Cloud Technology Payday Accelerators Once you have managed to migrate a relevant share of your external customer‐facing and internal workloads, or of enabling infrastructure to support a superior performance of your front‐ends into the cloud, your new cloud solutions can ideally be integrated with your digital experience technologies (explained later) and better support end‐to‐end customer journeys. This indirectly can increase related revenue and efficiency drivers. These you can model, target, and measure in your cloud migration business case. They include improved customer satisfaction scores, increased conversion and retention rates due to increased ease of use in front‐ends, and increased automation with less manual process steps in front‐end/back‐end integration. In addition, as I will explain in the Analytics Technology section of this chapter, the ability to combine data from various sources in one place (in the cloud) allows a wide range of additional benefits and new business models.

This business case upside has the potential to be boosted even more if you simultaneously aim to have more agility and faster time to market in order to react to upcoming market needs. Or, if you can outpace competition as a first mover with new propositions by carefully analyzing your data trove and applying analytics and pattern recognition. You can plan these impacts and measure them as incremental revenues from first‐mover product‐and‐services launches, profit improvements due to faster and segmented lifetime value‐driven service portfolio and pricing adjustment, or as the indirect benefits of faster customer journey improvements and process‐driven cost savings.

At the same time, your cloud solution can provide greater scalability and close‐to‐instant availability. This generates better capacity utilization versus cost ratios, which you can model in your business case as enablers of revenue growth cases or as mitigation to demand forecasting uncertainty/fault cost in case of massive volume peaks or surges.

Furthermore, you can aim to reduce your risks of service disruption, which can usually be extrapolated and estimated as cost avoidance or opportunity cost from related historical developments. Or you can forecast in different disruption‐threat scenarios, ideally closely linked with unfortunately high‐cost cyber‐technology considerations. These will be elaborated in much more detail later. The fact that (cyber) security is now under the responsibility of the cloud providers can provide synergies. This can, for example, be a shared cybersecurity team or immediate security updates in the cloud without the need of lengthy upgrade rollouts.

Such synergies can offer you cost advantages like as the best available services and features developed centrally by the vendors, and increased overall interoperability and flexibility. Obviously, depending on the cloud provider, this also means that you will lose autonomy and become a consumer of services instead of the driver of development. At the same time, you can substantially increase process efficiency in operations via new approaches for development cost reduction, which can be modeled in your business case when planning the corresponding cost buckets.

Finally, moving to the cloud can free you up from previous capex constraints, by moving the majority of spending into the opex bucket, which, as mentioned before, can also be a two‐sided sword—on the one hand, if the margin impact affects the view of investors on your valuation negatively, on the other hand, if headcount reductions will be feasible due to the fact that experts on your legacy systems and infrastructure topics will no longer be needed in‐house.

Cloud Technology Payday Decelerators Unfortunately, every cloud transformation also faces a wide range of payback decelerators, which I often see underrepresented in business cases. These decelerators start with (multi‐) cloud strategy design cost, a crucial investment, which is often overlooked, leading to a purely technology‐driven cloud transformation program, followed by the often substantial cost (if done right, because usually external neutral expertise is required over a long selection time period) for selecting the best fitting vendor or vendor portfolio in the light of the previously developed strategy, license, and usage cost and the often difficult‐to‐plan implementation and rollout cost from vendors, staff augmentation, subject‐matter experts, and consultants. As in any program, you should plan with a substantial buffer over and beyond what you think will be required.

This might technically take you to the cloud, but unfortunately required spending does not end here to generate the business impact you should be aiming for. Your payday will be further slowed down by softer sounding but often success‐critical hiring and training cost to replace the expensive externals you had to onboard in the early parts of your journey with very scarce and highly paid full‐time cloud expert staff; retention packages for already skilled users, developers, and architects you might have on board; and change management cost for the broader organization to actually appreciate and use the new technical, process, and innovation capabilities in line with your overall strategic goals. (All these workforce‐related topics will be further elaborated later in this chapter.)

Ongoing license, operations, and maintenance costs will obviously have to play another important role in your payback considerations, especially as the previously discussed value implications of impeding margin pressure after capex to opex shift could negatively influence your market capitalization from an external perspective. You could be stuck in a vendor lock‐in, potentially with substantial minimum‐volume commitments, without a proper exit and migration strategy to divorce from the relationship at a later stage if economics require you to do so. Cloud transformation is no one‐time effort but, rather, an ongoing process with permanent adaptations (new releases, new skills, and regulatory requirements).

Last, but often substantially underestimated, are the security costs any cloud transformation will add to your baseline, whether you like it or not. Buckets to include in your business case are data security validation efforts, regulation‐driven adjustments (e.g., from GDPR), and other contractual obligations.

Table 4.2 provides a summary that clusters some key accelerators and decelerators for cloud technology depending on their typical tangibility.

Digital Experience Technology Payday Accelerators Once you have all your digital experience platforms up and running, ideally as specified in an iterative process in which business and technology teams work closely together, the benefits of digital experience technologies can be manifold—at least if you manage to achieve a relevant and never‐resting capability boost versus your legacy systems. Then you can expect incremental revenues from many sources of new customers and within your existing customer base. These could take the form of:

• Higher conversion rates from your campaigns
• Lower acquisition costs
• Incremental revenue uplifts from better matching customer needs and (next best) offers when upselling and cross‐selling
• More successful identification and retention of valuable at‐risk customers
• Phase‐out or migration of lower‐ or negative‐profit customers to other services

However, beware. These are part of the flashy hypothesis, which every 360‐degree vendor postulates. You need proof from experience or, even better, from properly executed pilots, before you can rely on any numbers for your business case.

Digital Experience Technology Payday Decelerators Digital transformation failure rates have one very important source of origin. In real life almost every digital experience implementation is fighting expected and unexpected payback decelerators, which often come as a surprise for management when they materialize, even though they should have been foreseeable. First and foremost, what you should factor in are the often‐unaccounted‐for cost for a preproject and a feasibility study to be surer of what you are doing prior to starting into the larger exercise. Iterative, lean, approaches must be constantly adjusted based on feedback and change. They require you to start early, fail, do it again, and correct your trajectory to do it right in the end. Design exercises that are too big up‐front only lead to inertia and projects not being started. These are all business design costs in the wider sense. Often overlooked are also the substantial cost (if done right, as usually external neutral expertise is required over a long selection period) for selecting the best fitting vendor or vendor portfolio in the light of the previously developed strategy. More transparent and plannable are license and usage costs, less so the often difficult‐to‐plan implementation and rollout costs from vendors, staff augmentation, subject‐matter experts, and consultants. I have seen many cases in which the initial happiness of selecting the cheapest solution turned out to be a rude awakening when change requests blew the budget to a magnitude no one expected. Very simply, if you buy cheap, you often buy twice, so a substantial risk buffer is advisable in any case. All this might make your systems go live one day, but required investments do not end here. Also to be considered are:

• Success‐critical hiring and training
• Onboarding cost to replace the inevitable externals you had to onboard in the early parts of your journey with very scarce and highly paid full‐time expert staff (either for implementation or for backfilling, as you still have a business as usual to run in parallel)
• Retention packages for skilled users, developers, and architects you might already have on board
• Change management cost for the broader organization to actually appreciate and use the new technical, process, and innovation capabilities in line with your overall strategic goals

All these workforce‐related topics will be further elaborated in separate workforce catalyst sections of this chapter.

Ongoing operations and maintenance cost will obviously have to play another important role in your payback considerations.

Last, but often substantially underestimated, are security costs. Buckets to include in your business case are data security validation efforts and regulation‐driven adjustments (e.g., from GDPR), which I will explain in more detail in a later section.

Table 4.3 clusters some key accelerators and decelerators for digital experience technology, depending on their typical tangibility.

Intelligent Automation Technology Payday Accelerators Rarely have I seen the intelligent automation solutions lead to full headcount reductions as the naivest and, from a worker's perspective, most feared benefit of adding “intelligent robots” to the workforce. Instead, even though there are exceptions where three‐digit number of headcounts have been reduced, in most cases the current state of available tools (especially RPA) rather allows part of the workload of personnel being given back to the business, not the full‐time equivalent headcount. This is achieved by offloading staff from cumbersome, inefficient, and, in most cases, boring and not very fulfilling tasks. Nevertheless, the impacts should not be underestimated: Companies with very high growth trajectories can shoulder the implied additional workload without significantly increasing their workforce. This is driven by the indirect impacts automations can have on compliance or risk pass/fail data, net promoter scores, process speed, first solution rates, and many more. Still, for real transformational personnel cost impact, processes cannot just be painted over with new intelligent automation tools but need to be completely redesigned as part of the overall digital transformation effort. Intelligent automation solutions can serve as an additional booster of automation, as a positive “modern” driver to start previously stuck process‐optimization discussions or as a bridging technology until the full‐scale process redesign becomes available.

Intelligent Automation Technology Payday Decelerators On the other hand, cognitive technology driven payback decelerators are much more concrete and immediate. You must plan cost for RPA, cognitive/AI software (licenses, support cost), and related bolt‐on solutions (e.g., OCR), the necessary hardware or cloud hosting cost, plus plan to invest substantial time and money into hiring, training, learning on the job, change management, and ramp‐up inefficiencies until the integration with other work steps is practiced well enough. Under the assumption that in the early days you do not have scalable cognitive/AI expertise in‐house, you will also face highly relevant cost for required external expertise: to raise awareness for the success‐critical process of scouting and mining (potentially by different software with additional cost), to find the right pilot processes with buy‐in and scalability potential, process detail analysis and documentation, platform implementation and configuration, go‐live/hyper care, and ongoing maintenance and support.

Table 4.4 clusters some key accelerators and decelerators for intelligent automation technology depending on their typical tangibility.

Analytics Technology Payday Accelerators Unfortunately, until recently, “most analytics efforts have struggled to deliver on the simplest version of that potential: the rearview mirror describing what has already happened—or, for the advanced few, presenting real‐time views into what is currently happening” (Briggs 2019, p. 7). Still, many analytics experts take it for granted that analytics should be a critical future cornerstone for any successful digital transformation. For them it seems obvious that a substantially growing annual analytics budget should be approved without any doubts and they are then surprised when they face resistance because the payday for analytics technology is not that easy to explain and demonstrate to their business. That does not mean that, in theory, the benefits of a successful analytics platform and team are not clear: With advanced analytics, you can better target which customers to acquire and keep for optimum value and how to best cross‐ and up‐sell to them. Furthermore, instead of using only basic measurements for decision‐making you can improve the effectiveness of your marketing attribution and media mix modeling and thus optimize your advertising spend. Analytics also allows you to get a better understanding of correlations and, in the best case, something close to causality between planned action and outcomes. On top of that, analytics also leverages data to do things faster. In combination with cognitive/AI catalysts (as explained later in this chapter), you can act much quicker without the previous manual analysis. The payday contribution of speed can be measured and demonstrated, for example, as a short‐term resource time and expense reduction for developing and deploying reports, and the longer‐term revenue gains and expense reductions from quicker performance analytics being used to streamline business process and identify opportunities for growth.

Analytics Technology Payday Decelerators On the other side of the equation, analytics payback decelerators are not to be forgotten. You must plan software cost (licenses, support cost), the necessary hardware or cloud hosting cost, and expect to invest substantial time and money into hiring, training, learning on the job, change management, and ramp‐up inefficiencies until the integration with other work steps is practiced well enough. Under the assumption that in the early days you do not have all the analytics expertise in your workforce, you will also face cost for external expertise: to raise awareness, to find the right use cases with buy‐in and scalability potential, process detail analysis and documentation, platform implementation and configuration, go‐live/hyper care, and ongoing maintenance and support.

Last, you should not forget implied security cost. This includes data security validation efforts and regulation‐driven adjustments (e.g., from GDPR).

Table 4.5 clusters some key accelerators and decelerators for analytics technology depending on their typical tangibility.

Cyber‐ (and Data) Security Technology Payday Accelerators The general problem of estimating cybersecurity and data protection technologies as a digital transformation payday accelerator is that they are abstract. They are designed to maintain customer trust and secure ownership of one of the most crucial assets of your organization—data. As such, next to enabling new business models, access to new markets, and strengthening of value propositions, they are first and foremost about loss prevention. When you invest in security, you aim to reduce risks threatening your assets. The return on security investment is calculated by estimating how much loss you avoided thanks to your investment. This requires approximating how high these damages in various forms could have been if nothing had been done to mitigate them. Unfortunately, a proper estimation requires replicable processes and correct data to be captured. At many companies, I know these do not exist at the level of quality you would expect them to be. The often‐necessary ad‐hoc gathering of information usually diminishes buy‐in of recommendations before even getting started. And sadly, even benchmarks do not help much here, because every enterprise is different.

There is certainly no lack of concepts (ENISA 2012) to still get a good estimate. They share the intuitive idea to approximate the damage a certain event would imply, the single loss expectancy (SLE) multiplied by the likelihood of its occurrence (the annualized risk of occurrence, or ARO), giving you the annual loss expectancy (ALE). Whatever your measures, subtract from this ALE the benefits you are looking for. Obviously, these benefits are very different in character compared to the digital transformation payday accelerators discussed earlier. The first key question is: What are the damages that are being avoided? They range from the implications of non‐adherence to regulatory requirements (to protect licenses, for example, in financial services), to data privacy violation linked penalties (GDPR), to related personal liabilities of board members. Also, the damages of a loss of trust must be considered, regardless of whether it comes from your customer base or from your ecosystem partners (if business‐critical information is visible to competitors) or if private data (for example, on financial status) is becoming public. Not to forget real damages (data theft and blackmailing, critical system outages, and many more). In any case, the annual rate of occurrence is hard to estimate, and the resulting numbers can vary highly from one environment to another. These approximations are often biased by our perception of the risk. The accuracy of statistical data used in the calculation of return on sustainability investments (ROSI) is therefore essential. However, actuarial data on security incidents are hard to find as companies are often reluctant to capture data on their security incidents.

Cyber‐ (and Data) Security Technology Payday Decelerators The cost of the required solutions and services is easier to predict provided all payday decelerators are considered. They can be best structured by asset classes:

1. First, one must consider the cost for dedicated hardware whose prime purpose is IT security related, including firewalls, security gateways, security appliances, security toolset platforms, and ID tokens.
2. Second, software cost like annual license and maintenance as well as costs associated with new purchases and upgrades for all software dedicated to operate or manage the security systems applications for each category of security expenditure are of relevance.
3. Third, and not to be forgotten, facility and other costs such as hosting/facilities/occupancy for space dedicated to in‐scope security functions and personnel, such as the apportioned annual costs of hosting security‐related devices, storage arrays, and appliances in the data center, including power/heat management and raised floor. It would also include the annual cost of any consumables related to the security activities.
4. Fourth, outsourcing fees for third‐party or outsourcing contracts primarily comprising services for managing or monitoring security devices, systems, or processes where the services are provided on‐site should be an integral part of the business case.
5. Fifth, the cost of managed service providers (MSPs)/cloud as remote subscription‐based monitoring and/or management of security devices such as firewalls, intrusion detection, and prevention functions via customer‐premises‐based or network‐based devices can become a key digital transformation payday decelerator. It also includes remotely delivered specialist‐managed security services (e.g., threat intelligence, security information and event management/security operations (SIEM/SOC) center, distributed denial‐of‐service attack (DDoS), etc.) and cloud‐based security services such as identity‐as‐a‐service (IDaaS).
6. Sixth, consulting services that help companies analyze and improve the efficacy of business operations and technology strategies.
7. Seventh, IT security personnel whose roles and duties are primarily focused on information security activities can become a key cost block. This includes all full‐time, part‐time, and temporary full‐time equivalent resources (FTEs). These personnel provide support across the security functions.

Table 4.6 clusters some key accelerators and decelerators for cyber‐technology depending on their typical tangibility.

Digital Reality Payday Accelerators Unfortunately, scaling up digital reality solutions in a meaningful way first requires a sufficiently large hardware user base in your organization. A few niche pilots will not move the needle in any way you can measure. Not only do you need many users to be able to one day find any meaningful benefits in your bottom line, but you also need them to use the devices for their respective use case very frequently to pay back the required hardware investments. This is certainly true for the most obvious and communicated training use cases (savings in travel and location cost, steeper learning curve, and so on) but even more for use cases in field services (reduced training cost, remote direct access to specific subject‐matter experts, and so on), not to mention the required customer take‐up rates required for any selling and marketing efforts in digital reality to make any sense from a reach‐and‐impact perspective.

Digital Reality Payday Decelerators Next to the cost of sourcing (buying or as a service) the necessary hardware and software platforms to achieve sufficient penetration in your workforce for the selected use cases, creating digital reality scenarios at scale requires a specific up‐front investment in terms of content creation. This can initially be external (agencies, consultants, subject‐matter experts), but if a certain scale must be reached, it requires also building up internal expert resources, which are forecasted to be high‐cost, even today.

Decelerators also include strategy design, followed by the substantial cost for selecting the best‐fitting vendor or vendor portfolio in the light of the previously developed strategy, license, and usage cost and the often difficult to plan implementation and rollout costs from vendors, staff augmentation, subject‐matter experts, and consultants. As in any innovative program, you should plan with a substantial buffer.

Your payday will be further slowed by softer sounding but often success‐critical hiring and training cost to replace the expensive externals with expert staff; retention packages for already skilled users, developers, and architects you might have on board; and change management cost for the broader organization to actually appreciate and use the new technical, process, and innovation capabilities in line with your overall strategic goals. Ongoing license, operations, and maintenance cost will obviously have to play another important role in your payback considerations.

Last are the implied security costs, and today we have not even begun to comprehensively assess them. Buckets to include in your business case are data security validation efforts and regulation‐driven adjustments (e.g. from GDPR).

For easier handling in your daily business, Table 4.7 clusters some key accelerators and decelerators for digital reality technology depending on their typical tangibility.

Blockchain/Distributed Ledger Technology Payday Accelerators The primary abstract benefit of blockchain applications in an ecosystem will always be trust. Once the technology has replaced the traditional human‐relationship‐based notion of trust among the partners with a technical algorithm, your business can expect revenue increases from transactions otherwise not possible. Hand in hand with trust comes the advantage that blockchain creates unalterable records with strong encryption and is stored across a network of computers, making it very challenging to attack. It goes without saying that both these increases are very difficult to translate into concrete revenue increases. Easier to estimate are the implications on the costs for organizations by creating process efficiencies and easing reporting and auditing processes. Blockchain also helps businesses cut costs by removing third‐party providers, and by automating processes in transactions, blockchain can operate transactions significantly faster in a much more traceable manner, which should also be recorded as a cost benefit on the accelerator side of blockchain/distributed ledger technology (DLT) business cases.

Blockchain/Distributed Ledger Technology Payday Decelerators Unfortunately, the described abstract benefits come at a price. Every blockchain implementation is also producing a range of much more concrete payback decelerators. These decelerators start with the blockchain use case selection and partner search and contracting cost, both crucial investments to get right for later scalability in the ecosystem (or by joining a consortium) and continue with the cost for selecting the best‐fitting vendor or vendor portfolio in the light of the previously selected applications. Most of the time, you will only have small pockets (if any) of expertise in‐house and will have to consider the expense of bringing subject‐matter experts on board, either via traditional consulting projects or by hiring (temporary) expert staff to start building up the required capabilities. As with all other technologies discussed in this chapter, this might take you technically ahead, but, unfortunately, required spending does not end here to generate the scalability required for the blockchain to make sense. Your payday will be further slowed down by often success‐critical hiring and training cost to replace the expensive externals you had to onboard in the early parts of your journey with very scarce and highly paid full‐time blockchain expert staff, retention packages for already skilled users, developers and architects you might have, and change‐management cost for the broader organization to actually appreciate and use the new technical, process, and innovation capabilities in line with your overall strategic goals.

Ongoing license (if no open‐source stack is used), hosting, operations and maintenance cost will obviously have to play another important role in your payback considerations. Blockchain is no one‐time effort but an ongoing process with potential adaptations (new releases, new skills, regulatory requirements). While the security cost benefits have already been discussed on the accelerator side, blockchain implementations at scale also might, like any other IT implementation, carry security cost implications (which have not yet been fully reflected by regulators but for sure will be, not to mention the potential dangers of new technologies like quantum computing) with them, which should not be underestimated. Buckets for your business case are data security validation efforts, and regulation‐driven adjustments (e.g., from GDPR).

Table 4.8 clusters some key accelerators and decelerators for blockchain technology depending on their typical tangibility.

Next‐Generation Infrastructure Technology You could argue that fiber rollouts, 5G/6G networks, IoT, and smart infrastructure are already here today and therefore should not be put as a future prospect here. Yes, they are. But believe me as a telecoms specialist, the promises of these technologies coming together at scale are still quite some time away from us. Nevertheless, I believe no company with serious digital transformation plan can afford to not have them on the midterm radar of digital transformation technology catalysts.

Quantum Technology Quantum is expected to transform technology in major areas like computing, sensing, and communications. Quantum computing can solve advanced problems by leveraging quantum phenomena to process information at unprecedented speeds. Quantum communication applies quantum mechanics to create ultra‐secure communication networks that should be able to detect any tampering. For sensing, quantum sensing devices are expected to be much more precise than conventional sensors with promising use cases in many sectors.

Exponential Intelligence The next generation of AI aims to generate a better understanding of human emotion and intent. This has already been proven to be possible, for example, when spotting emotions in client service calls and, in case of anger, routing the respective customer to conflict‐trained agents. On top of that, “Soon, these technologies will be able to look at a statistical correlation and, much like the human brain, determine if it makes sense or if it is just a random feature of the supporting data that has no intrinsic meaning” (Buchholz and Briggs 2022).

Ambient Computing Ambient computing will make technology ubiquitous in all our lives. High‐performing digital assistants based on an array of sensors, voice recognition, analytics, and exponential intelligence capabilities will be able to accompany you all day: “Augmenting an individual's physical experience with digital information will be another major dimension of life beyond the glass. Researchers and entrepreneurs alike are already exploring possibilities for using smart contact lenses and even implanted brain chips to augment our senses and (literally) read our minds. Think about it: Why wouldn't it be natural to look at the sun and see how many hours until sunset?” (Buchholz and Briggs 2022).

Obviously for these future speculative technologies a discussion of specific accelerators and decelerators does not (yet) make any sense. However, by now you should have enough information from the already‐developed technologies to understand what you need to watch out for when they become relevant.

Workforce Skills Payday Accelerators A sufficiently skilled workforce will bring the ability to apply new ways of working in a much more agile and therefore higher‐paced way, which can allow faster time to market of new products and customer‐centric journey improvements with potentially positive implications on revenues. At the same time, previously untapped sources of creativity can be leveraged with potential differentiation upsides. Furthermore, in an idealized team of digitally up‐skilled teams the need for management overhead can be decreasing. Finally, more flexible working can lead to easier integration with skills outside of the firm—that is, from a wider ecosystem—potentially at lower fixed cost.

Workforce Skills Payday Decelerators However, the benefits of a more capable workforce also come at a price. Investments into establishing an end‐to‐end new way of working are required to avoid that the wave of accelerators just discussed is not hitting the breakwater of less‐skilled units and thus losing all its power (it might be the IT department in an agile business team or the other way around—the business team in a DevOps skilled development team). Because the skills to set up this new governance and ignite the spark of overall cultural change are usually not sufficiently scaled in‐house, external agile change coaches usually need to be hired and kept on board at substantial cost for a significant period until the end‐to‐end transition has been achieved. Especially in this transition period, this implies that management has less control of what is happening, a dangerous risk, especially if this occurs parallel to a large‐scale technical implementation program. It goes without saying that, given the scarcity of the capabilities just explained, additional retention cost and higher salaries must be considered, because the war for such talent is all over the place and loyalty of skilled workers falters, especially when they mostly work in digital‐only environments without proper attachment to a brand or a team or even less a manager. This and additional accelerators and decelerators will be explained in more detail later, when addressing the catalyst of new workforce expectations.

Table 4.9 clusters some key accelerators and decelerators for workforce skills depending on their typical tangibility.