By: Jagmeet Singh
Blockchain has already disrupted business processes in the financial sector, and is poised to impact companies across industries. Because the technology can provide an immutable digital record of contractual interactions and transactions across an ecosystem, we believe that manufacturing is likely next in line.
Blockchain is a mutually shared ledger of all transactions in a given transactional relationship. Combined with its consensus mechanisms and use of public key infrastructure (PKI) to verify and authenticate all changes made to the ledger, blockchain can enable the network itself to ensure trust among participants. The result: a whole new way to support distributed manufacturing across the value chain.
The Importance of Trust
Consider, for example, the ways in which blockchain can simplify how trust is developed within a manufacturing ecosystem. In the traditional manufacturing world, parties transacting with each other spend considerable time and money on establishing external mechanisms to ensure trust, in the form of contracts, service-level agreements, quality checks, inspections, audits, scanning, escrows and regulatory compliance reviews, to name a few. As the number of parties increases, so does the complexity. Reconciling separate ledgers, enforcing contracts, ensuring supply chain transparency and protecting intellectual property when multiple entities are involved are all laborious and burdensome processes, prone to error and vulnerable to fraud.
Research shows that companies that build a culture of trust can fuel stronger performance by enabling departments to interact better and perform better across multiple dimensions. Establishing trust betweencontracted parties has similar positive effects. All these measures, however, amount to a costly “trust tax.”
For participants in a blockchain network – product designers, production shops, 3-D printers, logistics partners, sales and customer service – that tax is greatly reduced. A secure, distributed ledger infrastructure accessible to multiple parties enables a new level of real-time transparency and efficiency for transactions involving the transfer of anything of value – whether that means ideas, money or ownership.
In our recent global study that included 281 manufacturing professionals, in fact, “trust” was a top driver for blockchain adoption.
Ensuring Transparency, Security, Auditability
Blockchain ledgers are:
- Shared: Separate entities share a common source of truth.
- Distributed: Blockchain relies on peer-to-peer collaboration, with no central ownership.
- Secure: Cryptographic algorithms verify, authenticate and secure transactions.
- Time-sequenced: Data is written consecutively and is time-stamped.
- Immutable: Once written on the blockchain, data cannot be changed, tampered with or deleted.
Through smart contracts with supply chain partners on the blockchain network – programmed agreements that are independently verifiable and automatically executed when predefined conditions are met – companies can minimize human intervention and ensure performance transparency, transaction certainty and auditability.
Within industries and even across interlocked, tiered manufacturing sectors, distributed ledger systems allow companies to develop new, platform-based process flows. A user might execute a smart contract for a custom-configured order, for example, combining designs from multiple sources. The encrypted design data would be recorded on the shared platform; materials and services could be autonomously sourced; and a shared factory could produce the customized product. Payments, including royalties to designers, would be issued when the product is delivered. A record of all transactions, from design selection to payment, remains on the blockchain.
A Rising Tide Lifts All Boats
Blockchain technology thus enables distributed manufacturing, offering participants unprecedented opportunities to develop new product and service lines, create new customer segments, enter new markets and find new ways to use and share assets:
- Through supply chain transparency. All parties transact on a common platform, gaining real-time visibility into processes in the value chain, and simplifying materials sourcing and the interaction of design, manufacturers and other service providers. Supply chain processes, including payments and trade finance, can be streamlined and automated using smart contracts.
- Through digital product memories. Immutable records of asset provenance, materials, production data, ownership and other data ensure authenticity and minimize transaction risk.
- Through secure digital intellectual property. Parties to a transaction can be assured that their intellectual property is protected. Using blockchain to manage a contracted production run from a 3-D printer of ceramic components, for example, would allow a manufacturer to encrypt proprietary 3-D print files from end to end while creating an immutable history of the transaction. Similarly, escrows and royalty accounting would protect designers and other owners of IP.
There are many more circumstances in which adopting blockchain technology can deliver value. Participants can slash inventory costs and service times. They can eliminate reconciliation, and automate and speed financial and process flows. They can reduce manual interventions and reduce fraud. And they can create new ways to extend the lifecycle of products and optimize the use of assets.
What’s Next? Evaluating Readiness
As manufacturers move toward a shared and distributed model, business leaders can consider four questions when evaluating readiness:
- Where in the value chain, internally and externally, are we paying the highest “trust tax” in terms of excess cost, effort or lack of agility?
- How would the availability of a digital product memory drive value for our company, our customers and our business partners?
- Which types of partners, in what geographies and with what expertise, could we work with if transaction costs and efforts were lower?
- Which information assets (e.g., manufacturing, maintenance, operational and usage data) about our products could we monetize if there were a secure way to do so?
A blockchain-enabled, collaborative database is optimal for ensuring agreement between all participants in a value chain. It’s time for manufacturers to examine the implications for their business model. Organizations that gain hands-on experience with blockchain technology thorugh pilot projects will have an advantage as consortia start to form, and will be better equipped to lead the effort and make key decisions around structure and governance, prepare for the corresponding cultural shift, build skills and capabilities, and understand how it will impact business strategy going forward.
Get in the blocks. The race starts now.
Olesya Gorbunova, a Senior Consultant in Cognizant’s Blockchain & Distributed Ledger Practice, contributed to this blog.
This article originally appeared on the Digitally Cognizant Blog
Cognizant (Nasdaq: CTSH) is dedicated to helping the world’s leading companies build stronger businesses — helping them go from doing digital to being digital.
Multimodal AI promises transformative changes to health care delivery
The health-care industry is slowly adopting first-generation AI technologies, but with multimodal AI on the horizon, it’s time to get ready for what’s next
Multimodal AI is the next big thing in health care. Except the previous next big thing — regular ‘garden variety’ unimodal AI — hasn’t even fully arrived yet.
“In health care, AI is a promising technology that has been deployed in very small pockets here and there, but it has not broadly impacted health care in a meaningful way,” said Dr. Amol Verma, a physician, scientist, Assistant Professor in General Internal Medicine at St. Michael’s Hospital and the University of Toronto and the 2023 Temerty Professor of AI Research and Education in Medicine at the University of Toronto.
“The vast majority of clinicians don’t really use AI in their clinical practice.”
That fact doesn’t mean we’re not at the outset of a multimodal AI revolution in health care, with transformative changes including diagnostics, enabling remote care, and driving efficiencies in an industry not exactly known for them.
But before we get too far ahead of ourselves here — what exactly is multimodal AI?
Multimodal AI systems are capable of processing and synthesizing multiple forms of data in order to provide outputs that include decisions, recommendations, and predictions.
Just like people.
“We are multimodal,” said Elham Dolatabadi, a data scientist and Assistant Professor at York University focused on machine learning in health care. “We can smell, see, and hear. These are all different modalities. Our brains integrate them to come to one decision.”
So, where a unimodal AI system in health care might only be able to assess MRI scans, a multimodal AI system could process the information from that MRI scan, along with clinical notes, lab tests, genomic data and real-time patient health tracking information, amongst other inputs. In this way, a multimodal AI system would replicate the way actual doctors process patient health information — by considering and analyzing multiple sources of data.
What multimodal AI could mean for health care
Multimodal AI systems could bring almost unfathomable computational power to health care, especially when it comes to diagnostics and predictive capabilities. This could mean a much more personalized and precise approach to health care for individual patients. Think earlier, more accurate diagnoses and better outcomes — all at scale.
For example, a computer could process radiology images and incorporate that data into its predictions to make them more accurate. It could also incorporate actual conversations between a patient and physician and even assess clinical deterioration from the sound of a patient’s voice.
Multimodal AI could also unlock the potential of telemedicine. Right now, health care providers are limited to assessments based on their conversations and observations over video calls. But if a patient had sensory technology in their home capturing their personal data and feeding it into a multimodal AI system, that could change health care dynamics for millions of patients.
“It could dramatically improve access to care for people who are far away from clinical care or who, for whatever reason, have a hard time getting out of their house,” said Dr. Verma.
Wait times could also be improved. For example, patients regularly face wait times for ultrasound imaging. That’s partly because operating an ultrasound machine is what’s called ‘operator dependent.’ A clinician needs to be properly trained to use it. But AI could actually help patients capture their own images if it was embedded in a device they could use themselves.
“It could change who can deliver health care, not just who receives it,” said Dr. Verma.
Ethical considerations remain central to AI deployment in health care
For many, the widespread use of AI in health care still seems risky. What about privacy? What about the risks of entrusting critical patient care decisions to AI?
Shaina Raza is an Applied Machine Learning Scientist for Responsible AI at the Vector Institute, an independent, not-for-profit focused on AI research. Her work focuses on the ethics of responsible AI in public health.
“With critical decisions about the life or death of a person, the doctors should make those decisions,” said Raza. “It’s not appropriate for generative AI models to do that. But if AI is used to facilitate research or help the doctors’ decisions, that’s different. We can save hundreds or thousands of hours that way.”
Raza notes that ethical AI in health care is ultimately about humans creating the frameworks for the AI before it’s ever fully deployed. That’s how best to address issues like patient privacy and systemic biases.
“Patient privacy is very sensitive. We need to de-identify or mask patient data before feeding it into the AI models.” said Raza. “We can also clean the data for biases before we feed it into the models, with what we call prompt engineering, the instructions we provide to the models.”
Once these sticky issues are addressed, multimodal AI will likely have revolutionary, positive impacts on our health-care system. But since the health-care industry is typically slow to adopt new technologies, that revolution may have to wait a few years.
This might actually be a good thing, according to Dr. Verma.
“Technology is deployed much more widely in the general society than it is in medicine,” said Dr. Verma. “We’re talking about a profession that still frequently uses fax machines. I can do more sophisticated things on my mobile device than I can for applications that are medical-specific. That’s a good thing because the stakes in medicine are very high — literally life and death. We don’t want to be deploying unproven technology so rapidly in that context.”
So while multimodal AI isn’t yet ready for broad deployment, the industry is still preparing for what’s next.
“Our aim is to include as many modalities in the models as possible, including images, text, electronic medical records, wearables, signals like ECG and EEG, and genomics,” said Dolatabadi, who is currently conducting research in multimodal learning with generative AI.
“Then the models could be used for different applications. And once the models are built, hospitals or health-care organizations can fine tune them for their own applications and patient populations.”
How health-care organizations and providers can prepare for the age of multimodal AI
With multimodal AI a matter of when and not if, it’s incumbent upon everyone in the health-care industry to be prepared for the changes ahead.
Dr. Verma outlines four key key areas of focus over the next few years:
- Design the right systems to safely deploy AI technologies. “We need to be designing the right institutions, relationships and incentives. That means creating, effectively, centres of excellence with the infrastructure, data and skilled personnel that can monitor AI technologies. These centres would then connect to primary providers, who you can’t expect to assess whether an AI solution is working. That’s just not feasible or practical for them.”
- Create a plan for collecting critical data to avoid exacerbating system biases. “We know systems are prone to bias, so the second thing that must happen at a system level is creating a plan to collect data about patient race, language and gender, which we currently do not collect. If we don’t collect that information, we won’t know whether solutions are biased and will therefore perform poorly. We could end up exacerbating biases in our health-care system.”
- Building up AI expertise, skills and organizational capacity. “Organizations need to identify AI champions, either through recruitment or upskilling. They need people that have technical skills and skills related to the legality and ethics of AI. And they need people that have skills related to the change management aspects of AI implementation. There’s also the question of scale. Not every organization can do this. Big organizations should scale up and smaller organizations that can’t should partner with big organizations that can.”
- Get educated on AI technologies. “I think at the individual practitioner level, people basically just need to become more aware and educated through professional development. Some basic understanding of these technologies would be good.”
Despite all the complexity with AI, there are options for more people to live longer, healthier lives if multimodal AI is deployed across the health-care system. The fact that health-care organizations are still working to embed unimodal much less multimodal AI doesn’t change the trajectory. In the coming years, almost all of us will find our health care experiences enabled by AI in ways both obvious and hidden. It’s the inevitable next step.
DX Journal covers the impact of digital transformation (DX) initiatives worldwide across multiple industries.
New IT infrastructure for Gilbane includes LLMs, payment automation, and unified tech departments
A look at 153-year old real estate titan Gilbane’s digital transformation
Canada’s construction industry has been slower to get on the digital transformation train. Yet virtual design and automation presents serious opportunities for the industry to evolve with digital transformation.
Research shows that more than 80% of construction companies have room to improve their data capabilities, and the most commonly cited benefits of digital transformation were increased productivity, customer experience and staff safety.
One company that made it happen is Gilbane — a U.S.-based real estate development company worth more than $6.5 billion, with family roots that persevere to this day. In fact, they even have employees in the family’s sixth generation. Despite the “legacy” mindset in the company’s fabric (it’s 153 years old) and business industry, Gilbane boosted risk management and productivity under a brand new modular IT infrastructure.
Here’s how they did it (and how you can, too):
Unify all digital initiatives under one leader
The company created a position that was responsible for overseeing the progress on multiple tech initiatives, including AI, analytics, IT, and cybersecurity. This helped them avoid the risk of wasted budgets through silos and miscommunication.
“We believe this structure is the most effective to bring together our data and technology resources to drive transformation and get a real return on invested capital.” — Karen Higgins-Carter, Chief Digital Information Officer (CDIO)
Invest in analytics and AI for risk management
Safety is a primary concern in the construction industry. Despite improvements in safety measures, equipment, and training, the construction industry still experiences high rates of death and injury. In fact, in 2022 the National Safety Council ranked the construction industry in the top four most dangerous, noting that it experienced the most workplace deaths.
Gilbane’s team is investing in analytics and AI with large language mode experiments to help them identify similar trends that indicate potential unsafe characteristics on a worksite, Higgins-Carter told CIO. “In construction, our teams are managing the construction of hundreds of projects happening at any one time,” she said. “Our analytics capabilities identify potentially unsafe conditions so we can manage projects more safely and mitigate risks.”
“To help us manage risk, I need to understand the leading indicators of risk on a job, like attrition or high volumes of change order.” — Karen Higgins-Carter, Chief Digital Information Officer (CDIO)
Automate payment processing with operation-specific triggers
Higgins-Carter told CIO the company recently piloted an automated payment program for Gilbane to pay subcontractors more efficiently. Powered by videos and photos of work completed as triggers, payments are automatically dispensed to the necessary parties.
Educate the entire team and inform new processes with their experience
Hold meetings and training sessions to ensure executives and employees understand the benefits and functions of any new tech or business processes.
“We can’t deliver technology if we don’t understand our employees’ experience. If I go out to a job site once a month, then my team will too.” — Karen Higgins-Carter, Chief Digital Information Officer (CDIO)
Read the full article on CIO here.
Veronica Ott is a freelance writer and digital marketer with a specialization in finance and business. As a CPA with experience in the industry, she’s able to provide unique insight into various monetary, financial and economic topics. When Veronica isn’t writing, you can find her watching the latest films!
The Northern Lights Technology & Innovation Forum navigates AI, economic concerns and upskilling in Alberta
Panelists dive into how innovation and collaboration may help navigate the changing industry landscapes
While rapid advancements in AI are reshaping industries worldwide, they’ve sparked discussions about innovation and community resilience through ongoing economic challenges. At this year’s Northern Lights Technology and Innovation Forum, panelists explored how technology could drive positive adaptation.
- Mark Little, co-founder and CEO Jotson Inc, and board member of General Fusion
- Anna Baird, culture and innovation evangelist at Google
- Dan Semmens, SVP and head of data and IT at ATB Financial
- Arthur Kent, Canadian journalist and author
- Joy Romero, executive advisor innovation at Canadian Natural Resources Limited (CNRL)
Approximately 250 attendees gathered for the forum at the Calgary Petroleum Club on Feb. 8. Filled with industry leaders and burgeoning entrepreneurs, the forum focused on collaboration and knowledge sharing in the tech sector.
Over the past five years, Calgary has seen a 22 per cent increase in tech talent and total tech jobs, emerging as one of North America’s top markets for young tech professionals.
“The talent pool here is amazing,” said John Givens, vice president of sales at C3 AI and one of the event’s organizers. “So how do we leverage our talent here? How do we share that knowledge?”
In response, this year’s forum included the inaugural “Mentors and Makers” initiative, where a dozen industry experts pinned green buttons to their lapels, signaling to anyone in the crowd that they’re open to a conversation.
Shawn Mahoney, another event organizer and co-founder of Spare Parts & Gasoline, said in his opening remarks that the initiative speaks to “creating the new innovators that we need to solve tomorrow’s problems.”
And with that, the panel took the stage to dig into the big questions: What are the challenges and opportunities for Alberta as a growing tech market? How will AI continue to change industries across the board? And if it does, will that be a bad thing?
The Alberta advantage
The panel conversation was kicked off by the first question asked by moderator Geraldine Anderson: “What is the Alberta mindset, or the ‘Alberta advantage?’”
Mark Little, co-founder and CEO Jotson Inc, said Alberta has a lot going for it — including having the highest GDP in Canada, a younger population, and high education levels — but those aren’t the advantages that stand out to him.
“There’s a resilience and an entrepreneurial spirit here,” he said. “As a result of that, we’re seeing innovation … I think 10 to 15 years from now we’re going to lead this country in innovation and it’ll be every sector you could imagine.”
Hailing from Vancouver and the only panelist not based in Calgary, Google’s Anna Baird said she considers herself an honorary Albertan based on the “sheer grittiness and roll up your sleeves and work together” attitude she’s witnessed.
“The grittiness takes us into innovation,” said Baird. “We’re willing to try new things, we’re willing to fail — hopefully fast and cheaply, as is Google’s ethos. But we’re also willing to borrow with pride and give kudos to the people we’re borrowing the pride from so we can have building blocks.”
The panelists’ discussion kept coming back to the importance of adaptability, innovation, and collaboration. While the province faces significant hurdles, including global market fluctuations and environmental concerns, they spoke with optimism about the potential to emerge stronger by investing in the future.
Dan Semmens, SVP and head of data and IT at ATB Financial, calls it an “opportunity” for both the province and country to focus on investing in the next generation.
“I think the opportunity there is continuing to invest in our most precious resource, which is our young people,” he said.
When it comes to AI, “it’s on all of us” to level up our own skills
AI is already impacting most industries globally, and it shows no signs of slowing down. But it’s not new either.
Joy Romero, executive advisor of innovation at Canadian Natural Resources Limited (CNRL), said she was using AI neural networks 20 years ago to take ecological data and process it through oil sands facilities.
“Why?” she asked. “Because that would allow us to improve our processing and our productivity … So for me, digital is our world. That’s productivity.”
The day of the panel, Google announced that Gemini Ultra 1.0, the largest version of their large language model, is being released to the public.
Baird was asked about the implications of the new AI model, and while she acknowledged there will be challenges, she maintained that “the train has left the station.”
“It’s on all of us here in the room to level up our own skills,” she says. “With an announcement like Gemini, like you have to get in there, you have to play, you have to try.”
Transitioning to the realm of media and journalism, Canadian journalist Arthur Kent highlighted the increasing role of AI in newsrooms. From assisting journalists in gathering and analyzing data to content creation, journalists are experimenting with AI for efficiency and detecting false information.
“We can become even better if we harness artificial intelligence to do that,” said Kent. “So we constantly have to be developing and pushing ourselves forward, to keep pace with this.”
However, he emphasized the critical role of journalists in maintaining integrity and discerning between fact and fiction in an era of AI-generated content.
“Journalism is always going to be a human process, because journalism is based on their location, and verification, verifying leads, tips, and figuring out rumour from fact,” said Kent. “So far, none of the machines that I’ve seen associated with artificial intelligence, have those human characteristics. However, there is also that human aspect called temptation.”
In the financial services industry, Semmens said the impact of generative AI “poses an existential risk” to the relationship banks have with their clients.
Despite this, he says incorporating AI technology into banking is “an incredible opportunity” to personalize experiences for customers more effectively and efficiently, and he expects to see a lot of changes in open banking in the next three to five years.
“With all the disinformation that is out there, a trusted source is going to be a high commodity,” he said. “And so I think in banking, being a heavily regulated industry, there is an opportunity for us to really show up from that standpoint.”
An innovation forum’s charitable roots
The Northern Lights Technology and Innovation Forum’s story begins over a decade ago. The organizers, including Givens, first banded together for the Gordie Howe C.A.R.E.S. Hockey Pro-Am Tournament in support of Alzheimer’s research and education.
As the cause drew more attention they opted to expand the tournament into the forum as a way to expand their reach. All of the event proceeds go to Gordie Howe C.A.R.E.S. Centre for the Alzheimer’s Research and Education Society — and this year they broke their record, raising a minimum of $40,000 thanks in part to a funding match made by Google.
“It’s amazing,” Givens said at the end of the night. “I always knew the potential of our community. And I explained to people that the community is the draw … It’s about education. It’s about doing the right thing. It’s about just finding ways for other people to get involved in doing the same thing. There’s enough energy there. Now we just have to harness it.”
DX Journal is an official media partner of the Northern Lights Technology and Innovation Forum.
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