Whether consumers know it or not, three next-generation technologies are playing a major role in shaping their experience with brands — and the future of consumer goods marketing: artificial intelligence (AI), machine learning (ML) and Internet of Things (IoT).
To keep pace and effectively compete in an increasingly connected marketplace, brands are investing in these three technologies to continually fine-tune their customer strategies, using hyper-personalized information across touchpoints.
Convergence of Disruptive Technologies
Have you ever wondered how Netflix makes movie and TV show recommendations, how Facebook prompts friends to be tagged in photos, and how Alexa, Siri and Google Now assist in our day-to-day activities? These are real-life examples of machine learning — a subset of AI. ML uses a customer’s historic data and behavioral patterns to create high-quality predictions of their future behavior.
IDC predicts that applications with predictive analytics will grow 65% faster than those that don’t have this functionality, and that by 2018, most consumers will interact with services based on cognitive computing.
Similarly, IoT is disrupting many industrial business processes. IoT refers to everyday “things” equipped with sensors that generate enormous amounts of data based on use and environmental conditions. Enterprises across industries are deploying next-generation business models around the convergence of two or more of these disruptive technologies to segment and analyze the volumes of data they generate to determine what is meaningful.
In light of this, tech giants such as Apple, Amazon, Google, IBM and Facebook are on an acquisition mission to beef up their ML and AI capabilities. For instance, Google has upgraded its image search and recognition capabilities to identify individuals or objects in photos on the Web. Meanwhile, Apple is investing heavily in AI in areas such as self-driving autonomous vehicles, mapping, image recognition and processing, and voice control.
Rethinking the Customer Experience with AI, ML and IoT
ML is already delivering consistent, gratifying customer experiences across digital channels in three key areas, namely sales, marketing and customer service.
1. Improving Sales Productivity
Sale personnel rely on their mobile devices to stay connected to their company and their customers. Given the enormous amount of data generated by various systems across channels, sales leaders are challenged to qualify leads and identify the right opportunities. By applying ML algorithms, forward-thinking businesses are improving their sales forecasts (predicting credit risk, customer churn, etc.), automating account management and lead-identification activities, and uncovering new upsell and cross-sell opportunities.
2. Better Targeting Marketing Campaigns
As more customer information becomes available through big data, ML will become an essential element of customer-focused marketing campaigns. Among the top challenges marketers face include lead generation, ROI measurement and generating personalized offers/messages in real time by utilizing customers’ personal data, demographics, historical purchase patterns and social sentiments, for example.
3. Enhancing Customer Service
Customer service organizations are increasingly incorporating human-assisted virtual agents such as chatbots to route customers to the right agent and improve the overall quality of service. AI technologies such as natural language processing and speech recognition assist live call center agents by looking up relevant information and suggesting appropriate responses. (To learn more, read: “How Machine Learning Can Optimize Customer Support.”) Another AI technology, conversational voice interfaces such as Amazon’s Alexa and Apple’s Siri, provides the ability to conduct a natural conversation with customers (and customer support personnel) and suggest the next best action.
Today’s enterprise systems generate enormous volumes of data that can be fed into AI, ML and IoT technologies to analyze meaningful trends and generate actionable insights. C-level decision makers must understand the important role of this treasure trove of enterprise and customer data in building and maintaining stronger customer relationships, providing hyper-personalized offers and increasing client engagements.
What’s the best way to evaluate where these technologies can be best applied? We recommend:
Identifying repetitive business processes that require a lot of manual intervention, often leading to mistakes in order fulfillment, inventory management, shipping, purchasing and billing. When automated, these tasks are predictable and manageable — freeing human resources to focus on more critical tasks.
Assessing IT back-office systems and batch processing functions, which are good candidates for intelligent automation.
Automating customer service functions for inquiries or tech support with virtual assistants to encourage self-help.
Enhancing business processes with ML algorithms to predict employee/customer churn, track equipment conditions and resolve trouble tickets faster by intelligently routing work to the right agent.
As ML, AI and IoT solutions mature, their impact will be felt in more profound ways across the enterprise. The time is now for companies to weave these disruptive technologies into their strategic agendas to enrich the customer experience, streamline processes, drive profitable business growth and transform the way they operate and serve customers.
“I sat beside a marketer this morning who said he came to mesh because he was interested in the topics, but that he also knew lots about the subject matter so he wasn’t sure how much he’d take away,” said mesh attendee, Sarah Coleman who travelled from Calgary to see mesh in Toronto.
“But after a full day of talks, he said to me that he was totally surprised by the cross-industry perspectives shared, and he walked away from the first day with thoughts he had never considered. For me, that’s the biggest value of mesh and it’s why I travelled across the country for my second mesh conference this year.”
Day two opened up with a frank discussion about the training of artificial intelligence (AI) and data sources with Elena Yunusov, AI strategy and marketing leader with the Human Feedback Foundation.
Yunusov recently started the foundation to crowdsource the human feedback layer that’s missing from private AI models. Private models will continue and make decisions we won’t agree with, she said, but open source initiatives offer the chance for more innovation and better-informed applications.
“We should have more say about how AI is shaped and developed,” said Yunusov.
There are a handful of models influencing us in ways we may not understand. But the Human Feedback Foundation is a small, but mighty open-source project trying to make AI less toxic and more empathetic.
Use human feedback to bring the human voice back into data
After opening remarks, Yunusov continued the AI discussion with Darnel Moore, founder and CEO of Distinct.ly, who sees technology as a tool to connect with people. “We need a way for people to see each other and for businesses to see those people,” said Moore.
Businesses just want to see the data point — not its context. But cognitive bias tells us that time, place, and situation influences people’s decisions, so the data means nothing without context.
Moore said somewhere along the line people became a bug, rather than a feature, for businesses and that needs to change.
“It’s important to get yourself out of the loop of data and buzzwords,” he Moore.
It’s hard when you’re driving hard and fast not to attach yourself to buzzwords. But it’s not about pitching, selling, or moving your product — it’s about connecting with people.
Both Yunusov and Moore expressed puzzlement around the anxiety many people have around AI handling routine tasks.
“Machinery is levelling human beings up from the mundane,” said Moore. People can now be more creative and learn in ways that weren’t possible before, he added.
“We have agency in this and the tools we never had before to get us to the next stages of that journey,” added Yunusov.
We’re living through a bit of a reckoning in tech, she notes. Things are going to change, but how they change should be up to us.
“Change is part of the human experience and we’re just doing it with different tools now,” said Yunusov.
AI is a very divisive concept
Rika Nakazawa, global vice-president with NTT’s New Ventures and Innovation team, joined mesh fresh from COP28’s World Climate Summit in Dubai where there were two camps — one that believed AI is going to be the end of our ability to attain sustainability goals, and the other that thought it would bring the dawn of a new horizon.
Amy Peck, founder and CEO of EndeavorXR, agreed. On one end of the spectrum, it’s the great saviour. We’ll be able to leverage it and achieve all our goals, she said. On the other end is the doom and gloom.
Peck said business leaders need to start understanding data better, urging for bias-free data to be the foundation for AI training algorithms. We’re equal in our humanity, said Peck, so we must learn to embrace our differences rather than vilify them.
“AI is an overnight success, 80 years in the making,” said Nakazawa. “There’s nothing artificial about artificial intelligence.”
It’s all made — binary code is mimicking our brain.
“We have to retrain ourselves to work with AI and not just hand over our tasks to AI,” Peck said.
We needed to manage and prevent food waste
For this event, the mesh conference partnered with Second Harvest to ensure unused food served at lunch would not go to waste. Using Second Harvest technology, unused packaged lunches were donated to a local charity.
“It’s the eHarmony of food,” joked Lori Nikkel, CEO of Second Harvest during a fireside discussion.
Nikkel was joined by Winston Rosser, VP of Food Rescue Operations at Second Harvest, who demoed the technology built to help conquer food insecurity and food redistribution.
Rosser explained that the app connects a variety of donors, from small retailers to major grocery stores, with local, non-profit charities who need food. Before the platform was built, huge trucks were sent to pick up 20 lbs of food from a grocer and take it across the city — an option that was not sustainable. Now, donors can easily connect with one of more than 61,000 charities via the platform.
Rosser also shared some startling stats:
58% of all the food produced in Canada is lost or wasted, mostly ending up in landfill.
3.9 million Canadians are food insecure.
Only 4% of food businesses were donating food.
Since the launch of the app, Second Harvest has flipped everything on its head. In 2016 the organization rescued nine million pounds of food, but after the app was deployed, that number skyrocketed — in 2022, nearly 75 million pounds of food was rescued in 2022. Last year Second Harvest kept food worth $234 million out of landfill.
When asked why there’s so much food waste to begin with, Nikkel offered a sober response: “We don’t value food,” she said, adding that we’ve commoditized food to the point where we don’t value it like we used to. An example: many people will buy food in a two-for-one deal even if they don’t need it, and oftentimes it’s simply thrown out.
Adoption requires sponsorship within the organization
Afternoon discussions on day two of the mesh conference also looked at laggard industries, and professionals who can be resistant to change.
Colleen Pound, founder and CEO of Proxure, and Mary Jane Dykeman, managing partner at INQ Law, talked about the difficult task of integrating AI in law and healthcare — two industries that can be averse to technological innovation.
“Their aversion creates a lot of white space to work in,” said Pound, adding that progress looks like evolution rather than revolution.
Dykeman agreed, adding that change in situations like this often takes a foothold when a series of low-risk initiatives are the starting point. Ultimately, they can lead to larger transformations.
In addition, privacy and data security are major issues for both industries that need to be managed first, Pound said. Data management is the starting point.
“Better data and better processes drive better business outcomes,” Pound said.
AI is what you make it
The day’s closing panel included a conversation on AI in media, featuring mesh co-founder and media pundit, Mathew Ingram.
Ingram joked that he would be terrified if he was starting his journalism career today. As the chief digital writer for the Columbia Journalism Review, Ingram noted that distributing information is easier today, but distributing disinformation is also easier.
“The quality of the disinformation doesn’t matter,” Ingram said, saying people believe disinformation because they want to believe it.
“A nine-year-old could think of a more plausible conspiracy theory than some of the ones I’ve seen people believe,” he said.
Chris Hogg, president and founder of the content marketing firm Digital Journal Group (DJG), said he sees B2B content marketing rolling back to what high-quality journalism used to offer. Hogg said success can now require businesses to produce less content, and instead focus on quality and distribution to stand out and drive results.
The fireside discussion also looked at the risks AI poses to the media industry.
AI may not always be able to make things better, but it has great applications as a technology to support journalists.
“It’s a tool that you can use and do things that help you and are valuable,” said Ingram, noting that transcription, story idea generation, and automating mundane tasks are big benefits offered by AI.
While there are considerable risks with OpenAI’s accuracy, deep fakes, and fake AI content, Ingram said the technology is still important.
“I’m a big believer in the power of individuals to change things,” he said. “There are things we thought would be inconsequential, but have changed the world, for better or worse.”
Join us next year in Calgary for the mesh conference, June 11-12, 2024. The two-day event then returns to Toronto the week of October 21, 2024.
While kicking off day one of the mesh conference, co-producer Chris Hogg remarked that “mesh is about people on the move.”
This is the second mesh conference of 2023 after nearly a decade-long hiatus, with the first occurring in Calgary in April. All mesh events follow the same format: no formal presentations or pay-to-play panels. The result is unique conversations about transformation and innovation with animated, real-time discussions.
New and returning “meshies” filled the space, excited to take part in dialogues about how to interconnect business, media, technology, society, and marketing — and further, change how we think, organize, operate and behave in these spaces.
When digital policy is not revisited for decades, it’s important to prioritize the issues
Geist came to mesh the day after testifying at the Canadian Radio-television and Telecommunications Commission (CRTC) on Bill C-11, which will set out broadcasting policies in Canada. But Canadian news agencies are currently struggling with another barrier — since August 1, 2023, Meta platforms (namely, Facebook and Instagram) have banned sharing news in response to legislation demanding compensation for publishers.
The government thought that Google and Meta were stealing their news, but Geist says that wasn’t the case — it’s users that are sharing links and driving traffic.
“This legislation’s been pretty disastrous,” said Geist.
Now, smaller outlets that rely on their communities are feeling the pain with lost traffic, fewer publishing partners and decreased revenue with some websites even ceasing hiring or shutting down completely because they can’t afford to continue.
“We need to listen to everyone and I don’t think that’s happening,” he said. They were trying to help legacy companies, but they’ve now put independent media at risk.
Geist was also asked if Canada needs to move faster when defining digital policy. He called for more reflection and purposeful-thinking.
“On many of these issues, it is better to get it right than get it fast… There’s no first move advantage here,” he said.
The fact that public safety is at the heart of many of the issues complicates discussions as everyone thinks about how we provide appropriate safeguards, but also preserve the right to free speech. As some policy consultations will continue in 2024, Geist encourages those with something to say not to miss their opportunity to be heard.
“Don’t wait until it’s too late.”
Technology will be at the core of every business going forward
The second panel of the day was the mesh innovation showcase, which featured a mesh moment with recipient Christa Hill (Tacit Edge), Alicia Kalozdi MacMillan (mesh) and Amber Mac (AmberMac Media).
The initiative recognizes innovation and digital transformation leaders from under-represented communities across Canada. People are encouraged to nominate someone they know since they’re much less likely to put their own name forward.
“It’s not just for Apple. It’s not just for Google. It’s for you too,” notes Hill.
If you’re building a product for everyone, everyone should be involved
It’s long been acknowledged there is a gap in the tech industry when it comes to hiring women, particularly in non-entry level roles. So after repeatedly witnessing this shortcoming, Marissa McNeelands decided to co-found Toast, a talent agency focused on matching qualified women with technology positions.
Women make up about 60% of graduates of STEM programs, but only 23% of the STEM workforce in Canada. Moreover, women often leave the tech industry around age 36 because there’s little opportunity for career advancement since they have more difficulty obtaining the requirements for leadership positions.
To combat these issues, McNeelands advocates for mentorship and sponsorship. The burden of mentoring is often put on other women, but it’s just as important for men to mentor their women colleagues and sponsor them for promotions and leadership roles.
The data shows that a more balanced team generates better results as companies can no longer rely on a single line of thought or single experiences. But McNeelands notes, “If you want a more diverse workforce, you have to put work into it.”
AI is a co-pilot
It would be impossible to have a discussion about AI without talking about the risks — but the panel discussion “AI, Creativity, and Inclusivity: Empowering Tomorrow’s Marketing Leaders” was primarily focussed on the benefits and opportunities it presents.
Anne-Marie Enns from Global AI school Mia, hosted a casual conversation with Liberty White, CEO of CHOZEN MEDIA, Natalie Black, Mia, and Prieeyya Kaur Kesh, also of Mia. The panelists discussed applications of AI in marketing and skills training and one thing that was clear by the end: the human element of creation is still irreplaceable.
There are many one-dimensional tasks, noted Kaur Kesh, and AI allows you to be more strategic and bring more humanity to your work versus being process-driven. Even now, as generative AI becomes more creative with image and video generation, the user still has the final call. AI isn’t going to be aware of the cultural nuances you are, so the need for human input is not going away.
“Never use [AI] as a replacement, but as a supplement,” she says.
Echoing this, Black notes the AI reacquaints us with our love of language and pushes the boundaries of human creativity. The brands that apply AI to productivity and efficiency are going to lose the ones who focus on experience, she says.
Everyone now has access to the same design tools, so it’s leveling the playing field. Dismantling the system takes one step at a time, but Black says “you can’t dismantle it if you don’t know how it works.
The people who have been doing this for a long time are now using AI to reach the masses, said White. But there are discrepancies in how the data is being collected and used, so it’s important to look at who is being included. It’s not just about the data, but the data’s context, she says.
Bodnarchuk’s organization surveyed women across Canada and their top priorities are:
Economic prosperity and affordable lifestyle
Climate emissions reductions.
“These are very polarized subjects, but that competitiveness comes right back to how we’re living,” she said. “People are done with the polarization of these issues – they expect government and industry to work together.”
And the fact that they’re not, means we don’t currently have we don’t have the compromise that’s necessary to be competitive.
But things are changing.
“Canadian tech businesses have squared their shoulders and presented themselves globally in a way they haven’t done before,” said Stuart MacDonald. Compared to the 1990s, he said tech businesses are in “a much better position.”
People are no longer moving to Silicon Valley after earning their degrees, but taking advantage of the resources available at home to create a business, make it global and attract talent from all over the world.
This has direct consequences for the Canadian economy. By growing big companies and generating domestic wealth, more taxes can be collected that can help pay for the social system, said O’Born.
Bodnarchuk agrees, noting we have to have a thriving industry to have a thriving social system.
How does bringing back the woolly mammoth help with climate change?
Colossal Biosciences is attempting the seemingly impossible: bringing back the woolly mammoth, the Tasmanian tiger and the dodo bird from extinction.
While this may seem like science fiction, Ben Lamm, Colossal’s co-founder and CEO, says they hope to piece together a de-extinction toolkit that can help conservationists.
By reviving extinct species, Lamm thinks they can help address the world’s current biodiversity crisis. If the crisis is left unaddressed, he says it could could lead to a loss of 50% of the world’s biodiversity by 2050.
The selection of these animals wasn’t arbitrary. They were chosen because they had the right answers to three key questions:
Is it possible to resurrect this species?
Would they serve a purpose in our current environment?
Are their current ecosystems similar to before they went extinct?
Additionally, Lamm notes that they’re charismatic from a story perspective, making it easier to rally public support.
The result will be a more biodiverse ecosystem, while making the innovative technology available to other industries for other applications.
Synthetic biology is “probably the most powerful technology humanity has discovered,” said Lamm. “It’s akin to inventing computers and discovering fire.”
It can be massively applicable to real world problems, but there’s a “responsibility to take the right steps.”
“Lets crawl, walk, run here,”: he said. “Not open Pandora’s box.”
Artificial intelligence (AI) is coming to transform every industry, but advanced data usage in Canada’s health-care system lags well behind other major industries.
However, we still might be sitting on the cusp of a wave of AI-driven health care innovation that could make life simpler for patients and clinicians alike, all while improving overall health care outcomes across the country.
“At a national level, we really have a unique opportunity to apply AI research and solutions to modernize health care, address the challenges we’re facing in our health-care system, and improve the overall quality of care that we provide for patients,” said Azra Dhalla, director of AI implementation at the Vector Institute, a non-profit corporation dedicated to research in the field of AI.
Dhalla works with stakeholders across academia in hospitals and public health agencies on the responsible deployment of AI solutions in clinical environments.
What does AI-driven health care innovation look like?
Dhalla says there are three particular areas worth noting.
“The first is personalized or precision medicine. With the use of AI, it will enable easier and earlier detection of patient health changes and also prediction of disease. Through predictive analytics, we’re able to speed up the diagnosis and decision making capabilities. And that also increases the amount of time physicians get to spend with patients.”
“The second is increasing health system efficiencies to target resources more efficiently, which improves both system performance and patient outcomes.”
“The third is in the area of drug discovery where we can use AI to analyze data and find ways to use existing drugs to treat conditions the drug may not be currently prescribed for, like existing and emerging viruses.”
“I’m excited about what’s coming with AI,” said Mary Jane Dykeman, managing partner at INQ Law, a Toronto health and data law firm. “But there’s the excitement and then there’s rolling up your sleeves and getting to work and getting it done.”
But what does “getting it done” mean, exactly? What are the issues preventing us from realizing AI’s full potential in our health-care system? Or even the benefits of plain old garden-variety digital transformation?
It’s a long list of issues that need to be sorted through – ranging from education to data privacy, data security, and beyond. But it all starts with actually making our health care data accessible. That’s step one – and it’s a big one.
Before you can leverage AI in health care, you have to make the data available
One of the main reasons that the Canadian health-care system hasn’t been digitally transformed is the complexity and location of its data.
Every health-care organization – from clinic to hospital to regional health authority – has massive volumes of data. And that data may be housed in a variety of systems. Some of it is still on paper, some of it is duplicated between paper and computers. Every region is a bit different. So, while there are other issues with digital transformation in health care, data accessibility is the starting point. And as part of that, we need to think about both the patients and the health-care providers that will be accessing that data. It’s a two-sided equation.
“We need to clean up the data so it’s usable and meaningful,” said Dykeman. “And we need to ground it in the patient experience if we’re going to transform the health system across Canada.”
Will Canadians approve of their health care data being used in AI?
The use of AI in health care raises questions for Canadians about how their personal data will be used and kept safe. How comfortable will the average Canadian – especially seniors that make more frequent use of the health-care system – be with their data being used by AI?
Privacy and ethics considerations loom large here.
“Privacy rules have been in place in Canada for many years,” said Dykeman. “Now many governments are modernizing their privacy legislation, because suddenly we have shared systems and electronic records and digital opportunities – and legislation needs to reflect that. Privacy is the bedrock though. It’s not a one-and-done thing. It is a constant commitment. And if we get it right, it opens all kinds of doors for us.”
Dhalla expands on that:
“When it comes to diagnostics, for example, the greater the number of X-Ray images we show an AI and the more diverse the data we show it, the more accurate its diagnosis and predictions will be – so the benefits from accessing this vast amount of data for patients and providers can’t be overstated,” she said.
“But there’s a stringent process as it relates to privacy with respect to health data. And rightly so. As AI becomes progressively more ingrained in our health services, there’s a need to build regulatory frameworks across the industry and governments. In fact, regulators and medical organizations are already developing guardrails to address these issues.”
So, privacy and policy are critical pillars.
But they’re not the only ones.
Canada needs to focus on (digitally) improving the patient & clinician experience
The bottom line is that digital transformation – and AI use – is expanding almost daily. And health care for a child born today will look very different when they’re an adult than it does today. In a good way.
That’s why Dykeman believes we all need to rethink the prevailing narrative around AI, data, privacy, and health care. She believes we need to tell a more compelling, positive story. As she noted to Vog App Developers:
“The public deserves transparency about their data – both the negative and positive stories. They don’t know what is possible, because all they hear is the negative, about the last big data breach. Patient-centric design includes them and can bring to them the same excitement we have about the tremendous opportunities to advance our health system with data. Because these transformations will ultimately help them, and if not them personally, others around them.”
There are two key areas where change management will be key:
Dykeman: “If I’ve been working in health care for years, and I’m quite used to the way I do things, and someone comes along and tells me they’re going to change everything, that’s challenging. But if that change will lead to improvements for patients and clinicians and the family members and caregivers that accompany the patient through the system, that’s different. I can understand that and embrace it.”
Dhalla: “At Vector, we’re helping to work with health-care leaders and clinicians to really change the narrative from fear about AI to how it can really help them augment care. We’re supporting them through this change, providing them with opportunities for learning, knowledge translation, and upskilling.”
Dykeman: “We need to make it so much easier for patients, because there’s a long legacy of them having to repeat the same information at every step of the way in the health care journey. Or perhaps they show up for an appointment and some aspect of their information is lost, and they have to ask them the same questions again and again. That can be changed. It’s also worth noting that a patient may not even be their own best heath historian – depending on the nature of their situation or condition.”
Dhalla: “It’s important we communicate to patients that it’s de-identified data we’re using, so patients know that we don’t actually have access to, for example, their names. In fact, what we’re doing is building more generalizable models that can be used across patient populations. These types of conversations can help alleviate some of the concerns patients are having.”
Building our digital and AI health care future in clinics and medical schools
There’s a whole new generation of health-care workers who will be the tip of the spear when it comes to digital transformation. Universities are recognizing the potential and need to prepare their students for this data-based future by starting to change curriculums. Ultimately, someone studying to be a doctor or nurse in 2030 could find AI as common a tool as a stethoscope. Such is the rate of change.
But Dykeman also believes that big change must start at a smaller level.
“Organizations need to ask themselves: ‘what can we do with this data? What are some of the pain points that patients and clinicians experience each day? What are the use cases that we can develop?’” she said.
“I think there’s a great opportunity to crowdsource ideas even within individual organizations. Ask the people who are inside them. What are the little problems you’d like to solve? What are the big problems you’d like to solve? How do we get there? We have big audacious goals, but small movements will push them forward piece-by-piece. System transformation is not one big thing. It’s a series of little things.”