Understanding the impact of the revolution

乌鸦传媒 defines generative AI as a technology with the capability to learn and reapply the properties and patterns of data for a wide range of applications, from creating text, images, and videos in different styles to generating tailored content. It enables machines to perform creative tasks previously thought exclusive to humans.

乌鸦传媒 Research Institute reports nearly all (96%) executives say generative AI is a hot topic of discussion in their boardrooms. Across organizations in every sector, digital and business leaders are talking about how generative AI might be applied to a series of use cases from customer engagement to sales processes and onto operational activities.

It鈥檚 important to recognize that generative AI is more than just chatter. that in a recent Gartner, Inc. poll of more than 2,500 executive leaders, 45% reported that the  has prompted them to increase artificial intelligence (AI) investments. Seventy percent of executives said that their organization is in investigation and exploration mode with generative AI, while 19% are in pilot or production mode.[1]

The rate of investigation is uppermost at high-tech companies. Where OpenAI led with its work on ChatGPT, other vendors are now following quickly in their footsteps. 乌鸦传媒 Research Institute reports 86% of organizations are either working on generative AI pilots or have already enabled functionality.

Attend any technology conference today and you鈥檒l hear a barrage of AI-related product launches. Fearful of falling behind their competitors, technology vendors are fighting to grab a piece of the generative AI action. The technology market is being flooded with standalone tools and AI-enabled additions to existing systems and services.

Panning for nuggets amongst this AI gold rush is an intractable challenge. At work, curious staff are beginning to use generative AI in their everyday activities and executives are looking at ways to harness that momentum.

While some of these tools could lead to huge boosts in productivity, it鈥檚 crucial to understand how these tools exploit data and how they might operate as part of an integrated technology stack. Today, we believe this deeper awareness is lacking. The result is a series of major hurdles that are associated with publicly available generative AI models:

• Too disjointed 鈥� They don鈥檛 address the needs for risk, privacy, and business controls.
• Too universal 鈥� They don鈥檛 understand business knowledge and cultural context.
• Too uncontrollable 鈥� They don鈥檛 have mechanisms to control the quality of outputs.
• Too risky 鈥� They don鈥檛 prevent third parties from reading and learning data.
• Too immature 鈥� They don鈥檛 have a built-in and enterprise-scale technology stack.

Developing an awareness of what鈥檚 next and what鈥檚 possible

At 乌鸦传媒, we recognize the potential benefits of generative AI are undeniable, yet so are the potential risks that come from unregulated deployment. Business leaders can鈥檛 afford to let the technology be implemented without due cause and consideration. In this fast-moving area of innovation, it鈥檚 vital to establish what鈥檚 coming next and what might be possible.

乌鸦传媒鈥檚 dedicated Generative AI Lab is working to develop this important insight. Our group-wide effort aims to understand developments and advances in AI. With the rapid rise of generative AI, we expect the pace of change to quicken further. We anticipate both impactful breakthroughs in capabilities and unforeseen challenges and applications.

The Lab orchestrates our efforts to make sense of this revolution. We develop thought leadership, research, and internal readiness in this emerging area, allowing the wider group to develop a strong sense of how generative AI will affect all businesses today, tomorrow, and long into the future. The Lab鈥檚 work concentrates on two key horizons:

1. Internally, we provide a lighthouse effect on what鈥檚 coming next in generative AI. We develop an awareness of the key capabilities that are required, providing an early warning to our group of any major changes that are emerging.
2. Externally, we present industry-leading thought leadership on the opportunities and challenges from advances in generative AI. We undertake research and development alongside partners and academics, establishing practical responses.

Our Lab is staffed by a dedicated team of 乌鸦传媒 AI experts from around the world. While the rush to implement generative AI is a recent trend, the technology itself has been a long time in gestation. During this period, 乌鸦传媒 has worked with clients on AI across multiple sectors, including life sciences, consumer products and retail, and financial services.

We鈥檝e helped a life science company re-sequence DNA and we鈥檝e supported banks as they鈥檝e used generative AI to translate old software into modern languages. We鈥檝e worked on documentation for highly complex engineering products, and we鈥檝e partnered with an insurance firm as its uses natural language to provide accurate answers to non-technical staff.

The experts in our Generative AI Lab will draw on these experiences and develop internal knowledge and external responses as professionals continue to explore emerging technology.

Conclusion: Rewards without the risks

The rapid rise of generative AI brings excitement and concern in equal measure. However, three-quarters (74%) of executives believe the benefits of generative AI outweigh the risks, according to the 乌鸦传媒 Research Institute. Our Generative AI Lab has been created to identify pathways to a brighter, AI-enabled future. The Lab will work to clear the obstacles and build the bridges that will help us all reach this destination successfully.

[1] Gartner Press Release, , May 3, 2023. GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.

I recently started working at 乌鸦传媒, having spent the last 6 months at home with my 1.5-year-old child. During this time, virtually all major technology companies launched their own version of generative AI or services related to it. While I was immersed in cleaning the highchair and diaper changing- the Twitter-verse (or should I say 鈥淴-verse鈥�) was abuzz with hashtags like #ML, #AI, #GPT and #LLM. For a solution architect like me, the world looks very different from what it was just a year ago.

While the technology landscape hasn鈥檛 changed that much in a year, the dawn of generative AI has been a major eye-opener in some respects. The promise of AI in a marketing context is enticing: the ability to generate personalized experiences with speed and scale. Even though the learning curve may seem steep, the reality is that you don鈥檛 have to be a data scientist to understand the business implications of AI. Let鈥檚 dive a bit deeper and I鈥檒l explain why.

How to train an AI?

A common misconception about AI models is that you always need to have lots of data to use one. Large language models (LLM) like GPT-4 do initially require vast amounts of data and training but that essentially constitutes as product development. An LLM requires human effort to 鈥渢each鈥� it to weed out inaccurate and false answers to user prompts. After sufficient training, the model can answer most user queries with decent accuracy. At this stage, it can be deployed and made publicly accessible. These pre-trained models can be implemented to specific business needs in a couple of ways.

The first method is via fine-tuning the model. Instead of using huge computational power and human working hours to train a model from scratch, . The model will likely provide decent answers to user prompts right from the start, owing to the model鈥檚 extensive pretraining. However, human supervision is required to flag all biases, misconceptions and falsehoods that surface. Over time, the model becomes more attuned to its new environment, and its accuracy improves.

The alternative to supervised training is to use what is called in-context learning (ICL). Instead of humans supervising the model and giving it feedback, , making predictions based on past information present in where the model operates in. A typical use case is deploying an LLM to a CRM environment, such as is the case with Salesforce鈥檚 Einstein GPT. The model looks for past records and uses that to provide more accurate answers to user queries. This method of training is attractive in that it has a greatly reduced need for human effort.

The inescapable truth of data quality

Whichever method you choose to train an LLM, you always run into the same conclusion: data quality is key. Bringing us back to the marketing context: what does good quality data mean? Marketing is unique in that it relies heavily on both external and internal data sources. Marketing deals with customer demographics; interests and preferences; website engagement; product or service usage; contact information; and purchase history. With the variety of data sources, there is also a greater risk of data management issues, such as:

• Duplicate leads
• Contradicting marketing permissions
• Outdated contact information
• False or spam contacts
• Mismatched marketing engagements

Identifying and fixing these issues should be your first plan of action if you plan on leveraging AI in your marketing. Failure to do so could lead to escalating already existing problems. For instance, if you provide the LLM bad data as context, you end up with biased, inaccurate, or simply false suggestions.

In the case of personalization, this can cause prospects to receive wrong versions of dynamic content. As for segmentation, bad data fed to the model can cause triggered automations to target the wrong people. The worst thing is that this kind of AI malfunction will go unnoticed until a customer complains about it. And then it will be too late. Bad data will undermine all efforts to incorporate AI into marketing.

Avoid pitfalls with a clear strategy

There are plenty of what I call 鈥淎I nihilists鈥� out there. A common criticism is that LLMs will always produce biased, limited, or flawed results. There are others who believe LLMs鈥� potential to be fundamentally out of reach for most businesses. I鈥檝e never found this sort of attitude particularly helpful. We saw the same thing with Electric Vehicles, and now we鈥檙e seeing an unprecedented surge in both demand and production of electric cars. Which side do you choose: the progressives or the laggards?

Assuming you chose the former, you are looking for a way to combat the problems I鈥檝e outlined in this post. The best way to do this is by shifting perspective to a strategic level.

You need what I鈥檇 call a marketing data strategy. It sits between the company marketing strategy and data strategy. Essentially, we want to explore how data and AI relate to marketing strategy building blocks, e.g.:

• Segmentation (How do we identify our key segments? Which data sources do we need?
• Targeting (Which life events or milestones do we look for?)
• Positioning (How do we determine customer pain points? How do we know if our marketing is successful?)

The above are illustrative examples; your marketing strategy is your own, and there is no need to reinvent it. Ideally, you want to leverage what you already have as much as possible. There are, however, certain areas that you want to include in your marketing data strategy regardless of your chosen format. Here are my suggestions:

A marketing data strategy lays out rules and policies that intend to help organizations to develop their marketing technology stack, incorporate new data sources, build new marketing workflows, or adopt new processes. It provides a bedrock that you can anchor decisions on, and it ensures that you consider all relevant questions before making major data-related decisions. As is the case with any strategy, this too is a living document; don鈥檛 let it fall into disrepair 鈥� include regular checkpoints to assess and update the strategy.

Final thoughts

We鈥檝e outlined the possibilities and risks associated with the use of LLM in marketing. By now, you should have a better understanding of what it takes to adopt an LLM in a business context. I鈥檇 like to add that LLM adoption requires a significant investment of company resources. To work properly, an LLM may require a Customer Data Platform, data lake, data management platform, analytics platform, or a combination of these. In addition, you need to hire or allocate specialists to supervise and develop your organization鈥檚 AI capabilities. Finally, I strongly recommend including a Chief Data Officer role for any business seeking to tap into AI. With top-level sponsorship, any such initiative has much better odds at succeeding.

Catch up on my session at Dreamforce where I explored this topic even further

You will need to sign up for a free Salesforce+ account to watch this video.

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3 keys to success: Using data to build empathy, drive personalization, and enable scale

1. Empathy: Building deep and lasting relationships through emotion-based engagement

At the heart of every great experience is emotion. Customers want to feel like the brand knows who they are and understands their needs. This relevance is how the brand becomes an irreplaceable part of the customer鈥檚 life 鈥� not simply because of the products or services they offer but because the brand evokes a feeling within the user.

Take, for example, travel. The most successful airlines aren鈥檛 selling flights 鈥� they are building memories. The same is true of fashion: Clothing brands don鈥檛 just produce shirts and pants; they help people shape their image and live with confidence. Leading consumer products companies don鈥檛 pump out pantry staples, they offer ways for people to live healthy, sustainable lives.

Understanding the needs and desires of your customers to forge this emotional connection requires a combination of enterprise data (customer insights owned by the brand) and experience data (insights developed through a combination of third-party sources) to pinpoint the issues that matter and the moments when they matter most.

At the same time, it鈥檚 important to remember that feelings can be fleeting. What matters today may not resonate tomorrow. This means that companies also must continually design, refine, and optimize journeys to deliver a CX that aligns with the consumer鈥檚 changing worldview. While the brand promise and CX vision should remain stable, the channels, messages, timing, and tone are all subject to change based on the consumer鈥檚 emotional state and the events happening around them. This is why it鈥檚 so important to not only have a clear sense of what matters to each customer, but the ability to quickly act on new, timely insights when they will resonate most.

2. Personalization: Leveraging actionable data and measurement to connect on an individual level

Emotions are personal. So, if a brand wants to elicit an emotional response from a consumer, then they need to take a personalized approach, identifying when, where, how, and why to interact with each person at a given moment.

For example, some quick-serve restaurants are experimenting with using geolocation data 鈥� in combination with enterprise and experience data 鈥� to text message offers to people when they are in or near a physical location. Whether that text message is a valuable interaction or annoying interruption depends on how well the brand knows that particular customer and the degree to which they personalize the offer. By factoring in both enterprise data (favorite menu items, purchase history, ordering history) and experience data (time of day, trending menu items, motivations as a person), the brand can send hyper-personalized offers, tailoring each message so that it makes sense to the individual, be it a quick lunch option or a low-calorie snack before cycling class.

Brands could also take it a step further and proactively fill the need for customers. For example, if the QSR knows that a customer orders a specialty coffee every weekday morning on their way to work, but their geolocational data shows that the person is running behind schedule one day, then the brand can send a text asking if they鈥檇 like to place their usual order. On the other hand, when the brand sees that a person who normally visits a city location every weekday is on holiday, the company might send them a mid-day offer instead, urging them to pick up an afternoon treat from the local franchise on the way home from the beach.  

Hyper-personalization requires speed and agility to ensure the offer is timely and relevant. Companies should also assess performance continuously and track the impact of those offers on the business. This is critical to identifying which efforts should be scaled and which should be adapted or set aside.

3. Scalability: Using the customer technology platform to connect and scale

To offer personalized, emotion-based experiences at scale, companies need to take a platform approach. There are two separate, but equally important pieces at play: 1. An engagement platform, such as Salesforce; 2. A data platform, such as MuleSoft, to serve as the integration layer.

While Salesforce is an incredibly powerful tool, it must be powered by clean, accurate, timely, and complete data from across the business in order to unlock its full value. MuleSoft provides that critical integration layer, drawing data from other systems, including the ERP, and allowing companies to create a single way of interacting with the customer across sales, service, marketing, and commerce.

These two elements, when combined, provide the platform with scalability, allowing companies to maintain high levels of personalization even as they increase the volume of interactions. 

Finding value at the intersection of empathy, personalization, and scalability

While each of the three above elements is important to the overall success of the business, they only generate the most value for both the business and the customer when they intersect.

For example, a personalized interaction will often drive an emotional connection. But if there is no platform in place to enable companies to personalize at scale, then the value of those efforts will be limited, no matter how accurate the data and relevant the interactions may be.

Likewise, companies that have a robust integration layer may be drawing data from across the business, but if teams are using large customer segments to define their campaigns and send messages en masse, then these elements will lack the personalization 鈥� and, by extension, the emotional connection 鈥� to create real impact.

If you’re interested in finding out more, I’d love to explore with you how companies can unlock the value of their data in Salesforce with 乌鸦传媒. Drop me a message using the options below.

Building on your unique skills and knowledge

By combining your company’s unique knowledge with foundational models to create tailored Gen AI solutions, you can deliver reliable outcomes at scale while addressing your specific business needs. Together, we can unlock this full potential and rewrite the boundaries of what’s achievable with our new offer Custom Generative AI for Enterprise.

We help you elevate and focus on your excellence to unleash new possibilities. This is what our Custom Generative AI for Enterprise is all about: building on the unique skills and knowledge that make you, you. And it鈥檚 because we are tailoring from your data, business knowledge and context that results will create maximum impact and benefit your organization.

Rather than sharing clients examples, I prefer to illustrate this with our partnership with Dinara Kasko, an extraordinary creative talent and architect-designer. At the intersection of GenAI and 3D printing, she is building on her skills to create unique art pieces in the shape of patisserie, unleashing her creative process with the power of technology.

We are collaborating with her with a bespoke solution to elevate her possible with Generative AI. Stay tuned for exciting updates!

And if you are curious about the new possibilities of Generative AI and the rapid pace of its technological advancements, !

Quantum Computing has the potential to be revolutionary in many computation-heavy area鈥檚: ranging from drug discovery to financial applications. The reason? Higher accuracy and faster computation times. However, one question is often neglected: at which cost? We鈥檝e seen that supercomputers and data centres can consume an enormous amount of energy [1,2]. Will quantum computers be the next energy-thirsty technology, or are they instead the gateway to a green computing era?

Quantum computing uses the most intriguing properties of quantum physics: entanglement, superposition, and interference. Quantum computers use these phenomena to do calculations in a completely different way than normal computers do. The result is an enormous speedup of the calculations, the ability to achieve higher accuracy levels, and solve problems that are intractable for the classical computer.

These quantum phenomena take place at a very small scale: the scale of an electron. As such, one computer calculation would barely cost any energy. However, to observe these potent quantum phenomena, the system must be completely isolated. Temperatures must be cooled to near absolute zero (-273 degrees Celsius). This comes with a large energy bill.

The energy consumption of a quantum computer scales fundamentally different from a classical computer. Classically, there is a linear scaling with problem size and complexity. For quantum computers, this may be very different. Insight into this new energy consumption of a quantum computer is essential for a green future of quantum computing.

The Scaling of the Energy Consumption

Currently, the power consumption of a quantum computer is about 15-25kW, due to the cryogenic refrigerator [3, 4, 5]. This is comparable to the energy consumption of about 25 households. Note that this power is not only consumed when a calculation is performed but is continuously consumed by the quantum computer. This leads to a large energy bill.

There is hope for the future. When a classical computer becomes twice as large, it requires twice as much energy. In the near future, a quantum computer, by contrast, may barely increase its energy consumption when scaling up. This is because the cooling volume barely increases, and heat created by extra electronics is also not expected to be significant. The largest quantum computer today is 127 qubits and scaling to 1.000 or even 10.000 qubit is possible with similar energy consumption.

In the far future, we envision quantum computers with millions of qubits, situated in large data centres. It would be naive to assume that this does not add any energy costs. Recent research shows that the energy costs will scale with the number of qubits and operations at a point in the future. This is mostly due to increased cooling costs.

There is another very important factor that positions quantum computers as potential candidates for green computing. The idea is as follows: if you must run a supercomputer for a month to solve a specific problem and a quantum computer can do it within minutes 鈥� this drastically reduces the energy cost. An example of how energy costs would scale differently for Monte Carlo simulations is shown in figure 1.

Figure 1: The Energy Consumption of a Quantum Computer scales very differently than that of classical computers. When high accuracy or complexity is required, the quantum computer may become the more 鈥渟ustainable鈥� candidate.

Recent research shows a difference in energy consumption between quantum computers and classical computers of a factor of 10.000 (!) [4]. A clear quantum energy advantage, but for a toy problem, favouring the quantum computer. The question remains whether this is applicable to more generic problems.

Recently, an energy estimate for a more generic problem was made, namely breaking the RSA encryption [6]. RSA is a very common encryption method for secure data transmission. The quantum computer is expected to have an energy consumption of 1000 times as little as a classical computer. It must be noted that this energy estimate was based on futuristic full-stack quantum computers, and still require major advances in quantum hardware.

Interestingly, this estimation also showed the timeframe where a quantum computer might be slower but requires less energy [6]. This gives a great perspective for the future. Before implementing quantum computers due to their speedup, can we implement them for green computing?

Green Computing for Financial Institutions

At 乌鸦传媒, the Olive project researched the opportunity of using quantum computers for green computing in the financial industry. This is specifically applied to using quantum computers for pricing derivatives, based on a new algorithm that allows one to do this on a quantum computer [7,8]. (See more here)

Green Computing is becoming increasingly important for financial institutions. Mischa Vos, Quantum Lead at Rabobank (one of the largest banks in The Netherlands), emphasises its importance for Rabobank:

鈥淎t Rabobank, sustainability is an integral part of our corporate mission: “Growing a better world together. The focus is now on green coding and sustainable data centres. On top of that, Rabobank is investing in green computing technologies. Quantum Computers would be an interesting new candidate.鈥�

Financial institutions use an enormous amount of computational power to ensure security, price financial products and perform risk management. Based on the insight about the 鈥渜uantum energy advantage鈥�, quantum computing can reduce the carbon impact of these computations. Would this be interesting for Rabobank?

鈥淭his has great potential for Rabobank. Running these calculations, especially when Artificial Intelligence is involved, has a negative impact on the carbon footprint of Rabobank. Rabobank is dedicated to reducing this. At the same time, as a financial institution, we still need to perform accurate risk analysis and provide security. If quantum computing would allow us to combine the two, this would be very interesting.鈥�  

There may be a timeframe when the quantum computer is slower, but more energy efficient than classical computers. Would Rabobank already be interested in quantum computers at this stage?

鈥�There are certain batch-oriented calculations that Rabobank performs, and these would be ideal for this. For example, evaluating the risk portfolio of investments at a large scale, or certain fraud detection methods. There will definitely be opportunities where Rabobank can already use the slower, but more efficient quantum computers during this time frame.鈥�

A future scenario

The current hardware limitations are the main bottleneck for practical quantum computing. However, it is important for financial institutions to be ready for implementing quantum computers when the time is right, especially when this can be important from a sustainability perspective.

Phase 1. Research & Development

The current hardware limitations are the main bottleneck. As such, firstly, the hardware challenges need to be overcome before it becomes feasible to run relevant calculations on quantum computers. The Quantum Energy Initiative points out it is important to already make conscious design choices during this phase to ensure an energy-efficient quantum computer [9,10]. This should not slow down technological progress but instead, prepare for long-term energy advances.

Phase 2. Green Energy Advantage

Due to slow quantum clock speeds, and intensive quantum error correction codes, the quantum computational advantage can take longer than the quantum energy advantage. As such, the first applications of quantum computers may be due to their energy efficiency. This will be dependent on the specific advances in quantum hardware.

Phase 3. Overall Quantum Advantage

Finally, both the quantum computational advantage and quantum energy advantage are achieved. Here, it is important to make conscious choices in the usage of quantum computers and avoid the Jevon paradox. See for example on quantum for sustainability. On the other hand, this is also the phase where quantum computers can really make a difference in sustainability 鈥� making better simulations leading to better material design all the way to general climate crisis mitigation plans [11]. 

Technology leaves an indelible mark on the environment. 乌鸦传媒 is determined to play a leadership role in ensuring technology creates a sustainable future. 乌鸦传媒 can help with implementing sustainable IT as the backbone of a company for a greener future.  It is important to consider the environmental footprint of emerging technologies. 乌鸦传媒’s Quantum Lab can help clients understand the future possibilities of quantum technologies and build their organization and strategy that will make the potential become a reality. With this project, more insight into the real environmental cost of quantum computers is acquired, as well as the opportunities that Quantum Computers can give for green computing.

For more information on the results of Milou鈥檚 research, watch the webinar

References:

[1] IEA, Data centres and data transmission networks, 2022. [Online]. Available: https://www .iea .org/reports/data-centres-and-data-transmission-networks .

[2] A. S. Andrae and T. Edler, 鈥淥n global electricity usage of communication technology: Trends to 2030,鈥� Challenges, vol. 6, no. 1, pp. 117鈥�157, 2015. .

[3] F. Arute, K. Arya, R. Babbush, et al., 鈥淨uantum supremacy using a programmable superconducting processor,鈥� Nature, vol. 574, no. 7779, pp. 505鈥�510, 2019.

[4] B. Villalonga, D. Lyakh, S. Boixo, et al., 鈥淓stablishing the quantum supremacy frontier with a 281 pflop/s simulation,鈥� Quantum Science and Technology, vol. 5, no. 3, p. 034 003, 2020.

[5] Personal communication with Olaf Benningshof, Cryoengineer of QuTech, 2023.

[6] M. Fellous-Asiani, J. H. Chai, Y. Thonnart, H. K. Ng, R. S. Whitney, and A. Auff猫ves, 鈥淥ptimizing resource efficiencies for scalable full-stack quantum computers,鈥� arXiv preprint arXiv:2209.05469, 2022.

[7] P. Rebentrost, B. Gupt, and T. R. Bromley, 鈥淨uantum computational finance: Monte carlo pricing of financial derivatives,鈥� Physical Review A, vol. 98, no. 2, p. 022 321, 2018.

[8] N. Stamatopoulos, D. J. Egger, Y. Sun, et al., 鈥淥ption pricing using quantum computers,鈥� Quantum, vol. 4, p. 291, 2020.

[9] A. Auffeves, 鈥淨uantum technologies need a quantum energy initiative,鈥� PRX Quantum, 3(2), 020101., ISO 690, 2022.

[10] quantum-energy-initiative.org [11] Berger, Casey, et al., “Quantum technologies for climate change: Preliminary assessment,” arXiv preprint arXiv:2107.05362, 2021.

The term 鈥榙ark factory鈥� can be thought of as metaphorical, for example, the factory might not actually be completely dark – its machines may require some light, if equipped with optical sensors.

Dark factories are a part of the global digital transformation and move to the Industrial Internet of Things (IIoT), which is being driven by increasingly capable robotics and automation, AI and 5G connectivity. In this article, we鈥檒l discuss the benefits, challenges, and how companies can move forward with this concept.

Pros and opportunities

Dark factories offer a number of benefits.

• First among them is increased efficiency and productivity. Dark factories are favourable on classic efficiency drivers such as production output, for example, offering 24/7 capacity beyond traditional shift hours 鈥� and they are unaffected by the human need for breaks, vacations, or sick days. And a secondary benefit is that dark factories do not need to be located near a labor pool – which means they can be set up in other areas, exploiting opportunities like cheaper land prices or more attractive surroundings.
• This also makes them more sustainable. Dark factories can be designed to be more energy-efficient and environmentally friendly than traditional ones; an obvious example of this is that they can do away with lighting and central heating.
• All of that means decreased operating costs, due to a reduction in non-added value tasks and staff numbers, a benefit which is especially prominent in high labor cost areas.
• It also improves worker safety. Fewer workers present means reduced risk of accidents and injuries in the workplace, a significant challenge in hazardous environments. Moreover, repetitive and physical tasks can be monitored (and assisted) to avoid safety issues or future physical disablement.  
• Finally all this can lead to improved quality as well as performance. Highly specialised machines monitored by a new generation of integrated industrial information systems work with the kind of efficiency that a human cannot match. They can also provide relevant recommendations to the operator, to avoid mistakes or support decisions (eg. to recycle the product or anticipate corrective actions).

Cons and challenges

There are, of course, some shortcomings.

• Whether retrofitting an existing brownfield facility or building a greenfield one from scratch, the CAPEX required to create a dark factory is considerable – new infrastructure is required and existing infrastructure may require modification. As is obvious, there are a number of technological barriers to overcome also, for example – AI, ML, 5G, robotics and system integration. These questions should be addressed with a clear vision of the future industrial platform and/or footprint, in order to avoid any 鈥渢echno push鈥� (a risky approach in which new products and services are driven by new technology and not validated by existing market needs).
• Additionally, dark factories will necessitate new training and staffing requirements.It鈥檚 clear that new specialist skills will be required in order to design, install, maintain and operate the systems that will run these plants.
• Suitability, scalability and over-specialization form another issue. Humans are still better at many tasks, and not all processes can be automated (yet). It may be a long time before dark factories are suitable for certain types of manufacturing. For example, it’s more difficult to build generalized (as opposed to specialized) automated systems and processes. This may limit a manufacturer’s ability to quickly respond to changes in production requirements. Here, we require AI sophisticated enough for generalized problem-solving (without human aid). For example, the automation of quality control is a particular challenge.
• Technological dependence is another issue that must be planned for. Cyber-driven industrial espionage is already a serious problem in conventional factories. The sheer connectivity of dark factories creates security vulnerabilities that could be exploited by malicious actors. This could result in data breaches, production disruptions, or worse. In addition, any non-malicious technical failures could result in major production delays without rapid human intervention.

The new human structure of the Dark Factory

How might humans fit in this new environment?

Lean manufacturing taught us that we could cut out much of middle management and improve the efficiency of operations. A dark factory could cut the bureaucracy further. Broadly speaking, the dark factory means fewer people in total, but more added value per person.

Consider the 鈥檈nhanced operator鈥� – which could be an XR-equipped human who makes periodic visits to the facility. Instead of a person with specialist skills on one part of an assembly line, this enhanced operator would be a generalist, with a very broad understanding of the factory鈥檚 E2E processes and systems.

Headcount may reduce, but collaboration will still be key. First – collaboration between teams to understand systems, engineering, impacts on manufacturing, impacts on operations and how to handle complex situations. Second – collaboration between robots and humans, to perform complex tasks requiring both capabilities.  

Darkening the factory: what now?

Implementing a dark factory (either from scratch or by retrofitting an existing facility) will not be easy. And, the pace of transformation is sector dependent. For example, it is easier to completely automate simple and repetitive tasks, ones in which every step in the end-to-end process is understood, down to the movement and the millimeter. But not all kinds of manufacturing are quite so straightforward. As companies progress the concept, here are some steps to consider.

A transformation roadmap and change management plan

Identify the steps you need for your transformation roadmap. Is now the right time? Transitioning to (or constructing) a dark factory requires a significant investment of time, resources, and capital. It’s important to carefully evaluate the potential benefits and risks of this transition before making any decisions.

Conduct a thorough analysis of the existing manufacturing processes to determine which ones can be automated and which cannot. Is it still worth it, in light of this?

If so, you may need to work with a recognized specialist company to determine which technologies will be most effective for your specific manufacturing process. The transition could also be phased – for example, a partially automated factory could run a 鈥檇ark shift鈥� overnight, which could provide a test or proof of concept.

And of course – build cyber security into the plan, not as an 鈥榓fterthought鈥�. The dark factory鈥檚 level of connectivity (and potential vulnerabilities that result) requires it.

Consider the human implications

How can we keep humans safe in this new (mostly) non-human environment? What safety measures are required – for example, can you create areas that are safe for people to traverse? And how must people behave in a space built primarily for robots, not humans? 

Anticipate and prepare for workforce transformation: think about recruiting for the skills needed for tomorrow. What will be done about those who may lose their job to a robot – can they be retrained and retained?

Consider future operations: flexibility and scalability

As previously mentioned, people are more flexible than robots and machinery. As such, forward planning must consider how the infrastructure will flex and scale, in order to meet future market needs. Detailed monitoring and analytics can help here, identifying what systems can be optimized or replaced.

Dark factories, bright future?

The fragility of global supply chains has become increasingly apparent in recent years – Russia鈥檚 2022 invasion of Ukraine, and the COVID-19 pandemic, in particular, have demonstrated the need to 鈥榦nshore鈥� (bring back) manufacturing, so as not to be dependent on foreign sources of vital goods.

But manufacturing was, of course, originally 鈥榦ffshored鈥� because it was cheaper to do the work abroad. Dark factories could be an equalising force – bringing down costs so goods can be produced back at home.

It鈥檚 also important to consider that fully automated factories have been tried previously, with varying degrees of success. There are a few cautionary tales; IBM tried its own in the 1980s, but closed it because it wasn鈥檛 able to respond to changing market needs. Apple also built such a plant in the 1980s, but closed it in the early 90s – likely because the plant was unable to deal with increasingly smaller components. More recently, Tesla walked back some of the automation at its Fremont CA facility, when machines failed to meet its ambitious manufacturing targets. This shows us the importance of flexibility and forward planning.

That said, successful dark factories do exist today. In perhaps the best example, robotics manufacturer, FANUC (Fuji Automatic NUmerical Control), operates a lights out facility in Japan. Here, complex robots assemble other complex robots, with zero human involvement in the manufacturing process.

As the previous examples demonstrate, success with a dark factory can be difficult – but is possible. Dark factories offer transformative benefits in terms of cost efficiency, sustainability, safety, and supply chain resilience. They also offer a considerable competitive advantage to those who 鈥榞et there first鈥�, who get it right and, returning to Philip K Dick鈥檚 Autofac, keep control in human hands.