Example - Tesla’s Autopilot and Full Self-Driving (FSD)

Project Goal

Tesla’s FSD software development project aimed to deliver a fully autonomous driving experience powered by advanced ML algorithms

Challenges

Developing and refining autonomous driving technology – substantial costs
Heavy investment in massive computational resources to run and train AI models
Expensive sensor technology
Global test fleets to ensure safety
Software needed continuous updates and vast quantities of real world driving data
Regulatory delays and liability issues

Lessons learned

Autonomous AI requires substantial up front investment in hardware and data
High costs for maintenance, model updates and regulatory compliance

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Real World Example - Googles Duplex for Automated Call Assistance

Project Goal

Designed to us AI to make calls for users interacting using Natural Language Processing – booking reservations and appointments

Challenges

Reliance on conversational AI drove significant costs due to the requirement for massive data processing power and extensive training data.
Heavy investment in in infrastructure and maintenance to support real time language understanding
Necessary scaling along languages and dialects also presented as a major cost driver
Ethical concerns around transparency needed to be addressed

Lessons Learned

Conversational AI can be very expensive especially human interactions are involved

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Framework for Cost Estimation in AI Projects

1. Project Scope Definition
2. Phased Development Approach

Phase 1 – Planning
Phase 2 – Data Preparation
Phase 3 – Algorithm Selection and Development
Phase 4 – Model Training and Tuning
Phase 5 – Deployment
Phase 6 – Maintenance and Monitoring

3. Cost Factors and Estimating Techniques
4. Dynamic and Iterative Estimation Process
5. Team Expertise and Resource Allocation
6. Risk Management
7. Case Study Review and Validation

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Framework for Cost Estimation in AI Projects – Project Scope Definition

Clear identification of project objectives - what are the project goals and desired outcomes
Outline the specific features and functionalities required
Identify clearly (or as clearly as possible) the data requirements

Types of data required
Volume
Quality
Potential Sources (and viability of acquisition of these sources)
Unique data considerations

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Framework for Cost Estimation in AI Projects – Phased Development Approach

Phase 1 – Planning

Conduct feasibility studies
Perform preliminary risk assessments
Identify stakeholders
Create communication plans and identify owner(s)
Develop the high-level project timeline (aware that this will likely change – work that into plan)

Phase 2 – Data Preparation

Estimate cost for data collection, cleaning and preparation – consider types of data, unique data characteristics, potential data sources, obstacles to data collection
Consider data acquisition methods – internal or external, data collection method(s), purchased data
Consider the associated costs with acquiring data (licensing, storage, etc.)

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Framework for Cost Estimation in AI Projects – Phased Development Approach

Phase 3 – Algorithm Selection and Development

Assess skills required and evaluate resource pool,
Evaluate potential algorithms based on the requirements of the project
Estimate time and resources for model development (consult SMEs and others involved in similar projects)
Consider the impacts of prototyping and testing

Phase 4 – Model Training and Tuning

Assess skills required and evaluate resource pool
Assess model complexity
Assess computational resources needed for training - consider things like GPU usage, cloud services, hardware needs, etc.)
Estimate time for parameter tuning and validation (consult SME’s and others involved in similar projects)

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Framework for Cost Estimation in AI Projects – Phased Development Approach

Phase 5 – Deployment

Consider the costs related to integrating the AI model into existing systems
Consider the on-going infrastructure costs (cloud services, on-premise servers, etc.)

Phase 6 – Maintenance and Monitoring

Consider ongoing costs for model maintenance, updates, retuning, performance monitoring
Identify metrics for success and tracking continuous improvements
Consider the on-going infrastructure costs (cloud services, on-premise servers, etc.)

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Framework for Cost Estimation in AI Projects – Cost Factors and Estimation Techniques

Traditional estimation methods are good but need to be tuned for the unique nature of AI development

Expert Judgement – experts are still valuable, but their opinions should be metered by experience with AI projects
Analogous Estimation – a poor choice if there are no similar projects
Parametric Estimation

Beware that these models are based on historical data and project parameters
Size and Complexity are still important but estimator should also consider things outside of their current model

Data issues (acquisition, cleansing, preprocessing, availability, quality)
Iterative nature of data and model tuning should be incorporated
Considerations comparable with estimating agile projects should rule

Bottoms Up Estimation - break down of costs is still valuable, but awareness of the iterative nature should contribute to estimate uncertainty – especially when organizational history is limited

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Framework for Cost Estimation in AI Projects – Dynamic and Iterative Estimation Process

It is not going to be easy for organizations starting out with AI
Successful estimators will embrace techniques similar to estimating agile projects

Expect things to change
Embrace this change
Communicate the risks to the stakeholders
Flexibility is key to successful projects
Probably won’t get it right the first time
Establish success metrics and track to learn from each project

Regularly update estimates based on ongoing findings

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Framework for Cost Estimation in AI Projects – Team Expertise and Resource Allocation

The resource skill set for AI projects are currently in high demand – incorporate salary differentials
Assess the skills and experience of team members, factoring in the fact that training and or new hires may be necessary to meet project requirements
Assess the maturity and capability of tools and platforms beings used
Consider the maturity and processing power of existing hardware
Incorporate where changes are likely into the estimate

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Framework for Cost Estimation in AI Projects – Risk Management and Case Study Review and Validation

Carefully consider potential risks specific to AI projects (data availability and quality, algorithm bias, iterative nature of AI development)
Stay away from a Point Estimate - give a range that recognizes the uncertainty in estimation decisions
Develop mitigation strategies and allocate contingency resources
Where possible – review similar relevant projects – understand how your estimates aligns with historical information (trust your history)
Incorporate lessons learned from previous AI projects to validate current estimate and establish credibility

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Next Steps

The framework proposed

50% reliant on Lessons learned from recent AI projects
50% common sense – something all estimators want to think when they estimate

While AI software development projects aren’t new – the speed with which the technology is advancing – makes history not necessarily relevant
The proposed framework gives a structure for estimators to consider how to estimate software projects and a structure for collecting data going forward
It has not been vetted
Next steps are to identify AI projects where quantitative data collection and lessons learned can be collected and used to improve the guidance
Looking for AI software projects willing to share data (in a perfect world) or lessons learned (in the real world)

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Wrap Up

Technology and data availability creates a situation where Artificial Intelligences applications are practical and valuable
Organizations intent on developing such apps need to revisit the way they estimate these projects
Factors such as the cutting nature of the technology, requirements for large amounts of data acquisition and handling, iterative process of data collection, model tuning and retraining are all things that may not be part of the history estimates are based on
A framework has been presented to help estimators consider these unique factor as appropriate to better aid decision makers in their organizations

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