Addressing a significant growth opportunity…

Profitable manufacturing relies on not only maximising equipment utilisation but also process efficiency. Research by Nowlan and Heap suggests that c 89% of manufacturing equipment failure is not time-predictable, and therefore it requires more than simple maintenance strategies. A 2022 Siemens paper estimates that losses from downtime based on simple predictive maintenance cost the Fortune Global 500 companies c $1.5tn pa, or 11% of annual sales.

…with a next-generation solution

IntelliAM AI combines deep domain expertise with machine-learning analytics models to boost customer productivity, identifying causalities not available to those using traditional analysis. The group is also poised to disrupt current predictive maintenance practices with real-time, context-based analysis, thus creating the industry’s first intelligent asset management platform.

An industry pioneer

IntelliAM AI solutions are built on its deep domain knowledge with over 10 years of manufacturing asset care consulting giving the group considerable customer depth. The ability to cleanse, tag and prioritise large volumes of data allows customers to generate meaningful insights from the data collected. This places the group in an extremely strong position, with an offering that adds significant additional value to manufacturing customers. This is being leveraged through a new ‘land & expand’ growth strategy.

Valuation: Undemanding

IntelliAM AI is set to transform from a consultancy to a SaaS business model, something that we do not believe is reflected in the current valuation. Given our forecast growth trajectory for the group, we believe that a discounted cash flow analysis is the most appropriate valuation approach. This provides a fair value of 150p per share.

IntelliAM AI was founded in 2023 by the management of 53 North, a provider of a broad range of asset care consulting and management strategies for manufacturers, particularly in the fast-moving consumer goods (FMCG) sector. IntelliAM AI acquired 53 North (founded in 2013) at the time of its IPO (July 2024) for £5.187m, adding this business to its recently developed machine-learning-based AI platform. By using 53 North’s deep domain expertise to ensure that the large amount of data collected remotely is safely ingested, cleaned, labelled and selected, machine learning models can be trained and run on the IntelliAM AI platform. This generates unique and meaningful insights that have the potential to unleash huge value to organisations, through improved reliability, productivity, sustainability and supply chain efficiency.

Unlike many technology start-ups, IntelliAM AI has a ready-made pipeline of opportunities with some of the largest FMCG (and related) manufacturers in the UK. Combined with its deep domain expertise, this pipeline offers the potential to develop an attractive recurring revenue machine learning platform, giving it an all-important head start in this emerging technology.

Our model expects the group to have 137 sites on the AI platform by March 2029, compared to the current consulting customer base of c 150 sites. We expect recurring revenues to be driven by growing adoption and increased platform usage, generating annualised recurring revenues of £0.8m in FY25 and growing to c £8m by FY27. We expect the group to be PBT and EPS positive from FY26, but expect a policy of paying no dividends for the foreseeable future.

With the majority of current year renewals for condition-based consulting contracts only occurring in H2, the recently announced H125 results give only a modest indication of the likely year-end installed base for the group’s AI platform (we forecast 18 sites at year end, up 16 y-o-y). That said, a significant consulting contract extension (in beverages) and a contract win (Hovis) contributed to the board being ‘comfortable with current market forecasts’, underpinned by first half pro-forma consulting revenues of £1.5m.

IntelliAM AI’s sensitivities include:

• Trading history: while the recently acquired 53 North consulting business is well established, the IntelliAM AI platform currently has only a small number of users.
• Execution risk: as a small company, IntelliAM AI relies heavily on senior management to oversee the growth strategy and relies on IT skills that are in high demand.
• Competition: given the power of incumbency, it is possible that customers might lean towards AI solutions from existing equipment suppliers rather than a third-party platform.
• Free float of c 23.5%. With a small free float, periods of liquidity constraint could lead to periods of significant mismatch between market capitalisation and fundamental value.

Given our expectations of extremely rapid short-term growth for the group and a lack of directly comparable listed peers, we view the analysis of peer group trading multiples as being of limited use when valuing IntelliAM AI. Instead, we have taken a stand-alone (discounted cash flow (DCF)) valuation approach. This captures the fundamental long-term growth prospects for the group while also adjusting for the uncertainties associated with early-stage ventures. Using a weighted average cost of capital of 12.5% (based on a beta of 1.5) and a long-term growth rate of 2.5%, we arrive at a valuation for IntelliAM AI of 150p per share.

Company description: Next-generation asset optimiser

The group has its origins in the founding in 2013 of 53 North, a provider of a broad range of asset care consulting and management strategies for manufacturers, particularly in the FMCG sector. IntelliAM AI acquired 53 North at the time of its IPO (July 2024) for £5.2m, adding this business to its machine-learning-based AI platform, which allows customers to increase the operating efficiency of complex manufacturing processes.

The group generates three principal revenue streams:

• Consultancy services comprising both asset management reliability-based contracts and project-based consultancy. This includes elements of training and hardware sales.
• Platform services covering the process of implementing and accessing the IntelliAM AI platform. Implementation includes a site assessment, dashboard customisation and data pre-processing/cleansing, while access is via a monthly charge.
• Usage revenues comprising a monthly charge based on the number and frequency of parameters being monitored in order to run the AI-based models.

Exhibit 1: IntelliAM revenue profile FY22–29e (£000s)

Source: IntelliAM AI, Edison Investment Research

The group’s consulting operations cover a range of asset care disciplines, including optimisation of maintenance and operation instructions, lab testing, root cause analysis, training and compliance support. The company also provides advice on blueprint design on new installations. As well as the FMCG industry, it also has clients in the pharmaceuticals, chemical, refining, building materials and printing sectors. As at March 2024 (the last year end), the consulting order pipeline stood at £1.57m, an increase of 37% from March 2023.

Exhibit 2: IntelliAM AI consulting customers

Source: IntelliAM AI

Consultancy services take several forms:

• Asset management reliability contracts (c 46% revenues). These are fixed term (typically 12 months) asset care service contracts, which include elements of remote monitoring and monthly site visits to optimise operational performance and/or support compliance.
• Projects (c 30% revenues), which generally focus on how to optimise and enhance machine performance and can often arise as a result of an existing contract. Projects can also provide support to customers during change management programmes.

• MMP (‘My Maintenance Planner’) (c 4% revenues) subscription revenues associated with the 53 North software-based maintenance scheduler. Periodic data collection is used to automate elements of a customer’s machine maintenance scheduling and other work orders.
• Hardware (c 20% revenues), associated with condition-based or project-based work.

53 North originally developed the cloud-based computerised maintenance management system (My Maintenance Planner, MMP), which is available to customers. MMP helps customer sites manage work orders and schedule maintenance to best optimise asset management.

The IntelliAM AI platform generates insights that go well beyond the MMP module. It uses richer data sets from sensors measuring a range of variables, including vibration, turbidity, speed, temperature, torque and lubrication, as well as information regarding energy usage and maintenance plans. By collecting, cleaning and storing this data, it can then apply machine learning algorithms to the information, producing insights that conventional asset care contracts are not able to generate. It achieves this by (i) collecting data in real time, (ii) collecting data and other predictive maintenance inputs in much greater volume and (iii) cleaning and tagging data extract the best possible insights. The outcomes produced from the machine learning models are thus much richer than traditional analysis, in terms of analytical granularity and by being specific to each customer’s operating infrastructure and working practices. Furthermore, IntelliAM AI can not only identify alternative (more productive) line configurations but also, via its consultancy operation, help customers with change management.

The group’s future platform services revenues will be increasingly based around this IntelliAM AI platform and take several forms:

• Setup: to use the platform, the customer’s plant must be digitally mapped to assess which sensors are available (often sensors are added as needed) and data collected from the specific equipment configuration that the customer is using. Any historical data also has to be collected, cleansed and analysed.
• Access: customers will pay a monthly fee to access the models. This access charge is currently calculated on a per-site, per-month basis, but many AI-based SaaS solutions are likely to focus on more usage-based charges in the future.
• Usage subscription revenues are currently associated with the number of variables on which data is being collected and modelled. We anticipate that only a small proportion of the c 5,000 potential customer variables that can be monitored will be analysed on adoption, but that the depth of analysis will increase over time.

The customer requirement

Given (a) the significant investment and (b) the significant cost of down-time, it is vital that manufacturers ensure that plant and equipment operate at the most efficient levels possible and maintain quality output. Poor overall equipment effectiveness (OEE) manifests itself in many ways including equipment failure, slow cycles, process rejects, idling, planned stops (setup and adjustment) and reduced yield. Equipment failure is the most challenging to anticipate. In a 1978 report entitled ‘Reliability-centred Maintenance’, Nowlan and Heap concluded that only c 11% of equipment malfunctions were age-related (ie somewhat predictable based on usage) whereas c 89% were not. If such a high proportion of equipment malfunctions are not predictable using duration of operation, then other factors must be responsible (and therefore monitored) in order to predict machine failure.

This challenge is applicable across the entire manufacturing economy and represents a significant addressable market for those offering related services in what is known as condition-based monitoring. This monitors a range of variables in addition to operating time/age and will include factors such as vibration, temperature and lubricant analysis.

This asset care philosophy begins as early as system design and continues throughout the lifecycle of the equipment. It manifests itself in the monitoring of production lines in a number of different ways:

• Programmable logic controllers (PLCs), which can monitor and record live operational data such as machine productivity, temperature and start/stop processes, and generate alarms if a machine malfunctions.
• Human-machine-interfaces (HMI), which act as a communication link between individuals operating machines and the system to oversee and control particular equipment.
• Supervisory control and data acquisition (SCADA) systems, which give operators a holistic view of the state of equipment and infrastructure running in a plant.

At first sight it might seem that manufacturers’ approach to production line optimisation is already highly scientific, however the reality is that a significant amount of operator experience is required to interpret and react to the various inputs (ie to add context to the data). There are a number of inherent inefficiencies in this approach:

• Data sets are heterogeneous. Production lines are built from equipment from different manufacturers with varying data architectures. It is challenging to build ‘clean’ data sets that are applicable across multiple sites.
• Available data sets are small. Sensors that collect data are often limited in number and type. Furthermore, the data that PLCs generate is often constrained by the manufacturer or customer’s ability to process and store large data sets. This issue also limits the amount of contextual data (eg tank levels, waste, line speed) that can be added to the machine-level data to build up a more comprehensive picture.
• Production lines are bespoke. Two identical pumps could operate at different speeds and flow rates, through different pipe lengths and handle liquids of different viscosity. This makes standardised approaches to line optimisation almost impossible.
• Production lines are multi-product. Even within the same customer, a single production line might be used to produce a number of products with different behavioural characteristics. For example, viscosity will play an important role in determining bottle filling times for skimmed and full-fat milk.
• Product lines are complex. In any multi-step manufacturing process, the tendency is to divide the process into clearly defined sections. This tends to focus on efficiency levels for each individual section rather than taking a more holistic view. In this way many causalities could be overlooked.
• Manufacturing conditions vary. Seasonal factors such as ambient temperature could affect both plant performance and product characteristics requiring more contextual analysis.
• High levels of operator reliability. While predictive maintenance contributes to reliability, it does not affect run-time productivity (e.g. throughput efficiency, reduced waste streams, improved cleaning cycles, reduced change over time, supply chain improvements etc). For this, manufacturers rely on experienced operators to operate machines as efficiently as possible. What emerges therefore is a generally accepted set of operator guidelines that (experience has shown) results in above-average levels of productivity by historical standards. Such insights are only available from experienced operators (who are falling in number as automation rises). Customers are therefore looking to turn asset optimisation from an art into a science.

IntelliAM AI’s solution

IntelliAM AI provides a multi-step pathway to AI-enabled manufacturing asset care. It aims to:

1. Form the most comprehensive picture of asset performance in real time, allowing operators to predict, prevent and react to performance issues efficiently,
2. Contextualise this performance data based on the specific operating conditions in order to optimise (1), and
3. Identify contextualised performance patterns across complex systems to create new, meaningful performance insights.

One analogy to the IntelliAM AI platform might be the role of computers in the game of chess. At a simple level they can act as guiderails, ensuring that players adhere to the rules of the game. At a higher level they can point out threats from opponents and advise players on the strength of one possible move against another. At an advanced level however, they can suggest moves that might not seem intuitive to a human player, based as they are on the analysis of the widest possible range of future moves that go well beyond human analysis. In this way, experience of playing the game is augmented by the mathematics of the game. In the same way, IntelliAM AI’s solutions have the potential to convert the ‘art’ of optimising asset productivity (which has historically relied heavily on the experience of operators) into a science. The science will always provide far more accurate results when the self-learning algorithms are fed with millions of relevant data points each week, which is now possible through technology convergence. Therefore, line performance is improved with the application of this technology.

The starting point for IntelliAM is to create a digital map specific to the process being analysed. This includes assessing the volume and nature of the data being collected, as well as the infrastructure required to upload data to the cloud (in this case Azure). An added challenge is that older equipment might generate data in older formats. This mapping exercise may lead to additional sensors and/or contextual data being suggested in order to build the most comprehensive picture. We would expect a site on average to be capable of monitoring up to 5,000 different parameters, although in the early stages of adoption customers might focus on only a small subset of these for analysis. Furthermore, IntelliAM AI is capable of collecting c 500 million cleaned and tagged data points per line per month, using deep domain expertise to identify relevant data from the billions of other data points that are generated.

The next phase of implementation is to clean and tag the data before IntelliAM builds a specific profile for each operating process. The large amount of data available for processing is both an asset and a liability. It is therefore key that the solution provider identifies, captures and catalogues the precise data required to generate meaningful insights. Once built, the models can run in real time, providing a wide range of insights on the performance of individual manufacturing processes and, most importantly, relationships between them. Site operators can see and act on the output from the models via a comprehensive collection of dashboards created with Grafana, an open-source analytics and visualisation tool.

In summary, output from the IntelliAM AI platform can be used at various levels of sophistication:

• At a basic level the platform can measure condition, or OEE (expressed as a percentage of maximum productive potential) at levels of granularity hitherto unachievable. This can be used to facilitate better maintenance by establishing baseline performance levels, setting alarms and streamlining maintenance workflow with work order creation.
• At an intermediate level the platform can be used to provide contextualised OEE by analysing machine performance under different operating scenarios, ensuring optimum productivity across the widest possible range of conditions.
• At an advanced level the platform can identify relationships between only indirectly related processes, providing insights that can extend throughout the supply chain. The platform could, for example, provide recommendations on the speed of container filling and container sealing processes are related based on content viscosity. There is also the potential to provide analysis with which management can measure the impact of different input materials on manufacturing efficiency, as well as developing action plans for emissions and waste.

It is important to note that much of the data used by the platform is already being generated by the manufacturing assets, but is often unused as it is difficult to prioritise, clean and tag. IntelliAM AI brings not only the ability to ingest this data efficiently but, most importantly, provides the ability to extract value from the data that would otherwise remain unavailable. This is achieved through powerful multi-factor machine learning models. Running current data through a model allows the algorithm to identify and ‘remember’ relationships between data sets that would otherwise not be highlighted by more superficial analysis. This analysis is then converted into a series of operational recommendations (for example – in simple terms – a recommendation to slow the speed of machine A, which will lead to an increase in output of machine B and therefore lead to an overall increase in production line efficiency).

Competition

Competition comes principally from several sources. Equipment manufacturers offer the ability to schedule maintenance tasks and generate work orders, and ERP solution providers are looking to add better maintenance workflow to their existing offerings. Equipment manufacturers are limited in their ability to take a process-wide view or to harness the powers of machine learning analysis, while ERP solution providers are looking to add value, mainly via workflow efficiencies rather than via data analysis. Furthermore, both solution providers do not generally have the expertise to ensure the data collected produces meaningful insights..

Another source of competition is from machine learning platforms such as C3 AI. The challenge for such vendors is again gaining access to manufacturing expertise, which IntelliAM AI has thanks to established customer relationships. Once again, without this expertise, analysis of the significant volumes of data created is unlikely produce meaningful results.

Other competitors include Siemens, which acquired UK-based Senseye in June 2022 and introduced new generative AI functionality into the latest release of its predictive maintenance offering in February 2024. Its solution integrates with any asset, system or data source, using existing data or with newly installed sensors. It focuses on the metals and mining, pulp and paper, automotive and food and beverage industries, claiming to reduce unplanned downtime by up to 50%. This focus on improving reliability overlaps somewhat with IntelliAM AI, but does not address the highest value-added productivity solutions.

Israel-based Augury is another offering, focusing on ‘machine health’, ‘process health’ and ‘production health’. This business model uses subscription-based predictive maintenance solutions, based on the supply of sensors and analytical tools to minimise downtime. The platform focuses on sensor solutions to gather data around specific parameters into the cloud, which then requires subsequent contextualisation in order to optimise predictive maintenance output. As such it represents just the first (albeit important) step in leveraging the power of AI solutions. Disclosed customers include Hills Pet Nutrition, DuPont, Osem-Nestle, Colgate-Palmolive and Heineken.

IntelliAM AI’s strategy

Educate

As is often the case with disruptive technologies, education is playing a key role in the early stages of the market’s development. Often a key starting point for IntelliAM AI is differentiating its productivity offering from simple reliability solutions. Another challenge is setting customer expectations at the correct level. An overly pessimistic customer mindset will fail to appreciate the significant upside that a machine learning-based optimisation strategy can ultimately deliver. Conversely, an overly optimistic mindset could set customer expectations unrealistically high. Early discussions with customers at a plant level also need to emphasise that AI is not synonymous with automation. The solution enhances performance primarily via operator interface recommendations, and not necessarily through automation.

Sell

The 53 North consultancy business forms the core of the group’s customer-facing employees and brings with it a well-established manufacturing customer base. The ability to harness the power of AI is a high priority for all businesses, and manufacturing is no different. The strategic value of AI investment means that the group’s sales strategy is to develop its top-down, investment-led strategic messaging for C-level management so that it will resonate alongside its already well-established reputation for providing bottom-up, problem-led solutions. Alongside this will be the development of the group’s account management and customer success capabilities as it develops a SaaS revenue model.

Convert

The group’s consultancy business currently has asset care service contracts covering over 150 customer sites, including some of the largest global FMCG manufacturers (see Exhibit 2). Adoption of the IntelliAM AI platform by these customers creates new, real-time remote data links between IntelliAM AI and the customer, and gives the customer access to the group’s machine learning modelling. We estimate that the majority of the group’s condition-based contracts are due for renewal over the next 12 months (typically December to March), and this represents a significant opportunity for IntelliAM AI to start transitioning customers onto entry-level AI-based solutions.

Land & expand

Given the heterogeneous nature of customers’ asset configurations, IntelliAM AI continues to develop models for new applications (eg supply chain), and in the process continues to expand its reach beyond the FMCG sector. In addition, further development is required to offer complete end-to-end solutions for customers. Given the strategic nature of the IntelliAM solution, the group is embarking on a more aggressive ‘land & expand’ strategy over the next few years with the emphasis on establishing a presence across the broadest possible range of customers before then focusing on building wallet share. Approximately £3.3m of the £5.1m (gross) raised at the IPO on AQSE will be invested in hiring additional key personnel in software engineering, data science and automation engineering. Expansion opportunities also present themselves geographically, given that the group already counts half of the world’s largest food and beverage manufacturers among its customers.

Partner

The group has already established strong working relationships with a number of original equipment manufacturers (OEMs), not least the leading manufacturer of bearings, SKF. SKF is investigating the opportunities of using IntelliAM AI models to further refine lubrication regimes as part of an enhanced asset care strategy. IntelliAM AI recently announced that SKF has signed a letter of intent, which includes the future incorporation of the IntelliAM AI platform into SKF’s own AI solutions. In July the group announced a Digital Innovation Fund (DIF) Lighthouse Funding Award of £263,000 for research into the application of AI in lubrication analysis. This will introduce machine learning solutions to a wider group of small and medium-sized enterprises (SMEs) as well as underpin the group’s new product development.

Differentiate

The ability to differentiate the offering in a nascent market is key. Neither asset care strategies nor many of the necessary sensors are new, and as such many third parties are quick to label any form of data-based predictive analysis as AI-based. IntelliAM AI continues to focus on its machine-learning models as well as (most importantly) the data lake that such models rely on. It is important for customers to understand the incremental added value that comes from identifying, cleaning and tagging the relevant information, so that meaningful insights can be gleaned from otherwise meaningless streams of data – something that is only possible with deep domain expertise. Furthermore, once line optimisation strategies have been developed, the same expertise is important to guide any change management strategies.

Underpin

The group’s technology strategy is to build upon well-established third-party infrastructure. A key component of this is the data intelligence platform Databricks. By combining the structure of data warehouses with the flexibility of data lakes, Databricks offers IntelliAM the ability to generate insights by encompassing the widest possible range of data types. Furthermore, IntelliAM AI can create secure integration between the Databricks platform and its cloud storage solution of choice, Azure.

The IntelliAM investment case

We believe that IntelliAM AI’s investment proposition is well-balanced, offering investors elements of both the growth and the quality of cash flow that underpin any going concern valuation. Although a number of these points have been covered, it is worth reiterating them within the framework of an investment case.

Growth

• Customer requirement. We believe that IntelliAM AI addresses a very real customer need given the role that asset care strategies must play in addressing the current stagnation of UK productivity. The IntelliAM AI platform has already shown sustainable improvements of around five percentage points in OEE in one food and beverage project (average industry OEE being c 55%). Management estimates that such an improvement translated across the entire UK industry could be worth £4bn in additional revenue capacity. Finally, there is a growing need for manufacturers to communicate along the supply chain in order to manage issues such as emissions and waste. This can only be done through the collection, processing and exchange of data.
• Addressable market. While not quantifying the size of the UK opportunity, it is clear that the group’s current 150 customer sites represent a very small fraction of food and beverage sites in the UK. The group’s end user markets are also strategically significant. There are an estimated 12,500 FMCG businesses in the UK alone, representing around £24bn of exports to more than 200 countries and representing 17% of UK manufacturing gross value added (sources: FDF, The Food Foundation, gov.uk). Furthermore, the problems addressed by IntelliAM AI apply equally across the entire manufacturing sector.
• Market share. While the market for AI-enabled asset care solutions remains nascent, IntelliAM AI already has UK consultancy relationships with five of the top 10 global food and beverage groups. This is key to ensure that the group is well represented among the companies expected to have the resources to be early adopters.
• Scalability. Like many SaaS models, the IntelliAM platform is highly scalable. Furthermore, the platform allows high-volume remote data capture. This greatly expands the group’s monitoring capability and, as set-up processes become more standardised, the ability to scale quickly should also expand significantly.
• Current trading. With the majority of current year renewals for condition-based consulting contracts only occurring in H2, the recently announced H125 results give only a modest indication of the likely year-end installed base for the group’s AI platform (we forecast 18 sites at year-end, up 16 y-o-y). That said, a significant consulting contract extension (in beverages) and a contract win (Hovis) contributed to the board being ‘comfortable with current market forecasts’, underpinned by first half pro-forma consulting revenues of £1.5m.

Exhibit 3: Interim results summary

Source: IntelliAM. Note: Following the recent results, on 12 December, David Richards purchased 7,142 shares at 70p, adding to his indirect 18% shareholding in the group held via Yorkshire AI Labs.

Quality

• Recurring revenues. The SaaS model has changed investors’ perception of earnings quality, as software vendors have substituted annual support and maintenance fees with higher-quality access and usage revenues. The former were expensive to manage and vulnerable to higher rates of churn, while cloud-based usage is easier to monitor by the software vendor, and easier to transfer between software releases and thus lends itself more easily to upselling. As such, investors will focus at period end on IntelliAM AI’s annualised recurring revenues (ARR), namely the annualised run rate for recurring platform revenues (access and usage). Growth in the customer base should ensure that at the end of any given financial period, ARR will be running ahead of reported revenues. These recurring revenues will serve to underpin the quality of the group’s future cash generation.

Exhibit 4: IntelliAM AI annualised recurring revenues, FY25–29e (£000s)

Source: Edison Investment Research

Note that our calculation of ARR takes the period-end number of sites for each annual customer cohort and takes into account for each cohort forecast average increases in expected parameter usage and any subsequent volume discounts.

• Domain expertise. The acquisition of 53 North brings significant domain expertise to the group. This is important because it provides the knowledge required to understand a wide range of customer engineering challenges. Equally importantly, this expertise has given the IntelliAM AI platform the ability to identify, clean and tag the most relevant operational information from the large volume of raw data being generated.
• Innovation. The group is located close to the University of Sheffield Advanced Manufacturing & Research Centre, which is renowned for work in this area. It provides access to leading edge insights and acts as a potential talent pool for hiring in what is a highly competitive market.
• Asset intensity. As the group continues to evolve its original consulting offering into a richer cloud-based intelligent asset management platform, the asset intensity of the business model reduces further. Furthermore, scaling SaaS revenues is less asset intensive than consultancy, which bodes well for strong future returns. Even after investing in future growth, we believe that the business model has the potential to generate significant levels of future free cash flow.
• Customer ‘stickiness’. Adoption of the IntelliAM AI platform requires digital mapping of each individual customer site and subsequently data contextualisation in order to minimise the occurrence of false positives. Once configured, there are significant incentives for customers to remain on the platform, and ultimately to adopt the same approach across multiple sites, which bodes well for future customer wallet share.
• Pricing power. Early adoption of the IntelliAM AI platform has led to significant tangible benefits in OEE. Early implementations have suggested improvements in OEE of 10 percentage points, with an average OEE across current sites of c 55%. Competitors claim such solutions reduce unplanned downtime by up to 50% and generate returns on investment of 300–1,000%. As such, IntelliAM AI is well-placed to demonstrate real and rapid returns on any investment. This provides a highly supportive pricing environment for the group.

Senior management

Tom Clayton (CEO) Tom Clayton is the co-founder and CEO of IntelliAM. He has over 29 years of engineering industry experience. Tom began his career at college as an apprentice in electro-mechanical engineering. He then went on to work as a machine analyst for a technology-focused firm, specialising in predictive maintenance and becoming technical and operations director. During this time, Tom also completed his master’s degree in maintenance engineering and asset management from the University of Manchester. Tom founded asset care firm, 53North Group, in 2013.

Daud Khan (CFO) qualified at PwC in 1998 before working as a technology research analyst at a number of US and European City institutions including Bank of America, JP Morgan and Berenberg. Between 2018 and 2022 Daud was vice president of corporate development for WANdisco (now Cirata) before becoming a managing director in the technology and media investment banking team at Peel Hunt. Daud has a degree in computer science and management from Cambridge University. He joined IntelliAM AI in 2023.

Keith Smith (COO) joined 53 North in 2016 and has spent over 16 years in the FMCG sector, developing and promoting asset care standards. Keith has a master’s degree in maintenance engineering and asset management from The University of Manchester.

Non-executive directors

David Richards MBE is a technology entrepreneur with a 25-year career in the software industry, having founded and led multiple software businesses. IntelliAM AI recently announced that, as part of the board’s post-IPO succession plan, David Richards will be standing down as chairman on 3 July 2025. A formal process to appoint a successor has been started.

Professor Keith Ridgway CBE is chair of Industry Wales and was the founder and executive chair of the National Manufacturing Institute Scotland and Advanced Forming Research Centre at the University of Strathclyde. He is currently the senior executive manufacturing at the University of Strathclyde. Keith is a member of the Prime Minister’s Council for Science and Technology.

Dame Julie Kenny DBE DL is a successful South Yorkshire-based entrepreneur, leading Pyronix from its founding in 1986, to becoming a leading global producer and distributor of high-quality security systems. Julie’s DBE in 2002 and honorary doctorate from Sheffield Hallam University in 2005 were conferred in recognition of her contribution to business in the region.

53 North acquisition, AQSE IPO

IntelliAM AI listed on the London AQSE market on 3 July 2024. The listing included the acquisition by IntelliAM of 53 Degrees North Engineering (53 North) for £5.187m (50% cash, 50% shares). The cash consideration of c £2.6m is payable over up to three years. We have modelled acquisition-related cash payments of c £0.8m in July 2025 and c £0.6m in July 2026.

A total of 2.8m shares were issued in connection with the acquisition, which, together with the 5.4m shares issued in connection with the fund-raise and the 10.9m shares already in issue, resulted in 19.1m shares being listed at 94p per share. In addition, following admission there were 559,600 share options, 399,600 of which had been issued as part of the acquisition. The gross proceeds raised were £5.08m.

We expect the income statement for the financial year ended March 2025 to thus contain the bulk of the listing expenses (we estimate £1.1m) as an exceptional charge. Furthermore, assuming c £0.5m of 53 North net assets were acquired, we have assumed that the resulting acquired intangibles (c £4.7m) will be written off over 15 years.

Financials

Prior to its acquisition of 53 North in July 2024, IntelliAM AI had modest revenues. As such, the financial data shown for FY25e and earlier is a pro-forma combination of the two entities. The data for FY26 and beyond reflect the resulting IntelliAM AI and 53 North combination.

The key short-term sensitivity for the group is the rate at which existing consultancy customers adopt its SaaS platform solution. The two key assumptions underpinning our financial forecasts focus on (1) the rate at which customers adopt (which will drive setup and access revenues) and (2) the depth of data analysis (which will drive usage revenues):

• Adoption: we assume two sites are added in FY25 and a further 16 in FY26 (primarily in H2). Note that the group currently has over 150 consultancy sites in the UK.
• Usage: we assume that only 10% of the possible 5,000 monitored parameters are analysed in the first year of adoption, rising to 40% in the second year and 80% by the end of year five. This assumption is based less on prior experience (significant platform adoption only began this year) but rather on the expected demand for additional analysis once the initial results of the model are available. We also assume modest price deflation for the cost per parameter per month over the forecast period.

Revenues

The group’s various revenue streams (see Exhibit 1) were discussed earlier. The assumptions built into our forecast are as follows:

• The group currently employs c 30 full-time, customer-facing staff, and this is expected to expand to c 38 by the end of FY27. However, we have forecast no growth in consulting revenues in FY26 or FY27 as any growth is expected to come in the form of additional IntelliAM AI platform SaaS revenues from the established customer base.
• The number of sites using the IntelliAM AI platform is expected to increase from two to 50 between FY24 and FY27. Following the recent H125 results, we estimate that this figure remained in single digits although the majority of contract renewals (which is when we expect platform adoption to occur) will take place in the second half of the financial year. This compares to the c 150 sites currently using the group’s condition-based consultancy services. Set-up cost per site (which is expected to fall slowly over the next few years) is modelled at £30,000–35,000 and sites are expected to be added equally throughout the year, with the exception of FY25, when we expect to see an H2 weighting for new site additions.
• We expect annual access revenues to be slightly greater than set-up revenues and grow at a CAGR of c 16% to the end of FY27.
• Usage revenue forecasts are based on the number of parameters (out of a possible 5,000 per site) that customers could analyse. Our assumption is that initial usage is modest for each annual customer cohort (10% of the maximum parameters available) but that this grows to over 80% of parameters over the first five years of use. As mentioned earlier, it is possible that this revenue stream changes to a usage-based model over the forecast period, which could have a positive impact on forecasts.

A key feature of SaaS models is the generation of ARR. In the case of IntelliAM AI this takes the form of platform access and usage revenues, and given the expected growth profile of the business it is likely that the ARR at the end of any given financial year will be higher than the reported revenues for that period (see Exhibit 3). Note that over time, factors such as customer churn rate and customer net revenue retention (capturing factors such as upselling/downgrades) will also drive ARR.

Exhibit 5: Evolution of IntelliAM AI platform revenues, FY22-29e

Source: IntelliAM AI, Edison Investment Research

Profitability

We expect gross margins in the consulting business from FY25 to be in line with the average reported for the FY22–24 period. By way of contrast, gross margins in the platform business are expected to rise significantly over time as lower-margin implementation revenue becomes a smaller proportion of overall platform revenue, thanks to growing usage revenues. In line with other listed software stocks, we also expect an element of annual R&D spending to be capitalised, which will increase reported profitability levels.

Exhibit 6: Margin progression assumptions by division

Source: IntelliAM AI, Edison Investment Research

At a net profitability level, we expect rises in sales and marketing and general and administrative costs as a proportion of sales through the forecast period, as management continues to invest in the business. For the platform business we expect R&D costs to grow at a 39% CAGR between FY26 and FY29, although we assume that a proportion of this is capitalised and written off over five years. Both sales and marketing and G&A spend are expected to be particularly significant from FY26 onwards (33% and 31% CAGR FY26–29 respectively), following the greater focus in FY25 on migrating existing consulting clients onto the platform.

Exhibit 7: IntelliAM AI margin profile, FY22-29e

Source: IntelliAM AI, Edison Investment Research

We expect a number of factors to be included in the reporting of an additional (normalised) statement of profitability. In addition to the write-back of any charge for the amortisation of acquired intangibles, share-based compensation charges and the costs of IPO are likely to be stripped out of a normalised profitability figure. We estimate these factors will account for P&L charges of c £1.3m in the current financial year. Some R&D investment will also be capitalised but this will not be written back into adjusted figures.

Exhibit 8: Reconciliation of reported and normalised operating profits, FY22–27e

Source: IntelliAM AI, Edison Investment Research

Balance sheet

The balance sheet remains strong following the capital raising in July 2024, with forecast net cash as at March 2025 standing at £0.43m, or 2.1p per share (see Exhibit 15). We model deferred consideration cash payments of £1.05m, £0.8m and £0.6m in FY25, FY26 and FY27, respectively. The principal group assets are intangible, comprising goodwill from the 53 North acquisition and (increasingly) capitalised R&D, which we forecast will be an initial £0.5m in FY25, rising to £1.4m in FY26 and increasing in line with future investment thereafter. The liability side of the balance sheet includes a mortgage on the principal property (c £250k) and car leases.

Cash flow

The 53 North acquisition and associated share issuance remain the major features of the group’s recent cash flow. In FY25 and FY26, acquisition and capital raising costs added to investment in the AI platform, leaving free cash flow negative (see Exhibit 11). In FY27, free cash flow is subdued by expected growth-related working capital requirements, but by FY28 we expect free cash flow to be meaningful, representing 21% of reported EBITDA and continuing to rise as a proportion thereafter.

We expect the group’s future capex to be focused principally on the development of software-related intellectual property with a significant proportion of R&D spend being capitalised (c 20% of revenues from FY26 onwards) and amortised over five years. Tangible asset capex is expected to be modest (c £50k pa).

Valuation

While a number of listed AI companies exist, many give investors exposure to the technology behind the infrastructure necessary for AI solutions rather than specific end-user solutions. For this reason the growth rates and profit margins of such businesses make them poor comparatives to IntelliAM AI. For those investors that still prefer a multiples-based approach to valuation, the following UK-listed AI-related technology names could be of interest:

• ActiveOps: AI-based Service delivery optimisation.
• Dotdigital: AI-enabled marketing optimisation.
• Ebiquity: data-driven media investment optimisation.
• Eagle Eye Solutions: AI-enabled real-time marketing optimisation.
• RELX: information-based analytics and decision tools.

Exhibit 9: IntelliAM AI potential peer group

Source: Edison Investment Research, LSEG Data & Analytics. Note: Prices as at 9 January 2025.

Exhibit 10: IntelliAM AI valuations implied by average peer multiples

Source: Edison Investment Research

An analysis of current trading multiples based on consensus forecasts suggests a wide range of implied valuations for IntelliAM AI (see Exhibit 10). This is unsurprising given the highly diverse group of potential UK peers. As discussed earlier, any proposed peer group will have a wide range of market capitalisations, business models, growth profiles and profitability, making a multiple valuation of limited use. Most importantly, the above group of companies, while providing AI-enabled solutions, are also at different stages of their development to IntelliAM AI, address different end-user markets and have shares listed on different UK exchanges, the liquidity of which can have an impact on valuation. As such it is important to value IntelliAM AI on its own merits and this implies a standalone valuation approach.

Methodology

Our valuation approach uses the short- and medium-term forecasts laid out in the forecasts section to arrive at a fair value of 150p per share. There are a number of key additional assumptions supporting this analysis:

• As a relatively young company with significant execution risk still ahead of it, we believe that a weighted average cost of capital (WACC) of 12% is appropriate. Using an equity risk premium of 5.5% and a UK 10-year gilt yield of 4.4%, our chosen WACC uses a beta of 1.5x.
• The perpetuity growth rate used to arrive at our terminal value is 2.5%, in line with long-term GDP growth.

Exhibit 11: IntelliAM discounted cash flow summary FY25e–29e

Source: Edison Investment Research

Exhibit 12: IntelliAM AI DCF valuation

Source: Edison Investment Research

The key sensitivities to our valuation are our underlying cash generation assumptions, based as they are on our underlying cash flow forecast and discussed in the Financials section. Working capital is certainly one element to highlight. The onboarding of a new site involves a significant resource commitment for digital mapping and data cleansing. Higher than expected onboarding rates could lead to greater working capital requirements.

In terms of macro factors that could affect the valuation, volatility in the prevailing interest rate environment (as reflected in 10-year UK government bond yields) could play an important role.

Potential volatility in these factors is captured in the sensitivity analysis shown below in terms of changes in WACC.

A final factor worth discussing is the assumed long-term growth rate. As mentioned earlier, we believe that 3.5% is at the higher end of generally accepted perpetual growth rates (particularly as a real rate) but it is not uncommon for technology stock DCF valuations. While the figure could be described as high, we would expect IntelliAM AI to grow at rates significantly higher than this for many years beyond the relatively short five-year explicit cash flow forecast that we have used.

Exhibit 13: IntelliAM DCF valuation summary (£ per share)

Source: Edison Investment Research

If we add or subtract 10% to our forecast AI platform adoption rates, the DCF valuation changes as shown in Exhibits 14 and 15.

Exhibit 14: IntelliAM DCF valuation summary, adoption +10% (£ per share)

Source: Edison Investment Research

Exhibit 15: IntelliAM DCF valuation summary, adoption -10% (£ per share)

Source: Edison Investment Research

Sensitivities

• Critical Mass. The effectiveness of machine-leaning models can be traced in part to the amount to data available to them. This suggests that the size of the ‘Data Lake’ that sits behind an AI model (or in the case of the Databricks platform both a data lake and a data warehouse) is an important consideration for customers. Many AI-related business models focus therefore on growing quickly to gain access to the largest possible data sets, resulting in loss-making short-term corporate strategies. While the IntelliAM AI business model carries some exposure to such competitive behaviour, it is important to reiterate the bespoke nature of each customer configuration and the deep domain expertise required to interpret that data makes ‘non-customer’ data sets of limited value.
• Competition ‘stickiness’. For the reasons outlined earlier (see Customer stickiness), once a competitor becomes the AI solution provider of choice it could be difficult to replace. This is a particular challenge in the early stages of technology adoption when an insufficient body of history exists against which to benchmark competing solutions.
• Go-to-market strategy. The combination of the 53 North consulting business with the IntelliAM AI platform will take the former in a new direction, and this brings challenges associated with the selling process. In essence, the sales model is set to shift from a bottom-up, problem-led approach to that of a top-down, investment-led offering. The challenge for the group is thus to build on its strong relationships at the operational level of its customer base, by adding an additional cohort of strategic-level contacts. Given the global nature of its customer base (particularly in food and beverages), the upside potential of achieving this is significant.
• Execution risk. As a relatively young IT company there are a number of execution-related risks including key person risk, cyber security risks and challenges associated with managing a fast-growing customer base.
• Low free float. A low free float can lead to fluctuations in the share price, which can run contrary to trading fundamentals as well as creating periods of low liquidity.
• Trading history. While the 53 North consulting business is well established, particularly within the UK FMCG sector, the IntelliAM AI platform still has only a small number of users. The strategy of converting users of the current MMP software onto IntelliAM AI could include additional cost (albeit for significant potential additional benefits). The next 12 months thus represents a key transition period for the group from a software perspective.

Exhibit 16: Financial summary FY22–27e

Source: IntelliAM AI accounts, Edison Investment Research. Note: 2024 figures are pro forma. 2022 and 2023 figures are for 53 North only prior to acquisition.