ARCHIVE FOR THE ‘ai’ CATEGORY

In this the second and concluding feature in a two-part series of extracts from a white paper published by IFS exploring the effect of Artificial Intelligence on field service operations we explore the challenges of implementing AI in a field service operational workflow...

In this the first of a two-part series of extracts from a white paper published by IFS exploring the effect of Artificial Intelligence on field service operations we ask is AI set to be a help or a threat in the not too distant future? 

Ours is an industry that has always had collaboration at its heart, but today we need to be thinkjing more and more about how humans can be collaborating with Artificial Intelligence and one another. Titos Anastassacos, Managing Partner at Si2 Partners, explains more...

M5STACK expands further into AIOT(AI+IOT) edge computing market with the K210 RISC-V 64 AI Camera.

Despite words like innovators and pioneers being regularly used to describe high profile companies, there is an argument that in business you never really want to be the first. Wang Computers released the Wang 3300 five years before the Apple 1. Friendster arrived two years ahead FaceBook. The difference between bleeding edge and earlier adopter can be critical to success as Rohit Argawal, Editorial Director at Copperberg writes...

The ‘creation of a technology’ and the ‘adoption of the technology’, what’s more important? One way to look at it is that technology prowess for an organisation helps it advance and differentiate but its scope is limited unless the technology is adopted widely and simplifies tasks or generates revenue.

Narrowing down to the Field Service Industry, keywords such as ‘Democratised Service’, ‘Augmented Workforce’, ‘Humanoid Field Workers’, are abundant and very easy to encounter today in most articles, podcasts and webinars. The hype around IoT, AI, AR and VR is causing Field Service Directors to sweat and are inducing fear of being left behind in the digitalisation race.

The major question of the hour is: has the industry crossed ‘The Chasm’ yet for digitalisation? For those not familiar with the technology adoption lifecycle curve, the curve breaks down technology adoption into five phases with respect to time. When a new technology is introduced, the innovators (read tech geeks, influencers and technology over-enthusiasts) are the first to try it. In the field service area, these innovators would be large field service companies that have an abundant budget, manpower and cushion to fail for new innovations.

Once these innovators find a use case for the technology and deem it fit is when the early adopters start using the technology. This is the make or break zone for most technology. The number of users increases non-linearly and more rapidly compared to the initial phase.

To move from the innovators to the early majority is the toughest phase for the technology and is known as ‘crossing the chasm.’ After the ‘chasm,’ the use of technology increases rapidly till peak usage when the market starts to saturate and the late majority comes in. The laggards are technophobics who are last to adopt the technology. Most field service companies that consider keeping machines up and running as important play it safe and would be in the ‘early to late’ majority category.

Coming back to digitalisation in the field service industry, the majority of field service organisations have started addressing the need for IoT and data collection to ramp up their field service offerings and have more satisfied customers. At the recent Field Service Forum 2019 in Amsterdam, Europe’s leading event for field service, more than 115 Field Service Directors came together to discuss the present trends in field service, the upcoming challenges and the future of customer satisfaction.


         "To move from the innovators to the early majority is the toughest phase..."


Most of them agreed that IoT and data will have a major impact on service businesses and that they need to start small, arrive at results and then move forward. They acknowledged the speed of technology development today and also benchmarked their own services to the standards set by the keynote speakers. Acknowledgement of the impact of the technology by the wider audience and relating to case studies show that IoT has crossed the chasm and reached the early innovators. All those not on board the IoT bandwagon are now scurrying to do so.

According to Gartner’s Hype Cycle for Emerging Technologies 2018 report, IoT platforms will reach their plateau of productivity in the next 5-10 years. The Field Service Directors who have adopted IoT and data collection reflected that tech trends can be misleading and that they should rather focus on business problems. Translating the data to meaningful insights that can lead to better business decisions. There was also contemplation and debate on whether machines could take over humans in the workplace, though the consensus was that it wouldn’t be likely.

One technology that can help in this data processing and generating insights is AI. However, only the early innovators and technology leaders have tested it so far. Has AI jumped the chasm in the field service industry? Not yet.

Most innovators are still creating use cases and the projects are on test-beds. The majority of field service leaders are starting to see the potential and value in using AI in their data processing, but then the implementation, adaptation and ROI are a long way down the path.

Another technology that is premature but is deemed to have high value is augmented and virtual reality. The potential to have an experienced technician assisting a new line of the on-field workforce is very appealing but will the customer be satisfied and confident with the blunt show of inexperience? Will the chances to have faulty repairs increase once the technology is out of test trials or on the field?

There is always a debate about technology, its potential forecasted and the actual benefits derived. Over the next few years, we will realise if these technologies will jump the chasm and go on to become basic necessities in the field service business.

To be involved in the Field Service Directors Community, pre-register here for the Field Service Forum 2020. 

Artificial Intelligence has increasingly become a key discussion in all industries and its impact in field service management is predicted to be hugely significant, but how should field service organisations leverage this powerful twenty-first-century technology? In the part one of this two-part feature Marne Martin, President of Service Management, IFS outlined why AI in field service is about far more than chatbots, now in the concluding part, she outlines how AI can bring a touch of genius to your field service operations...

 

---

Is Artificial Intelligence a key topic for you?! The full white paper on this topic available to fieldservicenews.com subscribers. Click the button below to get fully up to speed!

---

Sponsored by:

---

Data usage note: By accessing this content you consent to the contact details submitted when you registered as a subscriber to fieldservicenews.com to be shared with the listed sponsor of this premium content who may contact you for legitimate business reasons to discuss the content of this content.  

---

 

Solving Problems When One Isn't Albert Einstein

Human agents are capable of optimally dealing with a customer, and AI can free them up for the most interesting and demanding tasks. In the case of scheduling technicians in the field, humans are just not up to the numerical challenge of adjusting a schedule in an optimal fashion as humans typically focus in on an aspect of a problem to solve rather than finding the best solution overall.

A dynamic scheduling engine (DSE) driven by AI algorithms is designed to solve complex scheduling problems in real time—problems much too complex for any human dispatcher or customer service agent to handle, especially when at times individuals will act myopically based on their area rather than for the greater good of the company and its customers.

 

"Even a static service schedule can be handled in myriad different ways and decisions regarding which technician to send to which of several jobs in what order are often made based on suboptimal heuristics..."

 

Even a static service schedule can be handled in myriad different ways and decisions regarding which technician to send to which of several jobs in what order are often made based on suboptimal heuristics.

“Steve’s son is in daycare in this part of town, so I will schedule this appointment last, so he will be close by.” Sometimes jobs are scheduled based on first-in, first scheduled, regardless of the actual urgency of requests that come later.

Manual or traditional software-based scheduling may be a workable solution for service organizations with a very small number of technicians each engaged in a small number of jobs during a day. But it does not take many technicians or jobs for the number of possible solutions to outstrip human computation capabilities either individually or as a group.

Even at the low end of the spectrum, a human dispatcher cannot quickly identify all the possible solutions and pick the best one. With two technicians and four service calls there are already 120 possible solutions— different combinations of technician, job and order. Two technicians, and five service calls yields 720 possible solutions. Four technicians and 10 service calls present a dispatcher with 1,037,836,800 possible solutions.

But the time you get to five technicians that must complete six calls each—a total of 30 calls, you have 12,301,367,000,000,000,000,000, 000,000,000,000,000 possible solutions.

Finding the optimal solution becomes even more complex as additional and rapidly-changing factors are added into the mix:

• Emergent jobs come in that must take precedence over those already scheduled
• SLAs and other contractual requirements demand that some jobs be completed within a given timeframe
• Technician skill sets that influence which tech is sent to which job
• Tools and materials currently in stock on each service vehicle
• The current location of a technician in proximity to each job and to drop locations for inventory that may be required for a job
• The duration of each service call, both in terms of estimated time required to complete the call and whether a current job is running over the estimated time, resulting in knock-on effect on subsequent jobs

Former world chess champion Garry Kasparov, in his book Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins, makes clear that even his mind is not capable of computing possible solutions and outcomes as rapidly or effectively as an AI algorithm.

 

"Automating the schedule through AI not only enables a much higher level of service but frees up dispatchers to handle those “beautiful or paradoxical moves” that may delight a customer or solve a tough problem...“

 

The human mind isn’t a computer; it cannot progress in an orderly fashion down a list of candidate moves and rank them by a score down to the hundredth of a pawn the way a chess machine does,” Kasparov writes. “Even the most disciplined human mind wanders in the heat of competition. This is both a weakness and a strength of human cognition. Sometimes these undisciplined wanderings only weaken your analysis. Other times they lead to inspiration, to beautiful or paradoxical moves that were not on your initial list of candidates.”

Automating the schedule through AI not only enables a much higher level of service but frees up dispatchers to handle those “beautiful or paradoxical moves” that may delight a customer or solve a tough problem.

In the end, collaborating with intelligent machines will get us further faster than going it alone. According to Kasparov, the best chess is now played as grandmasters use computers to analyze positions, opponents’ games and their own games—elevating the level of play. In an interview with the Financial Times, Kasparov, who famously had matches against an early chess supercomputer, described how the best chess is now played by combining “human intuition and understanding of the game of chess with a computer’s brute force of calculation and memory.”

“I introduced what is called advanced chess; human plus machine against another human plus machine,” Kasparov said. “A human plus machine will always beat a super machine. The computer will compensate for our human weaknesses and guarantee we are not making mistakes under pressure … the most important thing is not the strengths of the human player. It is not the power of the computer. But it is the interface. It is the corporation.”

 

Legacy Approach to Inventory Logistics

Service management for many businesses relies on inventory … if completion of a service call requires inventory and you are out of stock, you cannot meet your commitment to the customer. When a service request cannot be closed on the first visit, it is often because the right part is not on the truck or immediately available.

So, service management software should encompass inventory management functionality, and that functionality should include automated reorder points for each part. The ability to take parts availability into consideration is a critical data set for AI to work on as parts are a critical determinant in first-time fix and job completion where parts are a factor. It also is a key aspect to successful SLA and outcomes-based commercial relationships.

Once inventory data is available and integrated, a powerful DSE may also be configured to influence inventory logistics so parts and materials are housed in warehouses, satellite offices or inventory drop locations closer to anticipated demand, with inventory matched to jobs in a forward or current day schedule. In one very large implementation of IFS Planning and Scheduling™ Optimization—in the London underground transit system—inventory and tools are dropped ahead of each service visit so technicians who ride the subway to the service site can pick them up.

This is only possible with a high degree of coordination between the service schedule, inventory logistics and an AI-driven scheduling tool.

 

Conclusion

Service organisations should recognise the tremendous potential AI holds—they can harness it to transform their operations, outflank their competitors and disrupt their markets. We are only starting to tap into the different ways AI can be used to better solve the problem of delivering optimal service in a rapidly changing environment as adoption is still lagging despite the real benefits AI brings. The good news is there are several straightforward and easily accessible ways service executives can harness AI technology right now, today.

 

---

Is Artificial Intelligence a key topic for you?! The full white paper on this topic available to fieldservicenews.com subscribers. Click the button below to get fully up to speed!

---

Sponsored by:

---

Data usage note: By accessing this content you consent to the contact details submitted when you registered as a subscriber to fieldservicenews.com to be shared with the listed sponsor of this premium content who may contact you for legitimate business reasons to discuss the content of this content.  

---

 
Be social and share

Artificial Intelligence has increasingly become a key discussion in all industries and its impact in field service management is predicted to be hugely significant, but how should field service organisations leverage this powerful twenty-first-century technology? In the first of a two-part feature, Marne Martin, President Service Management IFS, offers her expert insight... 

 

 

---

Is AI a key topic for you?! There is a full white paper on this topic available to fieldservicenews.com subscribers. Click the button below to get fully up to speed!

---

Sponsored by: 

 

 

---

Data usage note: By accessing this content you consent to the contact details submitted when you registered as a subscriber to fieldservicenews.com to be shared with the listed sponsor of this premium content who may contact you for legitimate business reasons to discuss the content of this content.  

---

 

Artificial Intelligence (AI) will impact every industry and every business discipline—including field service management. But how quickly will practical solutions be available that enable the typical medium to large field service organization to take advantage of AI? And by practical solutions, I mean AI that delivers knowledge efficiently, processes solutions to complex data sets, and automates repetitive activities to allow human workers to focus on personalized service, solving complex problems and escalations, i.e. what people do best.

In some cases, these easily applied solutions are still on their way to market. In three specific areas, however, practical AI applications for field service are already commercially available as proven, commercial off-the-shelf software delivering real business value.

 

AI For Customer Interaction

First impressions matter. And unfortunately, the first interaction a customer has with your service organization often involves several missteps. Chief among these are long wait times on hold due to high call volumes. And then, as a customer attempts to reach out through multiple channels including email, chat and phone, the resulting data stream goes into separate siloes that are disconnected from each other, resulting in disjointed communication.

 

"Today, AI solutions can solve both these problems, but it requires more than “just” chatbots..."

 

Today, AI solutions can solve both these problems, but it requires more than “just” chatbots. Commercially available AI software that ties into chatbots is capable of learning which answers posed in a chat are appropriate for each question and automating a significant majority of chat interactions. A chatbot can be taught to answer commonly encountered questions, like inquiries about when a technician is scheduled to arrive. Of course, at some point, the AI chatbot may get stuck when personalized service is required, and a human agent takes over the discussion thread without missing a beat. This should be seamless not only to the customer but for the internal customer service, ticketing and support systems as well. The chatbot—regardless of whether driven at a given moment by AI or a human agent—should update the same customer record as other channels including social media, phone and email.

And from interactions, the AI functionality learns from answers provided by human agents and gets better and better at answering questions through learning processes. A truly advanced AI chatbot will also seamlessly hand off the chat to a human agent when the extent of its learning is overtaken. Only then can the entire customer experience be unified and consistent, even with a static number of agents handling a rapidly growing fluctuating volume of customer interactions.

AI-based chatbots, for instance, can enable a good agent to handle up to five or more chats at a time. It can capture Facebook messages and tweets and direct them to an agent or to AI for intervention. AI alone can handle, typically, between 50 and 60 percent of requests, freeing up human capacity or lowering staffing levels required to handle a given volume of activity.

 

Enables Management By Exception

In the case of AI applications for the service organization, a primary driver for ROI is that it enables humans to manage by exception. A high volume of activity can be automated, and humans intervene primarily when a situation falls outside the business rules or logic built into service management software. AI doesn’t eliminate the need for human interaction—it makes the human interaction more focused on what humans do best—handle escalations and complex decision making for unique cases.

At one IFS customer, an AI chatbot handles about 50 percent of interactions— primarily those reaching out to cancel their service after a free three-month trial period. Interactions cancelling a free subscription are handled entirely through automation. But if a longer-standing customer is cancelling their service, the interaction gets routed to an agent dedicated to saving the account.

Some interactions are by default easily handled by AI. If 30 percent of inbound contacts are requesting information on the arrival time of a field service technician, it may be possible to automate 90 percent of that 30 percent of contacts. But it is also important to consider the demographics of the customer base. Millennials are more likely to communicate via chat or social media, so if a significant percentage of customers are under 40, heavier reliance on chatbots and AI may help you increase engagement by streamlining your customers’ preferred method of interaction.

 

"Management by exception is also more successful when an AI application has access to extensive information about each customer..."

 

Management by exception is also more successful when an AI application has access to extensive information about each customer. So full integration with enterprise resource planning, field service management and other enterprise tools is essential. AI tools can be more effective if they have more rather than less information on the status of the customer’s account, including their maintenance or service history and warranty or service level agreement entitlements.

Integration between an AI chatbot, email, voice, social and enterprise applications is important for another reason. It enables one version of the customer record. Lacking this, a customer can send an email, and get no response. They send a direct message through Twitter. Then call and sit on hold. Then initiate a chat. All these interactions may not appear in a central customer record, but there have been three attempts to contact the company. Right from the first contact by email, the clock started ticking on a service level agreement.

Full integration can also enable a customer service team, once a customer request is resolved, to close off all queuing activations at the same time for the various contact methods associated with a customer case. Failing this, a service organization may spend a significant amount of time chasing customer requests that have already been resolved.

 

---

Want to know more? There is a full white paper on this topic available to fieldservicenews.com subscribers. Click the button below to access it instantly...

---

Sponsored by: 

 

 

---

Data usage note: By accessing this content you consent to the contact details submitted when you registered as a subscriber to fieldservicenews.com to be shared with the listed sponsor of this premium content who may contact you for legitimate business reasons to discuss the content of this content.  

---

 Be social and share... 

Tieto announced recently that it is one the first Nordic companies to join the European AI Alliance, a newly-formed forum for artificial intelligence (AI) stakeholders to come together to push European competitiveness on AI research and development and its impacts on industry and society.

The AI Alliance, established by the European Commission, brings together a diverse set of leading AI actors, including companies, consumer organizations, trade unions and other representatives of civil society bodies across Europe to share best practices. The AI Alliance aims to directly contribute to the European debate on AI and impact the Commission’s AI policy-making.

To achieve that, the AI Alliance works in close collaboration with the High-Level Expert Group on Artificial Intelligence (AI HLEG), a group the Commission has also established, with 52 members from academia, business and civil society such as Bayer, BMW, Bosch, Fraunhofer Institute, Google, IBM, Nokia, Siemens, Telenor and University of Oxford. The AI HLEG advises the Commission on AI’s opportunities and challenges, and supports it in the implementation of the European strategy on AI. The AI HLEGwill also prepare AI ethics guidelines covering issues such as fairness, safety and transparency as well as the impact on our fundamental rights, including privacy, dignity and consumer protection.

The AI Alliance will complement and support the work of the AI HLEG in particular in preparing AI ethics guidelines and ensuring Europe’s competitiveness in AI. Tieto and other members of the AI Alliance can provide direct feedback on specific questions and draft documents prepared by the AI HLEG.

“As one of the first Nordic companies involved in the AI Alliance, this is a great opportunity for Tieto to facilitate the development of artificial intelligence in Europe. We are excited to join forces with other AI Alliance members to foster AI innovation while also ensuring the highest ethical and sustainability standards in the development of AI. We will work alongside other leading members to build strategies that accelerate AI research and industrial applications,” says Dr. Christian Guttmann, Vice President, Head of Artificial Intelligence and Data Science at Tieto.

“As a Nordic leader in AI, we have already established a Tieto wide AI ethics certificate and are already recruiting new talent in this area, including AI ethics and transparency engineers and AI Trainers to teach our AI systems,” Guttmann continues.

AI and advanced data analytics are an integral part of Tieto’s vision for future growth and success, and Tieto has recently developed several innovative AI and data-driven projects, including the trial with the City of Espoo. Tieto will have six representatives in the AI Alliance, each with deep scientific and industrial experience in AI, including AI’s sub-categories such as machine learning, deep learning, multi-agent systems, knowledge representation, machine perception and AI ethics.

---

 

Be social and share

We are undoubtedly entering a new era of technology, automation and innovation, but in a world of rapid industrial evolution how will regulations also adapt? 

Oliver Rickett, Solicitor, and Caroline Churchill, Partner, at law firm Womble Bond Dickinson  share their insights on this crucial topic... 

---

"Everything is true… everything anybody has ever thought." Philip K. Dick – Do Androids Dream of Electric Sheep

---

It is impossible to escape from the fact that technology, and increasingly artificial intelligence (AI), has transformed everyday life.

It all started with how we play our music, but Apple's Siri and Amazon's Alexa (along with other similar "virtual assistants") now have a daily interface with many of us. We are also, increasingly, now daily users of the Internet of Things (IoT) – connecting up smart fridges, boilers and alarm systems, each controllable from a smartphone. The "everyday" form of AI is almost unavoidable in the modern home, but, while not necessarily as obvious to you and me, there is also an ongoing, yet unseen growth in AI in the manufacturing sector.  What is still lacking, however, is concrete regulation in place for the use and development of AI in the industry.

This article looks at where AI regulation might be implemented and, specifically, what impact both AI has, and its regulation would have, on the manufacturing industry and what role the UK might have in this ever-changing sector.

 

What is "Industry 4.0" and how is AI related?

The term "Industry 4.0" is not a new one.  It relates back to discussions in 2012 of a forthcoming "Fourth Industrial Revolution", the idea being that the current trend of automation would increase, with technology enabling "smart factories". These factories take existing automated assembly line structures and include a cyber element, allowing for the underlying manufacturing machinery to communicate with one another and with the wider factory system as a whole via an IoT setup – increasing efficiency.

Machines with autonomy

The whole process is, and would still be, overseen by a human element, who the machines can also communicate with.  But one of the main goals of Industry 4.0 is to have the machines operating in a decentralised way and with as much autonomy as possible save only where exceptions, interferences or conflicting goals require additional input.

 

How is AI being used currently?

"So far, so sci-fi" you might think, but Industry 4.0 is alive across our manufacturing industry and there are already plenty of examples of manufacturers using this kind of technology across the sector.  Developments are being pioneered by high-end technology companies such as Tesla, Intel and Microsoft on an international scale, some through mere investment or others through actual manufacturing and application.

AI efficiencies and cost savings

Siemens has been using neural networks for a number of years in monitoring the efficiencies of their steel plants.  Siemens is now using this prior experience to make waves in the manufacturing AI sector, using AI to monitor variables (e.g. temperature) on their gas turbines which then adjusts the operation of the machine for increased efficiency and without unwanted by-products.

Others use system masters to spot potential problems and possible solutions, often before a human operator would notice such issues.  The use of this technology has resulted in positive improvements across their smart factories, reducing maintenance costs, as AI can now detect wear on machinery long before it becomes unmanageable.

 

The UK's role in AI and plans for regulation

In the UK, The Manufacturer's Annual Manufacturing Report 2018 conducted a survey on the possibility of a more widespread use of "smart factories" - 92% of senior manufacturing executives believe that digital technologies (including AI) will enable them to increase productivity levels.  Yet, the UK is generally seen as "lagging behind" many other developed countries when it comes to implementing AI in the manufacturing sector.  Is this an example of the UK "traditional mindset"?  With estimates on global turnover of the "smart manufacturing" market soaring to a projected $320bn by 2020 – let's hope not!

Sector-led regulation

Despite technology advancing at a rapid pace, regulation of AI is yet to emerge.  Whilst some commentators have theorised a Skynet-style AI uprising if the sector remains as unregulated as it is today, the UK government has provided a more pragmatic voice. According to the House of Lords Select Committee's report on AI[1], the UK "is in a strong position to be a world leader in the development of artificial intelligence" and with this comes some required element of regulation.

The report "AI in the UK: Ready, Willing and Able?" makes several recommendations as to how the law may need to be updated to account for these new technologies, but also states that "blanket AI-specific regulation, at this stage, would be inappropriate". The Lords instead believe that a sector-specific approach should be taken, with three new governmental organisations (the Centre for Data Ethics and Innovation (CDEI), the AI Council and the Government Office for AI) each taking a lead role in developing regulatory policy going forwards.

Manufacturing sector

For the manufacturing sector, this is expected to cover a number of areas. A key area of focus is likely to be the availability of data access.  AI systems are notoriously expensive and this could clearly impact on the revenues of SMEs struggling to compete financially with international corporations if they were to be further bolstered by AI. A possible solution suggested by the Lords is to implement an "Open Banking" style model where some data can be made public in order to make the sector, as a whole, more competitive.

Safety concerns

Terminator references aside, safety is also one of the primary concerns in this new technology. The current law is a long way from Asimov's "Three Laws of Robotics" and currently fails to address liability issues if, for example, a worker was to be injured by a machine malfunction. As with all policy issues at the moment, the spectre of Brexit looms large and is specifically referred to in the Lords' report as an area of concern since many of the UK's AI initiatives are run jointly with EU counterparts.

Impact on workforce

Finally, aside from direct regulation, businesses across the sector must prepare themselves for potential changes in personnel. Much has been made of how AI will "cost jobs" but the reality is that work in this field is expected to create as many as are lost. It will be more a matter of retraining current staff to deal with the new equipment, and each business will have to assess how much of an impact this will have on their own operation.

AI promotion of innovation and growth

The use of AI in manufacturing will inevitably increase over the short-to-medium term before becoming the "norm" and with an encouraging approach taken by the Lords, aligned with UK Government – who are committed to regulation "that promotes innovation and the growth of new sectors while protecting citizens and the environment", it is likely that we can expect domestic investment in AI as well as the inevitable international investment.

Sustainable regulation required

As with all rapidly growing technologies, the focus should be on sustainable regulation (such as the recent developments in UK law on the use of drones).  However, while the regulatory forecast is still uncertain, what is becoming clear is a real sense of opportunity.

AI - an opportunity for all

With the possibility of the UK being front-and-centre of the new age for manufacturing, there is huge potential for our manufacturing and technology clients:

• those that embrace the change and move towards a technology-focused approach away from the traditional "industrial" style will surely benefit from the efficiencies that come with that change, and
• for the smaller UK SMEs and start-ups out there – dream big!  With the AI sector in such an early stage of development and with many larger corporations lacking the technological know-how (for the time being) to trail-blaze the industry, UK start-ups, with the technological background, have the opportunity to partner or contract with large industrials and have their say on what the future of the manufacturing sector looks like.

Womble Bond Dickinson takes a sector-based approach in all work that we do for our clients and are particularly strong in both manufacturing and technology.  If you have any comments or queries in relation to this article, AI in general, or questions about how we can help your business grow and embrace this new landscape, please get in touch.