KEY OUTPUT 14: The role of data, analytics, AI and standardisation on infrastructure cost assurance

The role of data analytics, artificial intelligence (AI), and process standardisation on infrastructure cost estimates and assurance is expected to be transformative. These technologies will significantly enhance the accuracy, efficiency, and reliability of infrastructure project costs, leading to better decision-making, cost control, and overall project success. Some key aspects to consider include:

• Data analytics will allow more organisations to analyse historical project data, market trends, and other relevant information to make better informed decisions. Predictive analytics can be employed to anticipate potential cost overruns or delays.
• AI algorithms can analyse project specifications, historical data, and various influencing factors to automate the cost estimation process. This not only improves accuracy but also accelerates the estimation phase.
• Establishing standardised processes for cost estimation and assurance ensures consistency across projects. This can lead to a more transparent and reliable reporting, making it easier to compare and benchmark different projects and sectors.

In Key Output 14, the steering group sought to obtain answers to four key questions below from a multi-disciplinary perspective as usual. Our KO14 report is a must-read.

• GDPR versus transparency and accountability on Alliance and open-book cost contracts.
• The future role of AI and data analytics in safeguarding data integrity and cost accuracy.
• How standardisation can help with productivity and cost assurance in construction.
• How past data can inform future cost estimates and drive increased asset value.

Cecelia is a qualified accountant of 25+yrs, and director of CFBL Consulting, a cost and strategy consultancy specialising in independent cost audits on infrastructure projects and strategy advisory. She has worked across commercial, finance and project control functions. A CIMA fellow and member of AICPAs, sustainability and R&D panel. She is a cost consultant and auditor experienced in the rail, technology, defence, renewable energy, and electrification sectors and has led audits on major programmes. She is chair of the multi-disciplinary steering group. Her career in construction spans 20 years and she has worked on high-profile projects such as HS2, Hinkley Point C, and Crossrail, and as a result, brings a wealth of knowledge and experience to this space.

GDPR versus transparency and accountability on Alliance and open-book cost contracts

On alliance and open-book cost contracts in the UK, striking a balance between GDPR compliance and maintaining transparency is pivotal. In practice this often involves anonymising and protecting sensitive data, particularly when dealing with people project cost, to preserve stakeholder privacy. Ensuring consent and non-disclosure is often essential, in line with GDPR’s principles of data protection and limitation by gathering only necessary data for project cost assurance and audits.

The implementation of robust data-sharing protocols, including encryption and access controls, is critical for the protection of sensitive information. GDPR mandates transparency in data processing, which supports the objective of openness on infrastructure projects. On audits it is imperative to continuously monitor compliance, especially considering the evolving nature of data analytics and AI technology. This includes keeping abreast of regulatory changes, adhering to data protection best practices, and incorporating ethical considerations in AI-powered cost estimations. Key accountability measures, like appointing a Data Protection Officer and carrying out regular impact assessments, are fundamental to transparently managing cost estimates in infrastructure projects.

The future role of AI and data analytics in safeguarding data integrity and cost accuracy

AI and data analytics will transform data and cost accuracy on major infrastructure projects. AI can enhance data quality through automated checks, ensuring accuracy with inputs and fortifying data integrity. It is crucial in data validation and cleansing, verifying and correcting datasets for accurate cost estimation. Limitations of AI include trust, integrity and judgement discerning sensitive data.

Real-time data monitoring by AI can quickly identify anomalies, enabling prompt corrective action. Predictive analytics, driven by AI, can help forecast future cost trends, aiding with risk identification. Machine learning can help analyse large datasets for cost optimisation and efficient resource allocation. AI can assesses project data for accurate risk assessments and proactive mitigation measures. Automated audit sampling and compliance checks can help ensure adherence to standards, enhancing reporting accuracy. AI can help with fraud detection and combined with blockchain technology, will strengthen data integrity. Continuous learning in AI systems will allow more accurate adjustments in cost estimates as projects phases evolve. AI’s decision support systems will help by providing real-time, actionable information, emphasising the need for open-book transparency and relevant commentary in AI models for accountability and cost reporting.

How standardisation can help with productivity and cost assurance in construction

Standardisation is vital for enhancing efficiency and assuring costs. It brings consistency in project execution, ensuring uniform adherence to established procedures and methodologies across various projects, thus elevating quality and productivity. This uniformity aids in efficient resource allocation, enabling teams to optimally utilise materials, equipment, and people through consistent specifications. The result is improved efficiency, increased cost savings, streamlined procurement, strategic purchasing decisions and reduced delay risks due to more standardised specifications.

Moreover, standardisation bolsters collaboration and communication by setting common standards for scoping, delivery and reporting. This establishes a shared understanding of project requirements among stakeholders, effectively minimising disputes and potential delays. It significantly contributes to enhanced quality by stipulating defined requirements for materials, construction methods, reducing faults, rework, and additional costs linked to these. Crucially, standardisation plays an essential role in risk mitigation, facilitating early identification and proactive management of potential risks throughout the project lifecycle. By employing standardised risk assessment checks and processes, project construction teams can minimise the chances of unexpected risks, cost and delays, contributing to increased cost assurance overall.

How past data can inform improved cost estimates and drive future increased value

Strategic use of historical data is essential in improving cost estimates and increasing the value of future construction projects. Historical cost analysis, by examining past projects with comparable parameters, offers valuable insights into cost patterns and factors influencing future costs, establishing a realistic baseline for future projections. Benchmarking, which compares the performance of current projects against past ones, can enable project managers identify areas for improvement in cost management and set more realistic budgets based on past project successes.

Parametric estimating is another useful tool, by employing statistical correlations between historical data and specific project factors to refine data-driven cost estimates and identify key cost influencers. This historical data is key for risk assessment and mitigation, where proactive mitigation measures are based on previously encountered risk, thereby enhancing the accuracy of cost projections. Lessons learned training can contribute to a culture of continuous improvement, documenting experiences and encouraging regular review and revisions of cost-estimating methods. Aligning these best practices with strategic goals ensures that investment decisions are data-driven and significantly contribute to achieving project objectives, thereby boosting overall project value.

Imran is an experienced chartered accountant (ACA) providing Cost and Commercial Assurance services to a variety of major infrastructure clients with prior experience in external and statutory audits from working at the U.K National Audit Office. He is currently the UK lead and global SME for Cost Assurance service development and delivery within Turner & Townsend global provider of consultancy services within the construction industry.

GDPR versus transparency and accountability on Alliance and open-book cost contracts

Data protection is critical in an audit process to ensure that information shared, accessed and validated is only done in a way that aligns with the purpose of the audit. GDPR, as the formal structure around the concepts of data protection, provides the requirement for key roles and responsibilities to be formed and to make sure that both the data provider and the data receiver are aware of the impact that data misuse could result in. GDPR should not be a barrier to audit and transparency but the principles of lawfulness and purpose limitation should be used to keep the data collated specific to the audit purpose.

How standardisation can help with productivity and cost assurance in construction

Standardisation of data, particularly across a client’s supply chain, can be massively useful to enable better quality benchmarking and understanding of where costs have been incurred. Unfortunately, the reality is that all major contractors in the industry work across multiple clients, sectors and data requirements which often means that configuring a cost management or ERP system to one data standard is not possible. This challenge is further compounded by the fact that some costs (materials, equipment) are rarely ordered in a business by works activity rather they are typically ordered by location or overall contract. For example, concrete may be ordered to a site location or coded to the overall contract reference but it will rarely be specifically ordered to a site for a specific purpose. It is then up to the commercial and site teams to produce a form of allocation of cost but this then potentially limits the ability to benchmark and compare cost performance. Assurance plays an important role in validating these processes via ‘set up’ audits of the supply chain to assess the quality, control and consistency of how a contractor manages and allocates their costs.

How past data can inform future cost estimates and drive increased asset value.

Using cost data intelligently is key for any organisation to enable better decision-making, identify areas of cost risk and opportunity and have more well-informed conversations with their supply chain. Past data forms part of this but this data requires refreshing on a reasonably regular basis rather than relying too heavily on inflation or uplift factors, more critical now than ever. Organisations often find themselves in Year 4 of a 5-year spending period struggling to obtain and use up-to-date data to utilise in their future planning, ultimately resorting to data collected many years ago to try and form a view on prices that will be incurred in the medium term. This has rarely proven to be a robust approach and creates a cycle of injecting poor quality in and receiving poor quality out. A data strategy is key to putting some structure around this challenge ensuring that all individuals within the organisation understand the intent around the use of data and that this is something that is able to be shared with the supply chain as early as possible. Data requirements and standards should ideally be made aware to the prospective supply chain during the tender phase and requirements and standards included in the contract to maximise compliance.

Claire is a lawyer specialising in construction and engineering disputes with experience in litigation, adjudication, mediation, and domestic and international arbitration. Claire acts for employers, contractors, and subcontractors across a wide range of projects including large-scale infrastructure projects, energy, utility, oil, and gas supply projects. Our global teams operate seamlessly to deliver the commercial know-how and strategic alignment that clients need from their advisers to help further their business interests. We shape our advice to the unique circumstances and challenges of each project and ensure the right people are in the right places to offer insight and certainty every time – from the day-to-day to complex, multi-jurisdictional transactions.

GDPR versus transparency and accountability on Alliance and open-book cost contracts

Some of the key principles that underpin alliance and open-book cost contracts are transparency and accountability, particularly when it comes to a contractor justifying and demonstrating the costs that have been incurred.

The nature of these contracts involve the employer (or an independent auditor) requesting documents to check and verify a contractor’s costs, this can often clash with the requirements of data protection legislation. For example, on the disputes side, we have experience of contractors refusing to provide certain evidence of payment (certainly with respect to NEC contracts) for fear of this disclosure putting them in breach of GDPR.

The GDPR prohibits a “Data Controller” from “processing” personal data unless one of the specified exceptions applies and a data processing agreement is entered into. Processing is defined as any “operation” being performed on personal data, such as collection recording, disclosure by transmission and use. It is easy to see therefore how data, such as an employee’s payslip, which might be requested to justify certain costs, would fall within the definition of personal data and be subject to GDPR.

So, how can technology assist with this and allow alliance and open book cost contracts to remain collaborative and transparent whilst ensuring compliance with legislation?

Both AI and data analytics could certainly assist with identifying and selecting the specific data required from payslips (using the example above) and scheduling this out, whilst maintaining the confidentiality of the employees. This would have the benefit of saving time and also provide reassurance to the reviewer/auditor that the data had been extracted from the documents requested, ensuring that the process remained transparent.

How standardisation can help with productivity and cost assurance in construction

Standardisation is the use of processes or procedures, products or components, with regularity and repetition.

In recent years, the construction industry has slowly embraced digitisation and standardisation of some of its key processes – something which has been accelerated by the pandemic and the need to increase productivity and work remotely or at a social distance.

Tools such as BIM, laser imaging detection, ground penetrating radar and also the use of digital twins, are used more and more often on construction projects to help digitally design projects before they are built. This helps to make better project decisions, help productivity (and minimise errors), reduce costs and improve quality.

However, there are certainly challenges with standardisation (particularly with respect to design and construction methods) mainly because projects can be so complex, varied and subject to their own specific conditions. Often there is no one size fits all, and it is important to recognise that some things need to be adaptable and flexible to suit the individual needs of a specific project.

The challenge is therefore to combine the quality of bespoke design and construction on a project with the benefits of these standardised processes, without resorting to the mass production of the past.

Provided that this inherent need for individuality and flexibility is recognised and can be included where required, then standardised processes can significantly benefit productivity and cost assurance.

Tom is the Head of Commercial Management at Southern Water. He is a chartered surveyor and manager of commercial, contract, and audit teams for large infrastructure clients and contractors. Specialising in the commercial management of construction contracts, claims, and disputes, He demonstrates a solid track record in infrastructure markets with experience in major energy, water, aviation, transport, and communications networks.

GDPR versus transparency and accountability on Alliance and open-book cost contracts

Where the parties sign up to a cost-reimbursable payment mechanism they have already implied a willingness to be transparent with costs. This means that the Client’s representative, most likely a QS or independent auditor, will be able to ‘follow the money’ to ensure that what is being billed in any application for payment is bone fide.

It is an industry convention that cost-reimbursable contracts will lead Clients to undertake a level of assurance including examining some sensitive data. As an example, under an NEC Option C contract the Client should be given assurance that the applied salary costs under the people cost component are shown on the contractor ledger and linked back to the actual payment made to an employee’s.

Under controlled conditions, the QS or Auditor should be able to witness evidence of these transactions. Resistance to allowing proper access to cost information, whether that be for GDPR or otherwise, must be overcome. GDPR is an important issue and the proper controls need to be in place, this does not equate to an excuse to be less transparent or accountable for providing verified cost information.

In any event, it makes sense for Contractors to promote transparent and efficient assurance processes from the outset of any project. There are some key reasons for this:

• Transparency may prevent unnecessary disallowed cost
• Less resources expended if the audit regime is transparent from the start
• Less chance of costly disputes
• Reputation

Gary is a Director at Blake Newport, an experienced construction commercial, contract and disputes consultant and Expert Witness. His specialisms include quantum analysis, contract administration, construction law, cost control, payment assessments and change management.

How standardisation can help with productivity and cost assurance in construction

As a consultant, I work with lots of different businesses across different sectors of construction – employers, main contractors, subcontractors and suppliers. In my experience, there is very little standardisation of cost data from project to project, let alone across the construction industry. There are broad similarities and there are existing standards like ICMS but the reality is, that each business will attempt to standardise in a manner that suits their needs. So a UK-based water treatment DBO contractor with a Spanish parent company will cost-code in such a way that the parent company may require and a UK family-run architectural metalwork subcontractor will cost-code for its board’s requirements and possibly for data analytics for insight purposes.

My thoughts are, as with most things, government-funded organisations must lead the way with standardisation which is a prerequisite for tendering for the whole supply chain on those projects. This will ensure that, over time and throughout the supply chain, there is a concerted effort to standardise. It would also be useful if government organisations across different industries were also aligned in adopting the same standard.

There would be numerous benefits. Staff will be more able to work and apply the same standards across different industries, AI and computer-aided analytics can be applied without first tidying up and cleansing the data and auditors can streamline their processes and outputs. Even data input could be improved because developers could build apps that site supervisors could use to input records in real time that would flow directly to a cost system that would also align with the standards and drive out wasted time and improve claims for loss, expense and prolongation costs.

How past data can inform future cost estimates and drive increased asset value

Linked to the above, if costs were captured in a standardised form, then this would loop back to project feasibility estimates, tender costs and tender evaluations. The construction industry is often criticised for overspending against budgets which would indicate the budgets are too low in the first place. This drives overly optimistic tenders and a claim culture. If the budgets were informed by better past data it would add realism to the decisions to go ahead with the project in the first place. Politics can often play a part in those decisions but data-led budgeting would help in providing transparency in those political decisions.

The other by-product would be that contractors’ contract sums would be more realistic, thus providing a sensible profit margin. This will reduce claim culture where contractors use post-contract claims to generate a profit. If there are fewer claims there will be less costs and downtime associated with prosecuting and defending claims.

Elliot has over 30 years of experience providing specialist pre- and post-contract commercial and project management services in the built environment across multiple sectors that include: infrastructure, residential, office, leisure, and mixed-use development projects. His pre-contract areas of specialism include development due diligence and feasibility assessment, value engineering, cost planning, procurement strategy development, contract form selection, and contract drafting. Post Contract: proven in-depth experience in leading large project and commercial management teams to deliver high-value, key complex programmes.

The future role of AI and data analytics in safeguarding data integrity and cost accuracy

Analytics: The use of AI technology introduces new ways for information and data to be collated, analysed and used for producing infrastructure cost estimates for the purposes of planning and preparing project budgets. The capability of AI to enhance computer take-off as well as its ability to integrate 2D with 3D Bim take-off will increase estimate accuracy. The use of AI will enable estimators to provide clients with real-time data, in a consistent and reliable format. The following broad technologies will help achieve efficiencies and optimisation in safeguarding data integrity and cost accuracy when producing estimates.

• Big data / cloud-based systems: Undertaking in-depth digitally aided estimates from BIM models and performing augmented analytics requires access to the whole dataset population. This is increasingly becoming more prevalent in some major programmes where clients are introducing data platforms to collate all cost data submitted by the Tier 1 supply chain. The challenge however remains to ensure that cost data and information exists layers down within the supply chain to a comprehensive level of granularity and that this is also able to be collated. Analysing the whole dataset allows for the estimator and assurers to provide a more thorough view to definitively mitigate against identified risks and to increase estimate accuracy. Whilst the terms of the contract will dictate the level of access/ detail required, there should be a collective effort by the client and supply chain to maintain a complete, consistent and comprehensive dataset to support the programme.

2. AI/Machine learning: Maintaining a consistent and widespread dataset allows for deep analytics to be designed and embedded within the estimating approach. Whilst logic can be created to automate standard take-off (such as counting doors or measuring floor areas) more detailed logic can be developed that is able to learn from data previously submitted and develop insights into how cost over time is being expended (such as looking at the movement/use of resources, supply chain involvement, behaviours of how cost is coded/described). This logic could be developed for each supplier and further through the supply chain allowing a much more forensic approach to cost analysis with relatively little effort compared to more manual efforts. Machine learning could also be used across multiple datasets and data points (i.e., cost, programme, and contract data) using both structured and unstructured data to continuously develop logic and a more robust estimating approach.

The importance of having a digitally savvy and skilled estimating team is paramount to ensure scripts and automated take-offs are built with sound logic and in compliance with best practices and appropriate e standard methods of measurement.

Reporting: Technology has already positively disrupted how cost estimates can be communicated to stakeholders. The use of software like Cost X, Cost Os and Power BI represents new ways to visually present information, findings and analytics that were not as prevalent ten years ago. Providing a visual representation of information, particularly in relation to large and complex datasets allows the user to more easily digest the key messages. Going further, dynamic and live estimate reporting and dashboards are providing a greater level of control to key individuals within the client organisation (as well as the supply chain) to make better-informed decisions using the whole dataset and not subsections of it.

Charlotte is a Commercial Manager at Atkins SNC Lavalin with 15 years of industry experience on complex major civil projects, working for contractor organisations such as Costain, Bam, Kier, Mace, and Atkins. She has worked as both an estimator and a commercial manager and brings with her experience significant contributions to the steering group. She is currently working on the East-West Rail Alliance as the Atkins commercial lead.

General Data Protection Regulations (GDPR) versus transparency and accountability on Alliance and open-book cost contracts

General Data Protection Regulations GDPR is a set of EU rules on data protection and privacy. The difficulty is with construction contracts in particular Alliance contracts where the requirement to review audit and assess each other’s data is contractual. An established approach, protocols and NDAs all need to be utilised when encountering an open book agreement.

• The future role of AI and data analytics in safeguarding data integrity and cost accuracy

AI is becoming an increasingly present aspect to major civils. I heard of a project that is trialling a system where AI is producing permits for the start of shift briefings and the start of works. The AI produces the basic template then it’s adjusted by the engineers. Commercial aspects are slow off the ground to embrace AI. We should be using AI to generate the monthly basic commercial requirements, such as zero payment certs to prevent smash and grabs. Self-billing and applications for payments could be able to utilise AI. This could save the commercial team valuable time.

I have experienced the desire from clients to build data bases, to input into an AI system. We were asked to populate actual costs against estimated target rates. This data along with other project data was collated and used to estimate future works. When I later got asked to price additional works, the client had a value generated from the AI. The result was the client had an unrealistic project value and I had to spend time detailing why my estimate had accounted for elements that the AI had not. My conclusion was that the data inputted by the projects must have been misleading. For AI to correctly work, the data source must be accurate.

How standardisation can help with productivity and cost assurance in construction

The introduction of standard contracts has improved the commercial management of construction projects. However, the basics are being missed. What tends to happen on the major projects I have worked on, is that contract terms and conditions (T&Cs) are passed down to the supply chain. The main contract normally has a clause stating, “Subcontracts can’t be any more onerous than the main contract”. This clause safeguards the supply chain to a point. The flow down of these T&Cs does make smaller less equipped parties accountable for picking up the requirements of the main contractor. Small and medium Enterprises (SMEs) have to deal with onerous requirements that are costlier than they might be aware of or require them to adapt sooner to the changes in the industry. These contractual requirements normally get slid in within the health and safety section or the works information of the contract documentation. The commercial team are usually unaware of them or reluctant to highlight them at the tender stage. Putting the onerous back on the supply chain to read the contracts carefully.

Standardising and implementing a fair open and honest approach would be beneficial to all in the industry. I feel more effort needs to be made in becoming more collaborative, upfront discussions, where all parties involved in the implementation of the contract agree on the requirements and how they are going to do the work. The management of those works, what’s required from whom and when. Clear attendances and plans from start to completion of the works.

The basics of commercial management are getting lost in all the processes, reports and requirements of the project. Attitudes such as “Understanding the deal”, “right first time”, and “best for the project” all need to become second nature as the H&S “don’t walk by” has become. This will provide all the rewards of getting the project done on time, to budget with minimal change.

How past data can inform future cost estimates and drive increased asset value

I usually price additional works or estimate new works using the rates on the project I am working on. Making sure I make allowances or apply various factors to that rate. Using historical data is only beneficial for budget estimates when applying a price range. The difficulty is without detailed project knowledge of that historical rate, you are guessing at the adjustment needed to the rate for the new project.

AI could be used to provide a high-level estimate for works without design maturity. If the data used by the AI is accurate. I find using past data to benchmark rates useful in demonstrating value for money to clients. Benchmarking can highlight and identify anomalies or reasons for inflated values. AI could be beneficial to do this task.