The trend of investing in and purchasing new business technologies that utilise substantial data through machine learning and artificial intelligence software is growing rapidly. This investment can be costly and of significance to those in the built environment and property sector at a time when the sector is facing income and funding pressures. It is clearly important to spend selectively during periods of income pressures. With the daily plethora of new offerings and shiny toys that appear in new AI technology markets, many organisations may fear being left behind; even if their underlying systems are not ready for the challenges that utilisation of AI can bring.
Polished presentations, enthusiastic publicity, media heightened expectations, and a fear of being left behind may cause organisations to become reactive in their technology purchases. This phenomenon and reactiveness, reacting to hype, is not a new issue with innovative technology. AI is just one recent example. The Gartner Hype Cycle refers to the lifecycle of innovative technologies such as AI in five stages.
*Figure One: Gartner Hype Cycle representing the adoption and application lifecycle of specific technologies
(Source Category:Hype cycle - Wikimedia Commons.)
Initially, a new product attracts substantial media interest or "hype," resulting in inflated and unrealistic expectations or use cases. When the technology fails to meet these expectations (regardless of the underlying reasons), interest in the technology reduces to the point of disillusionment in it. However, as the technology further matures, effective applications and use cases of the technology become more evident, leading to its resurgence and recognition as valuable. It is also of interest to note that in many cases the various final uses of the technology may be fundamentally different than initially anticipated.
Evaluating the maturity of AI software within its development cycle presents challenges. Google search trend data does provide an indication that interest in the technology may be coming to a peak of media and public expectations, and maybe therefore the peak of inflated expectations. The following figures show the Google search interest in AI since 2004.
2004 - present - indicating the start of a significant spike in interest from 2012.
Last five years - interest in the term is still increasing, a new level of interest starts in 2023 with the public release of Gen AI products such as Microsoft Co-pilot.
Last 12 months - the level of interest in the term has remained but has not significantly increased.
(Source: Google trends analysis)
Where AI and its related technology lies on the Hype Cycle only remains important when we consider whether the return on investment from its use is currently being overestimated. Are we fearful that we would be purchasing something that fails to deliver our expectations?
This fear can be avoided altogether by adhering to fundamental business principles:
Technology and AI software solutions can indeed improve productivity and performance if they fit specific requirements. A professionally written business case for investment will enable you to establish this and importantly define the level of investment you may need. For instance, within a business case you can assess whether full access to a digital building twin with extensive features is necessary or if access to collated digitised estate data for improved strategic decision-making would suffice. The assessment will be based on “what do I need this for,” “how quickly do I want to start using it” and “what are my roll out capabilities.”
A business case that identifies the future needs will also include a performance indicator against which the technology delivery can be evaluated. Early monitoring of effective delivery will protect against technology that is simply not addressing the business need or has barriers to implementation.
We can all deliver new and needed capability but unless we know how to use it, and do actually use it, it is not effective. Training, communication, and evaluation will all add to the effectiveness of any newly introduced technology. The amount required in all these areas should be adjusted based on pre-defined delivery performance success indicators.
A well-defined business case for technology, defining the business need being addressed and monitoring of take up are essential to avoid reactive purchases and redundant spend. The good news is that this is not a new skill but the application of tried and tested business principles.
In the next article, we will consider your readiness to adopt and get the most from a new technology investment.
Artificial intelligence, generative AI and other digital technologies offer numerous potential benefits to the property and estates sector. Amongst other things these may include enhancing rental and sales marketing efforts, streamlining management tasks such as maintenance communication, and supporting investment decisions involving market analysis, site selection, space, and risk analysis. By leveraging AI and digital capabilities linked to a pre-defined business need, property and estate professionals have the potential to improve efficiency and make more informed decisions.
Ensuring recent technologies successfully deliver real benefits is importantly linked to first identifying a business need and then secondly preparing the structure, quality and reliability of your data:
Source: Provelio
The consistent labelling and storage of data enables the retrieval of data relevant to the query being raised. Standard labelling protocols across differing sources and time periods helps to maintain uniformity. A taxonomy (data class categorisation system) should be established.
Data should be of sufficient volume to correctly represent the scenarios it is meant to model and/or provide insight upon. Absent data can result in an incorrect emphasis and bias, in other words a sample size that is too small to extrapolate from. It is important to understand the extent of your underlying data population.
Any data input collected should be reviewed with built in control points for accuracy. Inaccurate data will of course lead to inaccurate actions, interpretations, or report generation. For example, outdated data should be removed to avoid AI and other digital technologies using this obsolete information. Equally extreme events or outliers should similarly be reviewed so that they do not have an unrealistic impact on your scenario modelling.
What you put in will directly affect the value of the data output. Data organisation and governance policies, and reviews, are an essential first stage to digital transformation. The best starting point is a gap analysis asking:
Data Security
Within a recent Microsoft report (2024 Business Opportunity of AI - November 24) concerns over data security and governance were cited as common reasons holding back on the adoption of generative AI such as Microsoft’s Co-pilot software. The concern being that AI searching a data system may return information to an operator without official access to that information because it has not been properly locked down or classified. Legacy issues in architecture, data labelling and access weaknesses should be addressed early on in your AI and digital transformation programme.
Implementing robust security measures, such access controls, and regular security audits, is essential when using AI and digital technologies which process and mine enormous amounts of data from your underlying data storage systems.
Conclusion
Reliable data, strong security, and clear governance are crucial for successful AI and digital technology systems. High data quality, robust security, and a well-designed architecture enhance AI performance and effectiveness in meeting information needs and actionable requirements.
In our final article we will consider how AI will impact the nature of the future estates’ professional.
In the rapidly evolving landscape of higher education, the role of university estates professionals is undergoing a significant transformation. The integration of AI and other digital technologies into estate management is important for modern universities aiming to optimise their operations, enhance sustainability, and improve the overall campus experience. Any integration should only be started against a pre-defined business need, a specified outcome requirement from the technology and should be in line with university strategy. The estates professional has a role in ensuring that traditional and trusted business case discipline remains in place.
This article explores the pivotal role of estates professionals in leveraging suitable AI and digital technologies for their work environment and the concept of the estates professional working in a rapidly expanding digital environment. We are entering the time of the augmented estates professional.
Estates professionals manage university facilities by maintaining infrastructure, ensuring statutory compliance, overseeing construction projects, optimising space use, reducing the estate's carbon footprint, and creating a great environment for students and staff. AI and digital technologies can make a significant impact in all these areas, by providing more visual, readily available and relevant data that is streamlined as a basis for decision making.
Data driven decision making provides actionable insights and enables the estates professional to make informed decisions. AI and digital tools may also facilitate better communication and collaboration among different departments within the university. For example, AI and digital platforms can integrate data from various sources and databases, providing a holistic view of the campus. By automating more routine tasks such as monitoring energy usage or scheduling maintenance, AI and digital technologies free up time for estates professionals to focus on more complex and strategic activities. An augmented professional leverages AI and technology to enhance their capabilities and decision-making processes.
In order to leverage AI and digital technology, estates professionals will need to ensure that they understand where their base data is located, in what existing systems, the sources of information and the validity and accuracy of that information. Combing separate systems and creating uniform classification systems will provide the underlying information for the decision-making process.
Many organisations have legacy systems that are not easily compatible with new AI technologies. Integrating AI into these systems can be costly and time-consuming. However, the use of bridge technologies and digital platforms can be used to bring disparate information together and are likely to be a cost-effective full alternative or interim solution. The estates professional should consider what information is required to make strategic decisions and where it can be found. Digital bridging platforms could then be designed to consolidate information allowing for the creation of a scenario or modelling system in accordance with a predefined method.
The augmented estates professional will also need to ensure they understand the principles behind the data models they are using and may need additional training for this. They need to be system thinkers and integrators. This doesn’t mean a new degree in modelling technology rather an intelligent understanding of the base data, filtering processes and operations. It is important to be able to spot errors, avoid unfairness and ensure that robust data governance policies and procedures are in place.
An augmented professional will understand the process and question the results produced by a model in line with their experience and knowledge:
There is a shortage of professionals with the necessary critical skills to implement and manage AI and digital systems. This skill gap can hinder the effective adoption of AI and digital technologies. To bridge the skill gap, professionals should engage in continuous learning. Traditionally this will have been focused on new techniques in building and maintenance, but it now needs to include understanding new technology changes and impacts in principle.
Estates professionals are used to working with other faculties, schools, departments and business functions within the university. They will now need to work closely with IT teams to ensure seamless integration of the new systems with existing IT infrastructure. This collaboration can help mitigate the challenges associated with legacy system updates. New IT coordinator roles based in the department may be needed to bridge the different traditional modes of operation between the two teams. Without collaboration new technology projects may stall or be delayed whilst the IT team evaluates security and compliance requirements separately. The estates IT coordinator will already be aware of these requirements and draft sections of the project brief ready for the internal IT department thereby minimising any delay. Effective communication between the two teams will only make a project proposal stronger.
The role of university estates professionals is evolving in response to the growing complexity of managing modern campuses. By embracing AI and digital technology, estates professionals can transform into augmented professionals, equipped with the tools and insights needed to optimise operations, enhance sustainability, and improve the overall campus experience. As universities continue to navigate the challenges of the 21st century, the integration of AI into estates management will be crucial in shaping the future of higher education.
