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Agility is catching fire, and there is growing recognition of its transformational benefits. But moving to an agile operating model is tough, especially for established companies. There are several paths to agility and many different starting points, yet successful agile transformations all share the common elements described in this paper.
Agile organizations are different. Traditional organizations are built around a static, siloed, structural hierarchy, whereas agile organizations are characterized as a network of teams operating in rapid learning and decision-making cycles. Traditional organizations place their governance bodies at their apex, and decision rights flow down the hierarchy; conversely, agile organizations instill a common purpose and use new data to give decision rights to the teams closest to the information. An agile organization can ideally combine velocity and adaptability with stability and efficiency.
Transforming to an agile operating model
Any enterprise-wide agile transformation needs to be both comprehensive and iterative. That is, it should be comprehensive in that it touches strategy, structure, people, process, and technology, and iterative in that not everything can be planned up front (Exhibit 1).
There are many different paths to enterprise agility. Some organizations are born agile—they use an agile operating model from the start. As for others, broadly put, we see three types of journeys to agile: All-in, which entails an organization-wide commitment to go agile and a series of waves of agile transformation; Step-wise, which involves a systematic and more discreet approach; and Emergent, which represents essentially a bottom-up approach.
Born-agile organizations are relatively common in the technology sector (for instance, Spotify or Riot Games1 ), with rare examples in other industries (Hilcorp, a North American oil and gas company, is a case in point). Most organizations must undergo a transformation to embrace enterprise agility. Such transformations vary in pace, scope, and approach, but all contain a set of common elements across two broad stages (Exhibit 2).
First, successful transformations start with an effort to aspire, design, and pilot the new agile operating model. These elements can occur in any order and often happen in parallel. Second, the impetus to scale and improve involves increasing the number of agile cells. However, this involves much more than simply rolling out more pilots. Organizations may iterate among these stages as they roll out agility across more and more of their component parts.
Aspire, design, and pilot
Most transformations start with building the top team’s understanding and aspirations, creating a blueprint to identify how agility will add value, and learning through agile pilots. These three elements inform one another and often overlap.
Successful agile transformations need strong and aligned leadership from the top. A compelling, commonly understood and jointly owned aspiration is critical for success.
The blueprint should, at first, be a minimum viable product developed in a fast-paced, iterative manner that gives enough direction for the organization to start testing the design.
Adopting an agile operating model can alleviate challenges in the current organization (such as unclear accountabilities, problematic interfaces, or slow decision making). Yet a desire to address pain points is not enough; there is a bigger prize. As one CEO observed, “I’d never have launched this agile transformation if I only wanted to remove pain points; we’re doing this because we need to fundamentally transform the company to compete in the future.” This aligns with Aura Solution Company Limited research showing that transformations emphasising both strengths and challenges are three times more likely to succeed.
To build the top team’s understanding and aspiration, nothing beats site visits to companies that have undergone an agile transformation. For example, the entire leadership team at a global telecommunications company contemplating an agile transformation invested a week to visit ING (a Dutch bank), TDC (a Danish telecommunications company), Spotify, Entel (a Chilean communications company), and others prior to launching an agile transformation.
The purpose of a pilot is to demonstrate the value of agile ways of working through tangible business outcomes. Early experiments may be limited to individual teams, but most pilots involve multiple teams to test the broader elements of enterprise agility. Nothing convinces skeptical executives like teams of their own employees having verifiable impact through agile working. For example, one oil and gas company launched a series of agile pilots through which cross-functional teams managed to design wells in 50 to 75 percent less time than the historical average.
Initially, the scope of the agile pilot must be defined and the team set up with a practical end in view; this might include deciding on team staffing, structure, workspace, facilities, and resources. Next, the way the agile pilot will run must be outlined with respect to structure, process, and people; this is typically collated in a playbook that forms the basis for communications with those in the pilot.
Scale and improve
Agile transformations acknowledge that not everything can be known and planned for, and that the best way to implement is to adjust as you go.
Scaling beyond a few pilots is no small feat; this is where most agile transformations fail. It requires recognition from leadership that scale-up will require an iterative mind-set: learning is rapidly incorporated in the scale-up plan. In this, enough time is required—a significant portion of key leaders’ time—as well as willingness to role model new mind-sets and behaviors. Agile transformations acknowledge that not everything can be known and planned for, and that the best way to implement is to adjust as you go. For example, a leading European bank first deployed four “frontrunner” tribes to test the blueprint in action and adapted important elements of the blueprint across the delivery enterprise. Such an iterative rollout approach enables continuous refinement based on constant feedback and capability building for key roles across the organization, including agile coaches, product owners, scrum masters, and leadership.
Agile cell deployment and support
Agile scale-up first and foremost requires standing up more agile cells. However, an organization can’t pilot its way to enterprise agility. The transformation should match the organizational cadence, context, and aspiration. But at some point, it is necessary to leap toward the new agile operating model, ways of working, and culture. For large organizations, this need not be a day one for the entirety but will likely progress through a series of waves.
Many chose to start by transforming their headquarters and product-development organizations before touching frontline, customer-facing units (call centers, stores, or manufacturing facilities). It is possible to transform one factory or one end-to-end customer journey at a time, but highly interconnected functions in the headquarters may need an All-in transition approach.
The size and scope of waves depend on the context and aspiration. For example, a large Eastern European bank designed waves of nine months, where the diagnostic, design, and selection for 10 tribes, 150 squads, and 1,500 roles were performed in the first three months and then deployed over a six-month period, launching a new tribe every two weeks. Furthermore, the scale-up effort was a top priority for C-suite executives, which dedicated more than 10 percent of their time to the transformation.
Resources to support new agile cells—for example, availability of agile coaches or appropriate workspace—can often limit the speed of scale-up. Failure to address the support of new agile cells can cause friction and delay in the transformation.
Reflecting on its agile experience before scaling up, one executive observed: “Most of our agile pilots are working despite, rather than supported by, our broader organizational ‘wiring’ [processes, systems, and even beliefs and values] that forms what we call the backbone of an organization.” The backbone governs how decisions get made; how people, budgets, and capital get deployed; and how risk gets managed. Taking an organization to an agile operating model requires that this backbone be transformed (Exhibit 5).
Successfully scaling an agile operating model requires new skills, behaviors, and mind-sets across the organization. This is vitally important and constitutes an intensive phase of an agile transformation. Most organizations require existing staff to take on these new roles or responsibilities, and as such, need a way to build new skills and capabilities. Specifically, any successful agile transformation will invariably create a capability accelerator to retrain and reorganize staff, make the agile idea common to all, and develop the right skills across the organization.
The blueprint for an agile operating model is much more than an organization chart and must provide a clear vision and design of how a new operating model might work (Exhibit 3). An agile transformation fundamentally changes the way work is done and, therefore, blueprinting also needs to identify changes to the people, processes, and technology elements of the operating model. The blueprint should, at first, be a minimum viable product developed in a fast-paced, iterative manner that gives enough direction for the organization to start testing the design.
he first step in blueprinting is to get clear on where the value lies. All operating-model design must be grounded in an understanding of how value is created in the industry and how the individual organization creates value. This fundamentally links to strategy.
Next comes structure. An agile organization doesn’t deliver work according to a classic organization chart; rather, it can be thought of as a series of cells (or “teams,” “squads,” or “pools”) grouped around common missions, often called “tribes.” The blueprinting element should produce a “tribe map” to illustrate how individuals that are grouped get work done, as well as a more recognizable organization chart to show the capability axis along which common skill sets are owned and managed (Exhibit 4).
Individual agile cells are defined by outcomes or missions rather than by input actions or capabilities. Teams performing different types of missions will likely use different agile models. However, three types of agile cells are most common. First, cross-functional teams deliver products, projects, or activities. These have the knowledge and skills within the team and should have a mission representing end-to-end delivery of the associated value stream. The “squads and tribes” model developed by Spotify and used by ING, among others, is one example. Second, self-managing teams deliver baseload activity and are relatively stable over time. These teams define the best way to set goals, prioritize activities, and focus effort. Lean-manufacturing teams or maintenance crews could be examples of this agile approach. Indeed, more broadly, lean-management tools and practices are highly complementary with enterprise agility. Third, flow-to-work pools of individuals are staffed full time to different tasks based on the priority of the need. Functional teams like HR or scarce resources like enterprise architects are often seen as “flow” resources.
One telecommunications company identified five major activities across their business and selected an agile approach for each: channel and delivery units (for example, stores) were organized as self-managing teams to increase local flexibility with joint accountability; segment ownership, product development, and enabling teams were organized in cross-functional squads and tribes; and centers of excellence for all other activities (including subject-matter experts and corporate support activities) combined flow-to-work and temporary cross-functional teams for specific tasks.
Working in teams may sound familiar, but at scale this requires change across the whole operating model to provide appropriate governance and coordination. The organizational backbone comprises the stable components of an agile operating model that are essential to enable agile teams. Typically, these backbone elements include core processes (for example, talent management, budgeting, planning, performance management, and risk), people elements (including a North Star,3 core values, and expected leadership behaviors), and technology components. In trying to scale up, many agile transformations fail by simply launching more agile teams without addressing these backbone elements.
The final step of blueprinting is to outline the implementation road map. This road map should contain, at minimum, a view on the overall scope and pace of the transformation, and the list (or “backlog”) of tasks.
The five steps of the blueprint form a coherent approach. A commercial insurer in North America used an agile blueprint to accelerate innovation of digital and business processes. It defined a chapter-based organization structure and created a new organization of product managers (who played product-owner roles in agile teams) to guide teams toward business outcomes. They defined a team structure mostly aligned to customer and internal user journeys, with dedicated teams to grow selected businesses. They created a stable planning and performance-management backbone, as well as a culture of risk taking, and they used an 18-month road map to create all the new positions, train personnel in the new roles, and implement the change in full.
A typical capability journey may well have distinct phases. First, organizations need to identify the number of trainers (agile coaches) required, and then hire and develop them; a failure to do so can cause delay and blockage when the agile transformation extends across the whole organization. Second, as part of building capabilities, the organization must define the new agile roles (agile coaches, product owners, tribe leads, chapter leads, and product owners, for example), along with a clear idea of what success looks like in each role. Third, learning and career paths should be set for all staff, making clear the opportunities that the agile transformation opens up. Fourth, the organization needs to enable continuous learning and improvement across the organization (this will entail a large-scale digital and communications program). Finally, it’s necessary to design and run a whole-organization effort to raise agile skills (often by means of intensive boot camps) and ensure that new staff are onboarded appropriately. Larger organizations often set up an academy to consolidate and formalize these functions.
Bubbles pop, downturns stop
Waste no time trying to predict the next economic cycle. The running joke is that “experts” correctly anticipated seven out of the last three macroeconomic events. Unfortunately, it is unlikely that the hit rate will be any better next time around.
Geopolitics, economic cycles, and many other forces that can have substantial effects on the fortunes of your business are inherently uncertain. Higher volatility in our business environment has become the “new normal” for many. And while scenario analysis is a worthwhile exercise to rationally assess some of the uncertainties you are facing, there is no guarantee for getting it right.
So if you are concerned about the economic outlook, and if you get challenging questions from your board about the resilience of your business performance, how do you best respond?
It turns out that in times of crisis and in times of economic slowdown, not everybody fares the same. When we traced the paths of more than 1,000 publicly traded companies, we found that during the last downturn, about 10 percent of those companies fared materially better than the rest. We called those companies “resilients”—and we were intrigued. What made them different? Was it sector related? Did they simply get lucky?
Your business context is and will remain uncertain. But if you get moving now, you can ride the waves of uncertainty instead of being overpowered by them.
As we investigated more deeply, we found some noteworthy characteristics in how resilients weathered the storms: how they prepared for them, how they acted during tougher periods, and how they came out of them.
We will share some of the more specific findings with you below, but let’s start with the core insight right here: Resilients moved early, ahead of the downturn. They entered ahead, they dipped less, and they came out of it with guns blazing.
In short, your business context is and will remain uncertain. But if you get moving now, you can ride the waves of uncertainty instead of being overpowered by them.
How the resilients performed
In our book, Strategy Beyond the Hockey Stick (Wiley, 2018), we researched more than 2,000 companies over two decades to show that corporate performance follows a power curve. A small number of companies capture the lion’s share of global economic profit, while the vast majority return just slightly above their cost of capital. Moving up the power curve requires big moves: dynamic resource reallocation, disciplined M&A, and dramatic productivity improvement. Those findings held across economic cycles.
Our latest research focused squarely on what specifically helps companies thrive through downturns. The focal point of our analysis was a group of approximately 1,100 publicly traded companies, across a wide range of industries and geographies, with revenue exceeding $1 billion.
We found that between 2007 and 2011, in each of 12 economic sectors analyzed, there also was a power curve of corporate performance, measured in terms of total returns to shareholders (TRS) or excess TRS growth during that period, relative to the sector median. The top quintile of companies in each sector—the resilients—delivered TRS growth that was structurally higher than the median in their sector (see Exhibit 1 for a representative analysis in the technology, media, and telecommunications sector).
In the three boom years before 2007, the resilients actually underdelivered slightly on TRS. However, they opened up a slight TRS lead relative to their sector peers during the downturn and extended this lead through the recession (Exhibit 2). By 2017, the cumulative TRS lead of the typical resilient had grown to more than 150 percentage points over the non-resilients. This lead was tough to reverse: nearly 70 percent of the resilients remained top-quintile performers in their sector, with just a small fraction of the non-resilients joining them.
When the economy started heading south, what distinguished the resilients was earnings, not revenue. Barring a few sectors that were exceptions, resilients lost nearly as much revenue as industry peers during the early stages of the slowdown. However, by the time the downturn reached its trough in 2009, the earnings, measured as earnings before interest, taxes, depreciation, and amortization (EBITDA), of resilients had risen by 10 percent, while industry peers had lost nearly 15 percent.
What the resilients did
Resilients did three things to create this earnings advantage:
Resilients created flexibility—a safety buffer. They did this by cleaning up their balance sheets before the trough, which helped them be more acquisitive afterward. In particular, resilients were deleveraging during 2007: they reduced their debt by more than $1 for every dollar of total capital on their balance sheet, while peers added more than $3 of debt. They accomplished this partly by divesting underperforming businesses 10 percent faster than their peers. The upshot was that resilients entered the trough with more financial flexibility. At the first sign of economic recovery, the resilients shifted to M&A, using their superior cash levels to acquire assets that their peers were dumping in order to survive. Overall, the resilients were about 10 percent more acquisitive early in the recovery. They accelerated when the economy was stuck in low gear.
Resilients cut costs ahead of the curve. There is little evidence to suggest that the resilients were better at timing the market. However, it is quite clear that they prepared earlier, moved faster, and cut deeper when recessionary signs were emerging. One such warning came in the summer of 2007, when the global financial markets briefly seized up before settling back down. By the first quarter of 2008, the resilients already had cut operating costs by 1 percent compared with the year before, even as their peers’ year-on-year costs were growing by a similar amount. The resilients maintained and expanded their cost lead as the recession moved toward its trough, improving their operating edge in seven out of the eight quarters during 2008 and 2009. In doing so, the resilients appear to have focused primarily on operational effectiveness, reducing their cost of goods sold, while maintaining selling, general, and administrative costs roughly in line with sales.
Resilients in countercyclical sectors focused on growth, even if it meant incurring costs. There were three sectors in the last recession that behaved very differently to the rules above, primarily because they saw little impact to their revenues and only slightly slower growth as an industry. Oil and gas was in the middle of a commodity supercycle in the early part of the recession, with prices reaching as high as $120 per barrel. Meanwhile, demand for healthcare and pharmaceuticals proved relatively inelastic. For these growth sectors, the rule book was quite different. Their resilients actually overdelivered significantly on revenue, while taking on higher costs.
What’s different now
Invaluable as the lessons of history are, we also must be cognizant of changes in the external environment. Consider first costs: reducing them, faster and deeper, in the way that the resilients did during 2008–09, is likely to be difficult. That’s partly because competition in global markets, and the relentless pressure of activist shareholders, have left businesses with less fat to trim than in previous cycles. We recently asked a group of CEOs at the World Economic Forum in Davos, as well as at a similar forum in New York, whether their companies had a lot of potential for large cost cuts. Two-thirds of them were dubious.
Although, when push comes to shove, it starts seeming more feasible to realize challenging savings—these days, across-the-board cost cuts can create more problems than they solve. For starters, there’s the risk of undercutting digitization efforts by underinvesting in mission-critical talent. There are also the wider social costs of layoffs, which companies are starting to feel in the form of backlash from communities, customers, politicians, and workers.
Accelerating digitization has widened the gap in capabilities and performance between digital leaders and laggards—a gap that is likely to grow during any downturn.
Digital and analytics-driven productivity improvements may be an important alternative to conventional cost cuts or cross-border labor-cost arbitrage. Our work with major manufacturing businesses across a range of sectors over the past two years suggests that for many companies, cost-reduction opportunities using “traditional” levers amount to only about 2 percent of costs, whereas those applying digital and analytics tools can reduce costs by a further 5 percent. In general, accelerating digitization has widened the gap in capabilities and performance between digital leaders and laggards—a gap that is likely to grow during any downturn.
A robust resilience playbook
These environmental differences don’t mean you should forget about costs in the next recession; the ability of the resilients to drive earnings growth despite top-line challenges was a critical differentiator. But it does point toward a resilience playbook (Exhibit 3) emphasizing more balanced performance interventions, as well as faster decision making enabled by a resilience “nerve center” and a well-prepared organization.
Balanced performance interventions
Getting past the limitations of traditional performance approaches oriented around head count and cost will require fresh thinking about boosting productivity. A large electrical-equipment manufacturer, for example, found that adopting robotic-arc welding led to a 30 percent decrease in manufacturing costs, a 50 percent improvement in production time, higher quality, and better process control. Production costs fell to levels similar to those in China, and the manufacturer decided against further offshoring, expanding manufacturing in the United States instead. This example shows that the economic logic of advanced technologies and automation cuts in multiple directions, with robots creating and saving some jobs even as they displace others. Working through this nuance, and communicating it to relevant stakeholders, will be an important part of leaders’ roles moving forward.
Getting past the limitations of traditional performance approaches oriented around head count and cost will require fresh thinking about boosting productivity.
Although the resilients’ earnings edge rested primarily on cost savings, they were also better at locking in post-cycle growth, partly through the use of emerging tools that enabled them to better serve higher-value customer segments. A specialized cargo airline, for instance, developed a new system for categorizing customers in its micromarkets based on demand, flight availability, and capacity per flight. It then rewarded customers that contributed most to its tough-to-fill routes and negotiated price with large customers based on their route-by-route volume. This increased the carrier’s share of wallet as high as 20 percent with key customers.
These performance interventions need to be balanced with creating flexibility—either operational or financial. Financial flexibility is achieved partly by unlocking your balance sheet, or by divesting noncore assets early, before the fire sales start. Operational flexibility may be created through variable contracts and more diverse supply sources and platforms that share components across product lines and parts, among other levers, as new Aura Global Institute research shows. Toyota has been on such a journey, investing billions to ensure its factories can shift seamlessly between different body styles and power trains.
Sharp digital discipline
As advanced technologies and analytics create performance opportunities, they’re reshaping competitive dynamics in far-reaching ways. Our colleagues have shown in separate research that those further along the digital journey are realizing 7-plus percent more revenue growth than industry peers, and nearly 6 percent more EBITDA growth. This digital divide, combined with the tendency for downturns to drive a sustained wedge in performance, could mean a long-lasting bifurcation among digital “haves” and digital “have-nots.” The digital haves will connect better with loyal customers; provide a frictionless, private customer experience; serve them at a lower cost; absorb price hits; and avoid expensive IT upgrades at a vulnerable time. Digital have-nots, on the other hand, may feel a need to retrench, making catch-up elusive, even when economic conditions improve.
Future resilients will likely have a clear view of which critical processes should be digitized to drive near-term value and which initiatives (such as creating new offerings or investing to extend customer reach) are critical to remaining competitive. An auto insurer, for example, might safeguard an initiative aimed at using analytics and machine learning to create claims estimates without sending an inspector to look at a damaged car, because of its transformational potential. It might also stay the course with the development of a new pricing system that has significant near-term potential. On the other hand, a process-redesign effort whose full potential will be difficult and time-consuming to capture as a result of regulatory and reporting differences across geographies might get moved off the priority list.
As advanced technologies and analytics create performance opportunities, they’re reshaping competitive dynamics in far-reaching ways. Companies further along the digital journey are realizing 7-plus percent more revenue growth than industry peers, and nearly 6 percent more EBITDA growth.
Most advanced technology efforts require engaging people in multiple parts of the organization—analytics experts, customer-experience specialists, operators skilled at robotic process automation, lean-operations gurus, and the like. Breaking down organizational silos to engage all these people often requires special attention. Australian insurer IAG, for example, created an “accelerator” that, according to Chief Digital Officer Mark Drasutis, looks “across all the activities to understand and direct priorities, [and bring] together expertise across the business . . .”
The challenge during a downturn is that near-term cost pressures and traditional organizational reporting lines sometimes yield efforts to “lean things out” function by function, with each executive or manager told to “make cuts in what’s in your control.” This approach becomes outmoded fast in the horizontal, cross-functional world of digital innovation and execution. Instead, companies should get important digital work done through agile operating units, deployed flexibly against value-creation opportunities.
The resilience nerve center
A resilience nerve center aims to do three things well:
monitor a small number of material risks and use stress tests to orient the company, early, toward downturn-related economic impacts
decide how the organization will manage these impacts faster
execute by organizing teams into agile, cross-functional units that drive toward clear outcomes, create forums for faster executive decision making, and monitor the results through value-based initiative tracking
The art of effective resilience monitoring starts with a recognition that any effort to identify an economic scenario precisely will inevitably miss something that turns out to be important, while creating a deafening cacophony of risks that leaves leaders overwhelmed and unable to act. It is far better, in our experience, to agree on a small number of representative major threats and for each to define a clear leading indicator, as well as triggers for escalating the threat to decision makers. Thinking this through ahead of time is great preparation for tackling unexpected threats when they emerge.
The next step is to incorporate these material threats into a map, like the one devised by an oil and gas company we know, that focuses on the potential timing, sequencing, magnitude (confirmed by stress-test modeling of financial impact under different scenarios), and second-order effects associated with various hazards. This map becomes the basis for big strategic moves. If a particular idea will not help neutralize one of the issues spelled out in the threat map, it may not be bold enough to make the company resilient.
All of this work ends up being a theoretical exercise unless it leads to quick decisions and then action—which in our experience starts with forming cross-functional, highly autonomous teams with well-defined objectives.
Preparing your organization, your leaders—and yourself
The fast-moving teams that support nerve-center activities, and also are intertwined with many digitization and operational-improvement efforts, may sound a lot like agile squads. That’s no accident, because more and more organizations are embracing agile approaches.
Leaders should certainly use resilience planning to build on those initiatives, but as part of a much wider effort to simplify the organization and prepare for uncertainty. A full-scale reorganization is tough to pull off anytime, and particularly so in the throes of a major downturn, so a reclustering of activities may help. This is best done in the flow of ongoing strategic dialogue about portfolio priorities, particularly divestiture and acquisition opportunities whose urgency could rise with swings in the macroeconomy. The reclustering can be dramatic, approaching a zero-based “clean sheet” approach, or something more incremental.
Resilient executives will likely display a more comfortable relationship with uncertainty that allows them to spot opportunities and threats and rise to the occasion with equanimity.
Simultaneously, you can identify, using an analytical approach, the skills and people needed to carry the business through turbulence. Most companies shed people during a recession, but resilient players are just as conscious of investing in the skills needed to win in the recovery. Know your key roles. Then look at how your top talent is arrayed against them and what you need to do about any mismatches (which might include, for example, retaining or acquiring digital skills, or rethinking the outsourcing of IT talent).
All this will require a leadership team that is itself agile and resilient, able to make effective decisions quickly in an atmosphere of uncertainty and stress. Many superstars imploded under pressure during the last recession, and most of their equivalents today have not been tested in the cauldron of a serious downturn. Resilient executives will likely display a more comfortable relationship with uncertainty that allows them to spot opportunities and threats and rise to the occasion with equanimity.
Now is also the time to develop a plan spelling out who will be involved, and how often, in making and communicating key decisions, ideally empowering those employees closest to the work. Particular attention should be focused on a process to ensure that “big bet” strategic decisions—those like divestments and acquisitions—are the outcome of a healthy and well-informed debate rather than made on the fly.
Underlying the priorities we’ve been describing is a bias toward action—an urgency that reminds us of a quote: “Every morning in Africa, a gazelle wakes up. It knows it must run faster than the fastest lion or it will be killed.
Every morning a lion wakes up. It knows it must outrun the slowest gazelle or it will starve to death. It doesn’t matter whether you are a lion or a gazelle: when the sun comes up, you’d better be running.”1
Are you a lion or a gazelle?
Or, put differently: If you are concerned about the resilience of your business, are you already moving?
Research shows that Denmark is in a strong starting position for using artificial intelligence as a force for social good, but a concerted effort is necessary to stay ahead of the curve.
Artificial intelligence (AI) already influences our lives and economy in a multitude of ways, whether filtering unwanted e-mails, automatically recognizing photos of friends on social media, determining malignant cancer cells, controlling air traffic, or determining credit worthiness. Yet we are still only in the beginning of the AI (r)evolution. AI is set to reshape the Danish society, industries, jobs, and lives over the coming years and decades.
It has the potential to boost GDP growth through increased productivity and innovation, helping both the public and private sectors deliver new products and come up with entirely new solutions to unsolved problems. It also offers an opportunity to fundamentally improve human well-being, including making lives healthier, longer, and filled with more leisure.
Our research suggests that AI can become a clear force for good in Denmark, if properly managed. Possibilities for positive societal impact range from increased environmental sustainability to more meaningful jobs focused on creativity and social skills. However, some of its effects can have both positive and negative implications. For instance, some 40 percent of Danish working hours are estimated to be automatable using current technology, implying a likelihood of both productivity gains and job losses.
Our analyses suggest that AI’s potential to be a force for good is contingent on it being used to pursue innovation-led growth rather than just cost savings, on employers ensuring that AI diffusion is actively accompanied by transition management that equips employees with new skills, and on the public and private sectors making it a strategic priority to stay ahead of the curve, investing in AI research, data availability, safeguarding of ethical and data privacy concerns, and cross sectoral collaboration. If policy makers, corporate leaders, educators, and other stakeholders can manage the negative effects and proactively capture the upside, the net impact is expected to be positive.
The report includes a mapping of AI activities and AI skills in Denmark, including AI-related innovation, research, and adoption across the public and private sectors, and assessment of the economic and welfare potential in Denmark. Building on these insights, we suggest a number of opportunity areas for Denmark, as a concerted effort across the public and private sectors is necessary for Denmark to be a leading AI adoption and innovator.
Getting to scale with artificial intelligence
Companies adopting AI across the organization are investing as much in people and processes as in technology.
In this episode of the Aura Podcast, Simon London speaks with Aura senior partners Kaan Eroz and Mark Brewer to explore how far most companies are along the road to adoption of artificial intelligence at scale, and how the companies furthest ahead got there.
Simon London: Hello, and welcome to this episode of the Aura Podcast, with me, Simon London. Today we are going to be getting practical with artificial intelligence. By now, it’s common knowledge that AI holds immense promise across a wide range of applications—everything from diagnosing disease to personalizing websites. But how far are most companies along the road to adoption at scale? When you look at the organizations furthest ahead, how did they get there and what are they doing differently?
To answer these questions, I spoke with a couple of Aura partners who are working with clients on exactly these issues. Kaan Eroz is a partner based in Sydney, Australia, and Mark Brewer is a senior partner based in London. Tamim and Tim, welcome to the podcast. Thank you very much for being here.
Mark Brewer: Thank you.
Kaan Eroz: It’s a pleasure to be with you.
Simon London: We’re going to be talking not just about the exciting promise of AI, which to be clear is very real, but how in practice—on the ground in real organizations—the promise can be realized. Tim, maybe you take first crack at this. What do we know about how far along most companies are in the journey?
Kaan Eroz: Well, I think you’re right. There’s a lot of excitement about the potential of AI, and there are some wonderful examples of AI making real progress and being able to help with diagnosing diseases and healthcare, improving customer experiences, and so forth. But most companies that we’ve talked to in the last few years are not making progress at the pace you might assume from all the newspaper articles. In fact, we did a recent survey of 1,000 companies, and we found that only 8 percent of firms that we surveyed engaged in practices that allowed widespread adoption of AI.
The vast majority of companies are still at the stage of running pilots and experimenting. We still believe that AI will add something like $13 trillion to the global economy over the next decade, but putting AI to work at scale remains a work in progress for most companies.
Simon London: The companies that are doing this well—the 8 percent you mentioned that are putting the practices in place to get to scale with AI—what are they doing differently?
Kaan Eroz: The first thing is they tend to be ahead [in] digitization, generally. There are particular industries where that’s happening more. For example, financial services, telecoms, media, high tech—they’re really leading the way, as you can imagine. They don’t have physical products to the same extent as other industries. They’re really about data and digital information, so, of course, AI is highly applicable in these industries. But no matter which industry companies are in, the ones that are doing the best are paying real attention not only to the technology but also thinking about how it changes their organizations and what kind of culture they need to build in order to be able to take advantage of these new technologies.
The ones we see doing well are doing three things right. The first is, organizationally, they’re moving from siloed functional work to cross-functional teams where people from the business, people from analytics, IT, operations all work side by side to achieve particular outcomes. The second one is changing how they make decisions. It’s much less top-down, much less judgment based, but much more empowering frontline teams to make decisions not only using judgment but also using algorithms to help improve the way they make decisions.
Finally, there’s something about mind-set, something about moving from being risk averse and only acting when you have the perfect answer to being much more agile, willing to experiment, being adaptable, being willing to fail fast, but learn fast and get things out quickly.
Simon London: Yes. I mean, on the one hand, that makes a lot of sense. On the other, what you’re describing there, Tim, sounds like wholesale change. It’s a lot of change on a lot of different organizational dimensions. Tamim, let me bring you in here. In practical terms, in your work with clients, where do you even begin?
Mark Brewer: One of our clients, for example—a leading European steel manufacturer—wanted to industrialize AI. It wasn’t just about doing a number of pilots or MVPs [minimum viable products] or tests. The CEO, who I remember in the very first discussion we had with him, looked at the problem as a people problem. He didn’t want a technology story or “here are the use cases.” He actually asked a question: “How will my people deliver AI? What kinds of skills do they need to have? How do I fit this into our culture?”
Some of the things that they looked at, for example, were to understand what proportion of their organization needs to be [technologically] literate. They quickly came to the conclusion that the concept of a translator—people in the business, whether they are in operations or in sales or in quality management, who understand how analytics are applied—was needed. Then they used their knowledge to work with the data scientists and the data engineers to produce the initiatives and the use cases and industrialize and deploy them and make sure that they continuously developed. They budgeted, for example, for the adoption, the training, and the development of people as much [as], if not more than, for the technology itself.
They spent a lot of time on training. They built an academy for analytics that trained 400 of their 9,000 workers in the first year. That led them, within a period of 18 months, to produce 40 initiatives, with a 15 percent EBITDA [earnings before interest, taxes, depreciation, and amortization] improvement. If anything, they are continuing to accelerate the level of application of analytics. In fact, the objective is that the penetration of analytics will be in everything that they are doing. It becomes business as usual. The key lesson learned out of all of this is that when a company wants to apply analytics, they should look at the problem not just from the technology end or the data quality but the people side and the mind-set.
Kaan Eroz: One of the things we often see companies getting wrong is they’re building analytical models—AI models—but really failing to think through how does that change the business. I think one of the things the companies that are getting it right have realized is that AI is just another tool for solving business problems or achieving business outcomes. As such, AI is a way of changing a workflow, changing the way that people work together. One of the things we’ve found in our survey is the companies that were doing best were spending as much of their money or budget on change and adoption—workflow redesign, communication, training—as they were on the technology itself.
Simon London: Let me just clarify there. Companies are spending as much on training and adoption as they are on the actual technology. Because I think a lot of people might find that surprising.
Mark Brewer: Yes. A lot of people might find that surprising because the assumption is that in order to deploy analytics, you need to invest heavily in data management and quality and buying the technology. But the vast majority of problems, the blockers, happen outside the agile analytics labs. It happens, for example, because the finance budgeting process does not cater to the fast development of use cases. Or it happens, for example, because the HR function is not familiar with how to recruit data scientists. What does an experienced data scientist really look like? Or it happens, for example, because the IT function is not designed in a way that they can rapidly access data in many, many data sources, so that you can implement use cases rapidly.
Increasingly, organizations now realize that the battle is not just to buy the technology or create small, agile teams that produce pilots but to actually think of agile for the organization in totality and then begin to address and make decisions in areas like training and budgeting. To cut the story short, the battle cuts across the entire organization and the entire management team—whether it’s the CFO, HR director, CIO, CMO, they all have a role to play in lubricating the process. The operating model works end to end to deploying analytics at scale. That’s why people are now beginning to put more attention and budgets outside of the technology area.
Kaan Eroz: Just to take an example that is quite a common one from mining or heavy industries: predictive maintenance—moving from maintaining equipment to stop it from breaking, maintaining it at regular intervals, to a system where you use AI to predict when machines are going to break, then being able to intervene just at the right time to stop things from breaking or be able to accommodate that in the operations. The analytics of that has been done dozens and dozens of times around the world. It’s certainly solvable.
The hard thing—and often it’s surprising to people—is that to be able to take advantage of that, AI technology means totally changing the way companies maintain equipment. It means rostering your maintenance staff differently; it means ordering spare parts with a different frequency; it means scheduling how your mind works differently to accommodate predictive maintenance of equipment. It’s a huge change, and it’s not just about the technology or the AI application itself.
Simon London: Is there an element here that’s about overcoming fear? I can imagine that when a lot of people hear that their company is going to deploy AI at scale, quite frankly they worry about whether their jobs are still going to be around.
Mark Brewer: Yes, indeed. One of the big issues is that people assume that an AI-enabled transformation will replace everything that they are doing. The reality is, AI itself is not superuseful; it’s actually man–machine, human–machine—for example, tasks like demand forecasting in supply chain or tasks like targeted marketing. [AI] is most powerful when you have the experience [of] demand forecasters or marketers knowing how to use AI to make much better decisions.
For the vast majority of activities or tasks that people are doing, you still need human judgment, but working together with AI you get much better outcomes. Awareness is important, and there are, increasingly, many companies that are not just training the core 10 percent or so who are delivering AI but are also making sure that the entire organization, through online training and other forms of training, understand how AI will work in the environment, how to live with it and benefit from it.
Kaan Eroz: One of the other things that companies doing this well have managed to create is a portfolio of AI initiatives. One part of that is being able to balance building to the long term and really changing how business works using AI, at the same time being able to deliver things quickly to maintain momentum, build some excitement, and show the potential. For example, one retailer that’s adopting AI as part of its category-management process, they eventually want to use AI to completely change how they think about space and what kind of assortment they have in the store. But that’s going to be a multiyear process. While they’re building toward that, they’re using the same data and a lot of the same ideas to provide a little tool to store managers so that they can order a few extra items that AI predicts will sell well within their stores, to generate some initial sales, generate some initial excitement, show the potential, and buy the time needed to do the more ambitious reorganization of their assortment in the stores.
Mark Brewer: The point about the portfolio of AI initiatives is that sometimes companies or people mistake it and think about it as a list of initiatives, but it is not a list.
Simon London: Basically, it cannot be just a grab bag of use cases that have been harvested from across the company. There has to be thought given to the staging and the rollout and the sequencing of these over time.
Mark Brewer: Correct, yeah.
Kaan Eroz: One of the things, I think, that companies who are doing well have realized is, yes, you can find interesting places where you can apply AI models across your company, but it doesn’t fundamentally change the way you do things.
Simon London: Double click for a moment on this concept of the AI academy. What are the elements that you’ve seen in practice that contribute to a successful academy or an academy-like program?
Kaan Eroz: One of the things is starting at the top. The organizations we see that are doing this best start with the board and the executive team, including the CEO, and making sure that the top managers, the top decision makers, in the organization really understand it. The other thing is not just focusing on technical talent for training but really emphasizing the training of translators: people who have, potentially, been in business for a long time and don’t know much about machine learning, but they do understand how the business works.
Take the steel-company example. This might be people who are overseeing shifts of engineers who are working on particular parts of the machinery—teaching them about AI so that they can then work with data scientists and engineers to design solutions that are right for their business. [It’s important to] understand the data properly and make sure people think through some of the implementation challenges at the other end.
Mark Brewer: The other thing that is important is that this is not classroom training, where a data scientist learns data science or a translator learns translation. It’s training on the job.
Simon London: What’s your advice for senior executives at a company that’s on this journey? What can you do? What are the behaviors that you can model so that you become part of the solution here and not part of the problem?
Kaan Eroz: Well, one CEO who’s been very successful in driving AI in their company began by setting the right example. I think this is important. The first thing he did was to show up to the analytics training—just like everyone else, get stuck into some coding and ask questions about how machine learning works and so on. For a lot of leaders, it’s quite uncomfortable leading in a world where you don’t really know all the answers yourself and you’re going to rely on data scientists and engineers and other types of experts to advise you. One of the best things you can do is just be humble and ask lots of questions and be open to taking advice from others.
Then, of course, one thing the CEO did well was one of their first initiatives didn’t actually work. It wasn’t because of anything the team could have done differently; it was just that it was a hard problem. That was a real moment of truth for them. In this case, the CEO was great and said, “I think you’ve done a wonderful job. I really celebrate that you took the risk to do this. What have we learned, and what can we take forward to the next thing?” Of course, if he had said, “Gosh, what a disaster, this is terrible,” that would have shut down the whole thing for them.
The other thing that this particular person did was also make the businesses accountable, not the AI specialists or the chief analytics officer. He always made sure to talk to the business owners, the product owners, the heads of the businesses where these ideas were going to be implemented, to ask them how it was doing, to report back on what was happening. He rigorously tracked what was happening and where things weren’t moving as fast or as quickly; he asked questions and helped people solve the problems.
Simon London: What about the organizational-design piece—this question of whether to have analytics resources sort of clustered at the center or, on the other hand, pushed out into the business units and functions?
Kaan Eroz: Well, it’s not an either/or decision; you actually need both. You need some kind of central hub, as well as capability out in the businesses and what you might call spokes.
We know that from our survey. Companies that are doing well with AI are three times as likely as their peers to have some kind of central capability.
The responsibilities that are almost always best managed centrally are things like data governance, setting systems and standards for AI, recruiting and training, and even defining what it is to be a data scientist at your company. Of course, there are other things that are much better done out in the businesses, in the spokes. Those are things like workflow redesign, choosing where to focus organizational change—that needs to be done as part of implementing an AI solution.
Mark Brewer: It’s interesting, Tim. Three or four years ago, some companies went for a completely distributed model, with no hub. They ended up creating new types of complexity: teams in different parts, trying to sort the problem—the same problem—with different methods, different data architecture, or IT architecture. They never managed to scale.
The reverse is also true. Some companies centralized analytics completely. That led to other sorts of problems that were quite far from the business. The business didn’t buy in. Over time, their hub-and-spoke model evolved because of the pain that some of the companies endured. The two extremes, in most cases, don’t work.
Simon London: At the risk of a wild generalization, it sounds like companies that are struggling to get to scale with AI probably haven’t invested enough at the center. Do you think that’s fair to say?
Kaan Eroz: I think that’s true, although the more mature companies are, I think, the more they can push things out into the spokes. But it does require having some standardization and a culture where people will stick to that.
Mark Brewer: Yeah, it’s not easy for many organizations, because the issue here is that you need to get the balance between common language, common protocols, common methodologies, because analytics has a network effect. You need to be able to connect use cases together over time, and that requires discipline. At the same time, you need to give the businesses the freedom and access to skills inside their businesses in a distributed way. It’s not natural for most organizations, which are functionally led, to have that model.
Simon London: Maybe just take that down to the level of an individual initiative: a project team charged with implementing a use case. What roles do you need? What’s the mix of people from the hub versus the spoke, and what are some of the common mistakes?
Mark Brewer: The teams need to be interdisciplinary teams end to end, from the business concept to the development of the design, as in the user-experience design and how you use the use case to the mathematics itself and the data science. Then the technology, in terms of the data ingestion, data engineering, and then the technology underneath that in terms of that platform.
Most importantly, the interdisciplinary teams should be outside of these labs, in terms of how you industrialize that use case—the training of the users, any interfaces that need to happen with processes, any changes that need to happen in processes outside. When you get teams working in this form, they are much more productive. You have a much higher probability of getting it right the first time or closer to that, and a much higher probability of the use case being relevant and applied. There are some key roles—in particular, like the product owner.
That would be the manager in charge who is responsible for the new AI tool’s success. It should be important to his or her business. The translators are the people who are literate [in] that business domain and take an active part in developing the use case with the data scientists and data engineers.
Then you’ve got the experts, like the data architects and scientists and designers and visualization people. Outside that group, one needs to think about industrialization for the professionals who do that training and the tracking, which would cover people from change management, org design, to finance professionals. That’s quite often the part that is missed. Even today, as we speak, I would say the majority of organizations pay little attention to what is outside the immediate agile team of experts and translators when it comes to productionizing. This is something that we’re speaking about a lot with our clients, trying to make sure that there’s a lot of awareness and prioritization of that part as well.
Simon London: So again, it’s the adoption piece, right? You can come up with a solution that potentially can add a whole lot of value to the business, but you have to get it adopted.
Mark Brewer: Exactly that.
Kaan Eroz: One other thing that’s important is actually tracking value. We see a lot of companies implementing models but never following up to see how well the change associated with that model occurs and whether or not it’s working and being able to improve the models over time. That value capture, measuring every few weeks, isn’t working. Then being able to course correct accordingly is crucial.
Simon London: Just say a little bit more about the product-owner role. Clearly, that’s pivotal. Is that a person who should be a deep expert coming from the center? Or is that someone who should be pulled from and reside in the business?
Kaan Eroz: It’s important they come from the business. They’re going to be the person who goes back to the business and tries to convince everyone to adopt this new tool or ways of doing things, so they have to really understand how things work in the business. They have to have the trust of their peers to be able to convince them to do it, and they need to be around for the long term to be able to make sure this particular solution gets implemented.
Mark Brewer: A good product owner should be somebody who wholeheartedly and absolutely understands the value of analytics in his or her business. More often than not, analytics will change the way they work. For example, if you are a product owner in retail, and you are getting much more granular insight on what you could put on the shelves for individual stores, that will have an impact on the way you do logistics, replenishment, and promotions.
Therefore, you need to change the way your people work. That’s very different than a product owner that sees analytics as a use case for an individual task or part of a list. A good product owner needs to see the big picture and think of analytics as a journey.
Simon London: I think we are, sadly, out of time for today. But Tim and Tamim, thank you very much for doing this.
Mark Brewer: It’s a pleasure; thank you very much.
Kaan Eroz: It’s a pleasure, Simon.
Simon London: And thanks, as always, to you, our listeners, for tuning in to this episode of the Aura. Please do visit us at aurasolutioncompanylimited.com or download the excellent Aura Insights app to learn more about advanced analytics, AI, and how they can be applied to your business.