How to Get the Early Adopter Edge
By Timo Elliott and Lauren Gibbons Paul
Healthcare system Mass General Brigham spent about $2.5 billion on research last year. Chief innovation officer Christopher Coburn heads an innovation team of 130 professionals who help turn that research into better health outcomes. To do so, Coburn’s group works closely with the organization’s chief digital officer, Jane Moran, to support the evaluation of “many dozens of technologies at the enterprise and pilot stage,” Coburn says.
Health services are, to understate the case, highly regulated. Lives are on the line. Can such an organization be an early adopter of new technologies, given all the uncertainties they entail? Coburn says it’s not only possible but necessary.
“Discovery, innovation, and application are at the core of modern healthcare. Innovation cannot advance without risk,” he says. “That imperative is always balanced with the need to identify, manage, and minimize the risk.”
Sometimes early technology adopters seem both brave and foolish. Do it right, and you may get a leg up on your competitors. Do it wrong, and you burn time and money for no gain (or sometimes worse).
Recent Accenture research estimated that 12% of firms that are mature in their use of AI – arguably the hottest new technology category around – enjoy 50% greater revenue growth on average compared with their peers. And companies that hesitate are simply taking a different kind of risk. After all, for every Amazon or Netflix, there’s a Toys R Us or Blockbuster Video in the corporate graveyard.
Not every early tech adoption decision is an existential issue. Still, the unpleasant possibilities are legion: the emergent technology itself could run into insurmountable problems, prove to be of limited business value, or fail to be embraced by business users. It could simply be surpassed by the rise of an easier solution. Or a technology that looks good in pilot could prove impossible to scale or integrate with other enterprise systems.
The truth is that successful early adopters aren’t just thrill-seekers, willing to laugh at these dangers and roll the dice. As Coburn indicates, it’s about knowing how to approach nascent tech projects the right way, to minimize the risk and maximize the reward. Here, experts and practitioners show the way.
The motives that matter
In every company, some individuals are more prone to new tech tinkering than others; early adopter organizations encourage and depend on that impulse. As with any technology project, the key for piloting new tech is to stay firmly grounded in a real business problem, says Scott Likens, new technology and innovation leader at PwC. “Experimenting with emerging technologies just for the benefit of potential innovation is not the right approach,” he says. “We’re looking at emerging technology as a way to accelerate or enhance the delivery of benefits for really well-known business use cases.” These include things like automating the handling of invoices and process paperwork – “boring AI,” if you will. They’re not the super-splashy plays you see from the biggest names in the business, but Liken says they deliver real value and generate less risk.
That’s a time-worn lesson that technologists, whether enthusiasts or professionals, occasionally need to learn again.
But the biggest successes start with a motivation to solve not just any business problems but problems for customers.
Today’s early adopters are using technologies such as machine learning, advanced robotics, virtual reality, drones, and 3D printing with the hopes of making a big difference for their customers. Improving customer outcomes is the surest path to beating your competition, and that’s the motivation to encourage.
“The imperative is driven by a need to shift business models and improve profitability more than anything else, either through product enhancement, efficiency, or being able to support customers in a rapidly changing world,” says Stuart Carlaw, chief research officer for ABI Research. “They need to do things differently,” he adds – and new technology presents a way to do just that.
Coburn’s innovation squad underscores the point. “The overriding mission of our team is to benefit patients by accelerating new technologies and care tools that will help them,” Coburn says. That customer-minded mission also helps ensure that risks are carefully calibrated and balanced with controls. “Providers at all levels work within a highly organized system to ensure that balance is always present, and the interest of patients always overrides all others,” he says.
Early technology adoption: Five strategies to get the payoff
Some companies see the risks and decide not to try. Others try but dabble, fail to plan properly, get poor results, and then pull back. For those who seek the rewards, here are five ways to get out of that dilettante ditch:
1. Forget “fail fast.” Build a “learn fast” culture.
People will not experiment if they don’t feel safe and like they belong. It’s a matter of the triune structure of the human brain, says Sunnie Giles, partner at venture capital firm Lane and author of The New Science of Radical Innovation: The Six Competencies Leaders Need to Win in a Complex World. Psychological safety is the most basic need, at the level of the reptilian brain. In a corporate environment, this translates into not being afraid of losing one’s job. The next level, limbic, governs emotions and the need to be part of something larger than oneself. Everyone’s perspective is welcomed, and feedback is not allowed to devolve into attacks. Finally, at the third level, neocortex, higher levels of thought and collaboration are possible; this is where real innovation occurs.
There is no way to shortcut to the cortex level of functioning without covering the first two basic needs, says Giles.
“Our brain has been fine-tuned over several millennia to pick up any signs of threat. That’s how we’ve survived as a species,” she says. One research study found that people can distinguish – by smelling socks – the difference between sweat generated from exercise and sweat that came from fear. “When we don’t have a psychologically safe environment in an organization, people are going to be busy just covering their butts instead of truly learning from experimenting,” says Giles.
There’s a difference between a failure of performance and one that’s a consequence of learning, says Andrew Binns, director with Change Logic and author of the 2022 book Corporate Explorer: How Corporations Beat Startups at the Innovation Game. “Explorers need to fail frequently in order to learn, and they need space for that,” he says. But they also need to be willing to pull back as soon as it becomes clear the project is a nonstarter – more on this point about responding to evidence below.
“The word ‘failure’ is really not helpful,” says Binns. “It’s about ‘How have you proven or disproven your theory about what your customers wanted and what they would pay for?’ And that has to be driven by evidence, which results from experimenting.”
Likens proposes that the operative phrase should be “learn fast – people are happy that it’s not about failure.”
2. Give explorers autonomy and the right objectives.
Experts agree: organizationally, intrepid new technology experimenters who are focused on customer problems and adept at handling novel technologies are important to achieving successful early adoption. These explorers need their own separate units and practices – even different compensation structures than the rest of the company. Experimenting with new technologies is very different from implementing or exploiting them at scale, which is done by the core business, according to Giles.
“Those two functions inherently conflict with each other, so they have to be under a different governance and compensation structure – almost to have two separate companies,” says Giles.
The experimenters – Binns calls them “corporate explorers” – might be given incentives, such as a piece of the revenue of any new product that comes out of the group. Giles says this kind of structure can and often does lead to jealousy on the part of the “regular” employees, but their careers are generally more secure than these mad scientists. Greater risk, greater potential reward. It helps to have visibility into what the explorers are trying to achieve and how their work is measured. There are metrics, but they are innovation metrics – such as number of ideas in development, test plan milestones reached, customer reactions to prototypes – that may differ, or be weighted differently, from the measurements used for the rest of the organization.
“The core business is about driving operational performance, whereas exploration is about managing uncertainty,” says Binns. “These two things need to coexist, but they can’t run in the same unit very easily because their objectives are pretty different and their operating practices are very different.”
An extreme example: when Matt Price, CEO of digital security company Cloudflare, wants a new innovation for his business, he not only sets up a separate unit – he puts it in a different city and tells its members to do things on their own and steer clear of the rest of the company. Price believes this rather draconian method maximizes speed.
Most companies don’t go quite so far. “That works provided the explorers don’t need to leverage assets like customers or data from the core business,” says Binns. More often, they do need to share resources with the main business, so a bit more collaboration with colleagues goes a long way in setting the stage to scale innovations later.
Indeed, researchers who have studied early adopters (both companies and individuals) in the wake of American social scientist Everett Rogers’s pioneering work in the 1960s have discovered that early adopters have solid connections both inside and outside the organization. Their conclusions were remarkably consistent, even as their research subjects varied widely, according to Alexander Harrowell, principal analyst, advanced computing at Omdia. These early, early adopters – Rogers called them “innovators” – had connections to both outsiders and insiders.
“They can pick up new ideas and new practices from outsiders and disseminate them to the insiders in the mainstream,” says Harrowell.
Meanwhile, productive early adopter organizations don’t hamper their experimenters by insisting they follow corporate norms, such as the need to prove hard cost savings before a project can proceed. When the goal is tapping new technology for innovation rather than increased efficiency, the traditional rules of building a business case do not – and should not – apply.
3. Steer clear of proof-of-concept purgatory.
Science projects are easy; implementation and scaling up are hard. Organizations that fall in the middle of the adopter pack are prone to getting bogged down when trying out new technologies.
“You'll see companies dipping into different technologies and very limited proof of concepts (POCs) to see if there is a meaningful impact on their business. But there is often so much held up in the POC process that very little was translating into meaningful, agile project initiation that was moving the needle for companies,” says Carlaw.
Very often, this happens when there is “technology for technology’s sake” that is disconnected from corporate strategy. “The CEO goes, ‘I need a story around AI; tell me that we're doing it.’ You put a [proof of concept] in place; that's the story around AI. You make a big blow of it, and it goes nowhere,” says Carlaw.
This goes back to the point about focusing on customers and real business problems. “The customer pain point has to be the starting point for any use of technology. That’s a design principle,” says Giles. “It’s completely backwards to start with a technology like AI just because it’s getting popular.”
“Pilot purgatory happens when no one knows what all the little projects add up to. You’re not sure why you’re making these investments; they’re more of a curiosity than a commitment,” says Binns. So the POCs fail because they’re too small to matter. “They’re disconnected from a strategy. And therefore, ultimately, they stand all alone.”
The agile early adopters strive for a clear view of the dramatic process improvements and customer breakthroughs that are possible with a new technology, so they are much less likely to get hung up in project land, says Carlaw.
Another key to not getting stuck: putting in place a multifunctional team that understands the implications for each group that will be affected if the project were to scale up. What would it mean for production, for the warehouse, for customer experience, for aftermarket sales? It all needs to be thought of at the beginning, according to Likens, who says, “You don’t really want to do things that will not be used at scale.” That means front-loading the implementation framework into your experiments, a step that is easily overlooked.
4. Go where the evidence leads.
The heart of experimentation with new technology is to test theories about how you could use it to better serve customers. Every experiment begins with optimism but leads to evidence – which drives rational decisions about how to deploy a technology or not.
“You use evidence to go in a particular direction because the biggest pitfall for the early adopter is getting starry-eyed about the possibilities of a technology without really knowing whether it’s needed in the world,” says Binns.
The big failures happen when people make leaps of faith and commit themselves to bold investments but lack any evidence to support them, says Binns. This can happen with small explorations, but the rise and fall of the GE Digital business unit made this point in more spectacular fashion. GE CEO Jeff Immelt created the unit in 2015 because he felt he needed to take dramatic action to shock the then-125-year-old industrial giant into adopting digital modes of operation. However, neither the technology nor the manufacturing market were ready for the all-encompassing “industrial Internet” GE Digital proposed to build. GE spent too much too soon, and what remains of the unit today is dramatically scaled back.
“There is a psychology that goes, ‘My organization is inert and won't move. And I'm going to force it to move by making a major commitment.’ Unfortunately, that sounds big and brave and bold, but because it’s lacking evidence, it just creates a big hole,” says Binns.
5. Experiment and collaborate with customers.
To improve the customer focus motivation further, many early adopter companies – particularly technology vendors – invest in space for their customers to collaborate and jointly see what’s possible with new tech.
For example, chip maker Analog Devices’ ADI Catalyst unit, based in Ireland, works with different customers to explore the potential for robotics, AI, sensors, and other connected technology. “They are playing with possibilities with customers – sometimes three or four different companies on the same project. So this is a really exciting way of finding out how technologies could solve problems that matter,” says Binns.
The advantage of this joint approach for the customers is clear, as having a big vendor’s backing and experience can increase the odds of new technology finding its footing. SAP illustrates this idea by working in both directions, collaborating with both tech startups and customers through the SAP.io program.
Innovation is always exciting, but early adopters recognize that methodical work underlies results. At Mass General Brigham, Coburn describes his innovation team as “part of a rigorous process that coordinates the assessment of new technology, co-development, potential scale-up and integration, and addressing unmet needs/areas of opportunity.”
“We have a coordinated set of resources that help us test, develop, and accelerate research on emerging digital technologies,” Coburn says. By combining determined customer focus with good risk management processes in a similar way, any company can support consistent innovation with new technologies – the early adopter edge.
By Timo Elliott and Lauren Gibbons Paul
Healthcare system Mass General Brigham spent about $2.5 billion on research last year. Chief innovation officer Christopher Coburn heads an innovation team of 130 professionals who help turn that research into better health outcomes. To do so, Coburn’s group works closely with the organization’s chief digital officer, Jane Moran, to support the evaluation of “many dozens of technologies at the enterprise and pilot stage,” Coburn says.
Health services are, to understate the case, highly regulated. Lives are on the line. Can such an organization be an early adopter of new technologies, given all the uncertainties they entail? Coburn says it’s not only possible but necessary.
“Discovery, innovation, and application are at the core of modern healthcare. Innovation cannot advance without risk,” he says. “That imperative is always balanced with the need to identify, manage, and minimize the risk.”
Sometimes early technology adopters seem both brave and foolish. Do it right, and you may get a leg up on your competitors. Do it wrong, and you burn time and money for no gain (or sometimes worse).
Recent Accenture research estimated that 12% of firms that are mature in their use of AI – arguably the hottest new technology category around – enjoy 50% greater revenue growth on average compared with their peers. And companies that hesitate are simply taking a different kind of risk. After all, for every Amazon or Netflix, there’s a Toys R Us or Blockbuster Video in the corporate graveyard.
Not every early tech adoption decision is an existential issue. Still, the unpleasant possibilities are legion: the emergent technology itself could run into insurmountable problems, prove to be of limited business value, or fail to be embraced by business users. It could simply be surpassed by the rise of an easier solution. Or a technology that looks good in pilot could prove impossible to scale or integrate with other enterprise systems.
The truth is that successful early adopters aren’t just thrill-seekers, willing to laugh at these dangers and roll the dice. As Coburn indicates, it’s about knowing how to approach nascent tech projects the right way, to minimize the risk and maximize the reward. Here, experts and practitioners show the way.
The motives that matter
In every company, some individuals are more prone to new tech tinkering than others; early adopter organizations encourage and depend on that impulse. As with any technology project, the key for piloting new tech is to stay firmly grounded in a real business problem, says Scott Likens, new technology and innovation leader at PwC. “Experimenting with emerging technologies just for the benefit of potential innovation is not the right approach,” he says. “We’re looking at emerging technology as a way to accelerate or enhance the delivery of benefits for really well-known business use cases.” These include things like automating the handling of invoices and process paperwork – “boring AI,” if you will. They’re not the super-splashy plays you see from the biggest names in the business, but Liken says they deliver real value and generate less risk.
That’s a time-worn lesson that technologists, whether enthusiasts or professionals, occasionally need to learn again.
But the biggest successes start with a motivation to solve not just any business problems but problems for customers.
Today’s early adopters are using technologies such as machine learning, advanced robotics, virtual reality, drones, and 3D printing with the hopes of making a big difference for their customers. Improving customer outcomes is the surest path to beating your competition, and that’s the motivation to encourage.
“The imperative is driven by a need to shift business models and improve profitability more than anything else, either through product enhancement, efficiency, or being able to support customers in a rapidly changing world,” says Stuart Carlaw, chief research officer for ABI Research. “They need to do things differently,” he adds – and new technology presents a way to do just that.
Coburn’s innovation squad underscores the point. “The overriding mission of our team is to benefit patients by accelerating new technologies and care tools that will help them,” Coburn says. That customer-minded mission also helps ensure that risks are carefully calibrated and balanced with controls. “Providers at all levels work within a highly organized system to ensure that balance is always present, and the interest of patients always overrides all others,” he says.
Early technology adoption: Five strategies to get the payoff
Some companies see the risks and decide not to try. Others try but dabble, fail to plan properly, get poor results, and then pull back. For those who seek the rewards, here are five ways to get out of that dilettante ditch:
1. Forget “fail fast.” Build a “learn fast” culture.
People will not experiment if they don’t feel safe and like they belong. It’s a matter of the triune structure of the human brain, says Sunnie Giles, partner at venture capital firm Lane and author of The New Science of Radical Innovation: The Six Competencies Leaders Need to Win in a Complex World. Psychological safety is the most basic need, at the level of the reptilian brain. In a corporate environment, this translates into not being afraid of losing one’s job. The next level, limbic, governs emotions and the need to be part of something larger than oneself. Everyone’s perspective is welcomed, and feedback is not allowed to devolve into attacks. Finally, at the third level, neocortex, higher levels of thought and collaboration are possible; this is where real innovation occurs.
There is no way to shortcut to the cortex level of functioning without covering the first two basic needs, says Giles.
“Our brain has been fine-tuned over several millennia to pick up any signs of threat. That’s how we’ve survived as a species,” she says. One research study found that people can distinguish – by smelling socks – the difference between sweat generated from exercise and sweat that came from fear. “When we don’t have a psychologically safe environment in an organization, people are going to be busy just covering their butts instead of truly learning from experimenting,” says Giles.
There’s a difference between a failure of performance and one that’s a consequence of learning, says Andrew Binns, director with Change Logic and author of the 2022 book Corporate Explorer: How Corporations Beat Startups at the Innovation Game. “Explorers need to fail frequently in order to learn, and they need space for that,” he says. But they also need to be willing to pull back as soon as it becomes clear the project is a nonstarter – more on this point about responding to evidence below.
“The word ‘failure’ is really not helpful,” says Binns. “It’s about ‘How have you proven or disproven your theory about what your customers wanted and what they would pay for?’ And that has to be driven by evidence, which results from experimenting.”
Likens proposes that the operative phrase should be “learn fast – people are happy that it’s not about failure.”
2. Give explorers autonomy and the right objectives.
Experts agree: organizationally, intrepid new technology experimenters who are focused on customer problems and adept at handling novel technologies are important to achieving successful early adoption. These explorers need their own separate units and practices – even different compensation structures than the rest of the company. Experimenting with new technologies is very different from implementing or exploiting them at scale, which is done by the core business, according to Giles.
“Those two functions inherently conflict with each other, so they have to be under a different governance and compensation structure – almost to have two separate companies,” says Giles.
The experimenters – Binns calls them “corporate explorers” – might be given incentives, such as a piece of the revenue of any new product that comes out of the group. Giles says this kind of structure can and often does lead to jealousy on the part of the “regular” employees, but their careers are generally more secure than these mad scientists. Greater risk, greater potential reward. It helps to have visibility into what the explorers are trying to achieve and how their work is measured. There are metrics, but they are innovation metrics – such as number of ideas in development, test plan milestones reached, customer reactions to prototypes – that may differ, or be weighted differently, from the measurements used for the rest of the organization.
“The core business is about driving operational performance, whereas exploration is about managing uncertainty,” says Binns. “These two things need to coexist, but they can’t run in the same unit very easily because their objectives are pretty different and their operating practices are very different.”
An extreme example: when Matt Price, CEO of digital security company Cloudflare, wants a new innovation for his business, he not only sets up a separate unit – he puts it in a different city and tells its members to do things on their own and steer clear of the rest of the company. Price believes this rather draconian method maximizes speed.
Most companies don’t go quite so far. “That works provided the explorers don’t need to leverage assets like customers or data from the core business,” says Binns. More often, they do need to share resources with the main business, so a bit more collaboration with colleagues goes a long way in setting the stage to scale innovations later.
Indeed, researchers who have studied early adopters (both companies and individuals) in the wake of American social scientist Everett Rogers’s pioneering work in the 1960s have discovered that early adopters have solid connections both inside and outside the organization. Their conclusions were remarkably consistent, even as their research subjects varied widely, according to Alexander Harrowell, principal analyst, advanced computing at Omdia. These early, early adopters – Rogers called them “innovators” – had connections to both outsiders and insiders.
“They can pick up new ideas and new practices from outsiders and disseminate them to the insiders in the mainstream,” says Harrowell.
Meanwhile, productive early adopter organizations don’t hamper their experimenters by insisting they follow corporate norms, such as the need to prove hard cost savings before a project can proceed. When the goal is tapping new technology for innovation rather than increased efficiency, the traditional rules of building a business case do not – and should not – apply.
3. Steer clear of proof-of-concept purgatory.
Science projects are easy; implementation and scaling up are hard. Organizations that fall in the middle of the adopter pack are prone to getting bogged down when trying out new technologies.
“You'll see companies dipping into different technologies and very limited proof of concepts (POCs) to see if there is a meaningful impact on their business. But there is often so much held up in the POC process that very little was translating into meaningful, agile project initiation that was moving the needle for companies,” says Carlaw.
Very often, this happens when there is “technology for technology’s sake” that is disconnected from corporate strategy. “The CEO goes, ‘I need a story around AI; tell me that we're doing it.’ You put a [proof of concept] in place; that's the story around AI. You make a big blow of it, and it goes nowhere,” says Carlaw.
This goes back to the point about focusing on customers and real business problems. “The customer pain point has to be the starting point for any use of technology. That’s a design principle,” says Giles. “It’s completely backwards to start with a technology like AI just because it’s getting popular.”
“Pilot purgatory happens when no one knows what all the little projects add up to. You’re not sure why you’re making these investments; they’re more of a curiosity than a commitment,” says Binns. So the POCs fail because they’re too small to matter. “They’re disconnected from a strategy. And therefore, ultimately, they stand all alone.”
The agile early adopters strive for a clear view of the dramatic process improvements and customer breakthroughs that are possible with a new technology, so they are much less likely to get hung up in project land, says Carlaw.
Another key to not getting stuck: putting in place a multifunctional team that understands the implications for each group that will be affected if the project were to scale up. What would it mean for production, for the warehouse, for customer experience, for aftermarket sales? It all needs to be thought of at the beginning, according to Likens, who says, “You don’t really want to do things that will not be used at scale.” That means front-loading the implementation framework into your experiments, a step that is easily overlooked.
4. Go where the evidence leads.
The heart of experimentation with new technology is to test theories about how you could use it to better serve customers. Every experiment begins with optimism but leads to evidence – which drives rational decisions about how to deploy a technology or not.
“You use evidence to go in a particular direction because the biggest pitfall for the early adopter is getting starry-eyed about the possibilities of a technology without really knowing whether it’s needed in the world,” says Binns.
The big failures happen when people make leaps of faith and commit themselves to bold investments but lack any evidence to support them, says Binns. This can happen with small explorations, but the rise and fall of the GE Digital business unit made this point in more spectacular fashion. GE CEO Jeff Immelt created the unit in 2015 because he felt he needed to take dramatic action to shock the then-125-year-old industrial giant into adopting digital modes of operation. However, neither the technology nor the manufacturing market were ready for the all-encompassing “industrial Internet” GE Digital proposed to build. GE spent too much too soon, and what remains of the unit today is dramatically scaled back.
“There is a psychology that goes, ‘My organization is inert and won't move. And I'm going to force it to move by making a major commitment.’ Unfortunately, that sounds big and brave and bold, but because it’s lacking evidence, it just creates a big hole,” says Binns.
5. Experiment and collaborate with customers.
To improve the customer focus motivation further, many early adopter companies – particularly technology vendors – invest in space for their customers to collaborate and jointly see what’s possible with new tech.
For example, chip maker Analog Devices’ ADI Catalyst unit, based in Ireland, works with different customers to explore the potential for robotics, AI, sensors, and other connected technology. “They are playing with possibilities with customers – sometimes three or four different companies on the same project. So this is a really exciting way of finding out how technologies could solve problems that matter,” says Binns.
The advantage of this joint approach for the customers is clear, as having a big vendor’s backing and experience can increase the odds of new technology finding its footing. SAP illustrates this idea by working in both directions, collaborating with both tech startups and customers through the SAP.io program.
Innovation is always exciting, but early adopters recognize that methodical work underlies results. At Mass General Brigham, Coburn describes his innovation team as “part of a rigorous process that coordinates the assessment of new technology, co-development, potential scale-up and integration, and addressing unmet needs/areas of opportunity.”
“We have a coordinated set of resources that help us test, develop, and accelerate research on emerging digital technologies,” Coburn says. By combining determined customer focus with good risk management processes in a similar way, any company can support consistent innovation with new technologies – the early adopter edge.
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