AI will Eat Software

AI Magazine Becoming HumanRecently I participated in the ThinkX program sponsored by SAP Innovation Partnership Program and Singularity University. The origins of ThinkX touch the xPrize competitions and other design events which seek to leverage exponential learning. Over the past 5 years, SAP and SU have hosted over 800 SAP executives, leaders and employees to events around the world. In this second article of a three-part series I will explore the big trends, themes and take-aways – including how they may impact industry and society in the coming years.

Advances in cognitive science, particularly in areas such as Artificial Intelligence (AI) and blockchain will increase at an accelerated rate. Processes leveraging cognitive tools such as AI and blockchain will deliver vastly greater value to organizations, people and society than traditional approaches.

Blockchain-enabled processes alone are poised to generate over $3T in value globally in the next 30 years (SAP & Singularity University). As a disruptive technology, blockchain removes the central controlling figure in a large system or institution – banking being the classic example – which can lead to greater process efficiency and control. Supply chain applications based on private blockchains – tuna harvested in the Pacific Ocean or vehicles assembled in Mexico – offer greater accuracy, control and traceability than traditional logistics approaches. In a growing geopolitical world, these blockchains are also border-less, providing a greater flexibility to leverage data and process across what would be traditionally high-risk business environments.

Beyond blockchain, AI represents yet another vast and potentially limitless source of value. While many technologies exist based on AI algorithms – vehicle Advanced Driver Assist Systems (ADAS) renders the age-old art of parallel parking nearly obsolete – the growth and maturity of these systems will only accelerate.

AI is undergoing six levels of development, which began with rules-based systems in the 1950s with the first computer. Since that time predictive analytics have leverage patterns to rules to create highly-likely potential scenarios that could be acted upon. Today’s cognitive computing have applications not only in transportation systems such as ADAS but also in agriculture (robo-farming) and healthcare (robo-surgery) just as examples. The key point in this transition stage is the move from AI learning what “not to do” to a state of AI learning “what to do.” This only allows for greater advances in the area of so-called “Creative Machines” in the next decade as well early stage physical embodiment and eventual sentience of bio-robots and androids into the 2030s and beyond.

In the end it will be determined by how our machines of today will create the machines of tomorrow – AI creating the next AI – without the trepidation of Skynet and other human oriented fears portrayed in Hollywood movies.

This is the second of a three-part story. Part three: Leading for Exponential Organizations may be found here after it publishes. This article is cross-posted on LinkedIn, Medium and WordPress by the author.

 

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Leading for Exponential Leadership

Cabecera_AboutIEExponentialLearning

Recently I participated in the ThinkX program sponsored by SAP Innovation Partnership Program and Singularity University. The origins of ThinkX touch the xPrize competitions and other design events which seek to leverage exponential learning. Over the past 5 years, SAP and SU have hosted over 800 SAP executives, leaders and employees to events around the world. In this third article of a three-part series I will explore the big trends, themes and take-aways – including how they may impact industry and society in the coming years.

In the age of Exponential Leadership (XL), organizations will need to look at not just who are the high performers but also the synergies across individual members of teams, cohorts and groups to create exponential performance. The nature of leadership will change – in some obvious and in some very subtle ways. Management will be of less valuable to organizations as new leadership models emerge. This is however only the beginning.

The classical consideration of style and focus of leaders equipped with broad vision and the ability to “look around corners” will be more important than ever. However just as important is the ability to put the right “team on the field” and ensure that each of the players is performing to the expectations of that role but also to those of the other players on the field. For instance, hiring the best performer for the role in a traditional sense allows that individual a greater chance of success. But it may not necessarily be the skills needed to grow, expand and improve the overall performance of the team. Taking the team sport analogy further, just because you draft a great receiver doesn’t mean the team wins if the line can’t block and the passer can’t throw accurately. The move from “hiring the best person for the job” to “hiring the best person for the team” is at the heart of this shift.

Additionally, humans will need to consider the impacts of technology on their team-based activities and interactions. While Artificial Intelligence (AI) and Machine Learning (ML) will address some near-term repetitive tasks, the larger more cognitive space of bio-robots may suggest that more sentient devices will play meaningful roles in teams and groups in the future – as early as the second half of this century. This will also have an impact on how we form teams, the needs and performance of those teams, and the relationships between team members in ways we have yet to fully imagine.  Manufacturing companies are already embracing some of these early shifts in the war for talent, as Millennials tend to group perform versus individually perform – in addition to Digital Natives (those born after Millennials) who have great comfort interfacing and working with intelligent machines.

My colleagues Claudia Mandelli and Gina Nodar recently wrote about human – AI workplace impacts in Forbes article Meet the New Co-workers who Won’t be Joining you for Happy Hour. Coming sooner to a workplace near you than perhaps you expected.

This is the third of a three-part story. Part One and Part Two are also available. This article is cross-posted on LinkedIn, Medium and WordPress by the author.

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Exponential Learning will Govern How we Work and Grow

Foundations_Of_Expontential_Thinking_Tile

Photo: Singularity University

Recently I participated in the ThinkX program sponsored by SAP Innovation Partnership Program and Singularity University. The origins of ThinkX touch the xPrize competitions and other design events which seek to leverage exponential learning. Over the past 5 years, SAP and SU have hosted over 800 SAP executives, leaders and employees to events around the world. In this first article of a three-part series I will explore the big trends, themes and take-aways – including how they may impact industry and society in the coming years.

What makes great companies outperform their peers in industry? One answer is in exponential learning. Exponential learning allows companies to grasp new ideas, concepts and techniques and to apply them in non-linear, exponential fashion. An exponential organization is an organization that can leverage this learning and create results that are 10x – an order of magnitude – greater than their peers across key performance areas. The changes required to perform at these levels will necessitate changes in leadership as well. Exponential leadership requires a broad vision across industries in anticipation of disruptions and the creation of new white space opportunities.

In the hospitality space, Airbnb has leveraged 90x listings per employee by their disruption in the short term, vacation rental segment. In automotive, Tesla achieves 30x market cap per employee (based on data from earlier in 2019) compared to its traditional automotive peers. Platform Maker and start-up Local Motors has discovered its ability to create a vehicle platform 1000x less expensive than its traditional automotive peers while improving time to market by 5-22x based on the vehicle configuration (source: Singularity University). Learning leads to leadership, which leads to performance.

Exponential leadership in many ways addresses the so-called innovator’s dilemma. Traditional companies are terrible at generating disruption and don’t typically respond well particularly to new market disruptions. As such, innovators need to be aware of what’s happening in lateral and adjacent industries in ways not seen before. In automotive and discrete manufacturing, the trend to smart products, products as a service, and consumption-based models – all enabled by sensor driven technologies and big data – is driving a shift from the “industrial revolution” through the “information revolution” and into an “artificial intelligence (AI) economy.”

Exponential performance is initially difficult to gauge and monitor, and in fact it can appear that a company may be failing compared to other traditional business models. While exponential performance starts deceptively slow, it can continue and accelerate at a tremendously fast pace. Returning to Tesla, the company’s desire to move to production tooling without a conventional prototype phase in favor of a “fail fast” and “experimentation mentality” typical of high tech and aerospace made no sense when viewed through the lens of a traditional automaker operating model. However, while the leap was painful for Tesla, the savings in time to market – and the gains in results in net promoter score (NPS) and near-term market cap previously noted are impressive.

The big “show down” between exponential organizations and traditional organizations may be netted out in a few broad areas, which when applied to companies in a given market segment or group of segments provides separation from winners and losers.

  • Growth vs margins – exponential companies like Amazon look for first mover growth areas and once dominated expect margin growth to follow.
  • Learning vs Outcomes – returning to exponential learning, teams need to understand how to learn in a new exponential reality, versus focusing on traditional quarterly outcomes.
  • “Pipes” vs “Platforms” – the ability to leverage multipliers between ideas, concepts, and products – the connectors or “pipes” that create synergy – are giving away to the more powerful, integrated platform. Platforms that aggregate value and create ease of use will serve as the basis for exponential performance.

In the end there is a big challenge: what do we need to unlearn as individuals and organizations in order to move from a traditional model to one that embraces exponential learning, exponential leadership and – ultimately – exponential performance.

This is the first of a three-part story. Part two: AI will Eat Software may be found here after it publishes. This article is cross-posted on LinkedIn, Medium and WordPress by the author.

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Automotive Service Leaders: Accelerating Growth In Aftermarket And Customer Care

280945_GettyImages-683745421_super_lowThe automotive aftermarket is expected to reach a segment size of $1.2 trillion globally by the year 2030 (McKinsey, 2016). As such, customer care and service leaders need to prepare for this growth and capture the opportunities that come from higher vehicle retention by owners, larger numbers of vehicles in operation (VIO), and the introduction of highly available and highly utilized connected and autonomous vehicles of many types and models, from heavy trucks to scooters.

The challenges facing customer care and service leaders are many. First, leaders must provision a meaningful and exceptional customer experience. Brands of all kinds exist and do it yourself (DIY) vehicle owners and do it for me (DIFM) service provider have many choices when it comes to recommending repairs and service parts. Often, leaders are faced with suboptimal business processes to conduct operations. From the very small, family-owned “mom and pop jobbers” who run entire businesses on clipboards and spreadsheets, to very large brand dealerships that often toggle between service repair and inventory applications, lack of process automation inhibits increased productivity. Third, DIY parts providers and DIFM service providers are often ill-prepared to take advantage of new business models, products, and services because they live in the “now” of today’s sales and service environment. With few assets to plan for the future, leaders are highly reactive to change around them. And finally, this inertia breeds a certain lack of speed and agility. It is very difficult to adapt to the changing needs of the customer even though this market shift is paramount.

Customers are always considered to be buyers, regardless of their position on the continuum of the customer care journey. The move between consideration and buying has blended through ownership cycles with many different use and service models. Add to this the impact that customers have to influence via social sentiment and word of mouth, and it is clear leaders have a responsibility to manage a customer lifetime value (CLV) throughout a brand, often representing hundreds of thousands of dollars.

The good news for leaders is that customer care and service organizations can be the catalyst for driving future growth. The sales arm has seen and will continue to see an uptick in the use of leads from digital sources, whereby most sales leads will come from online and digital sources within the next two years. Services margins will continue to rise through the use of advanced diagnostics, preventative maintenance, greater customer intimacy, and inventory visibility. And while total revenue will continue to climb as vehicles require greater frequency of repair due to greater utilization and part value, rises in customer satisfaction (CSAT) scores have the ability to drive down the average cost of services. In the future, margin will drive key gains for leaders in the customer care and service space.

For service leaders, a focus on experience data and management allows them to move to a cycle of continues delivery and improvement, relying on greater agility and data visibility – into both the vehicle and how it is used as well as the personal habits of the driver and consumer. Service leaders must continue to experiment and move beyond their comfort zones to take advantage of the opportunities the future holds in the area of customer care.

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The 4 Ways of CASE and Disintermediation the Automotive Landscape

1 BrjY-vWtfOqUZgUj56-e2A@2xThe introduction of CASE (Connected, Autonomous, Shared, Electrified) technologies has created a new set of choices for automotive companies. Each company — regardless of their origins as Silicon Valley start-up or Detroit iconic stalwart — needs to determine which set of capabilities it will need to be successful in its quest to get closer to the customer. And with customer sentiments moving from owning vehicles to consuming vehicles based on subscription models (allowing greater choice of vehicle, time and use options), automotive companies are putting bets on where to play, with whom to partner and how to go about doing so.

In our work with companies at SAP, I find that a number of roles are emerging to consider what business model — and resulting capabilities would be needed — to win in the new automotive world. While many companies (particularly start-ups) have predefined growth and exit strategies, many companies are still trying to forge ahead in the brave new world. Some have figured out their initial direction and some are still working on it with the help of partners. In one recent meeting I was asked what did we do in the connected vehicle space. My answer at the end of the day is a flip question: what does an automotive company want to do in terms of brand, operating companies, partnerships and technologies to get closer to the new automotive customer?

From my perch I see four ways CASE is already leading to the disintermediation of the automotive industry. It’s a heady time and companies are trying to understand where they fall into the new roles and strategies based on past and future roles that automotive companies can — and should — play.

  1. Traditional Auto Maker. These are the traditional brands that build cars, for both traditional personal consumer use as well as enabled with CASE technologies which may look (and drive) a lot differently. In this case an automaker may elect to build cars for a Fleet Operator, as in the case of Fiat-Chrysler who builds a lot of Pacifica minivans for the Google Waymo fleet of robtaxis. In this model, traditional OEM capabilities continue to be important, with focus on revenue, margin and Operational Excellence. Captive finance options — the ability to up-sell and out-sell to customers within the brand ecosystem — is very important.
  2. Platform Maker. These may be new start-up and growth companies like Zoox or traditional companies like Honda that are creating new platforms or are designing products around very well defined CASE components (such as BEV and other electrified propulsion and power systems). Platform makers may or may not opt to actually build cars. Interior design and instrumentation company IDEO is a good example, defining the elements which are distinct in a platform and working with traditional automakers like Ford to express and build the platform. When and if Platform Makers do commit to actually build a vehicle, there can be a steep learning curve (as in the case of Tesla, which literally had to learn to build vehicles at scale after designing and building successful low volume platforms). Low cost, start-up mentality, often pre-revenue or early revenue often drives behavior and capabilities needs. This can also include high touch, 1:1 configurations for Fleet Operators as well as personal vehicle owners (a good example of this is uber-lux maker Karma Automotive).
  3. Fleet Operators. While this may sound not as bourgeois as the makers, Fleet Operators are closest to the customer which makes them the kings of the ecosystem. Fleet Operators can certainly be brands within automakers (GM Maven is a great example) or customers / partners of Platform Makers (Google Waymo has already racked up millions of vehicle miles traveled — VMT — as part of their robotaxi fleet). Fleet Operators can offer subscription access to a variety of make and model vehicles in an automaker’s line-up or look more traditional like the next generation rental car company (think ZipCar). Asset management, high vehicle use maintenance (up to 90% over time), predictive fleet MRO are all major operating concerns. Fleet Operators also have a unique advantage to be able to harvest and manage both vehicle use data as well as customer use data while using fleet assets (as privacy allows). This means that Fleet Operators are also well positioned to be Information Brokers.
  4. Information Brokers. While the expression might conjure images of rows of data servers in a large raised-floor room humming under dimming lights, one thing is certain: data is the new oil. Information Brokers (as privacy allows, don’t forget GDPR) can monetize vehicle use and occupant information for many lateral industries such as finance and insurance, transportation and logistics, smart city grids and vehicle to infrastructure (V2I) applications, and many other personal customer experience (CX) applications. Information Brokers are very focused on big data operations (think Petabytes weekly per origin of data), data analytics, and product creation based on intended audience(s). Tuck-in opportunities for third party data sources to round out and enhance data already collected offer many commercial possibilities, as in the case of insurance companies tracking operator behavior and vehicle use.

There is of course the big wild card in all of this — what is the next business model that has not been created that we can’t forecast. The many kinds and fashions of roles automotive companies will take on as CASE technologies mature over the next 20 years are simply unimaginable. However, based on where we sit heading into 2019, automotive companies will choose one or more of the business models available today and craft their strategies to be successful in in the market tomorrow.

This blog was originally published in Medium and LinkedIn.

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Aftermarket 4.0: From Digital Process to Digital Products

AASA AIM Keynote 2019

Inaugural keynote for the Aftermarket Innovation Mobility council (source: AASA)

In early October, the Third Annual Digital Aftermarket Study was released by SAP and the Automotive Aftermarket Supplier Association (AASA).  The key findings were presented at the AASA Technology Conference with a focus on digital process improvements in the areas of customer centricity, supply chain planning and inventory management.  In addition to examining the digitization of process in the automotive aftermarket segment, this year’s study also examined for the first time the digitization of aftermarket products, or what industry watchers have referred to as the “shift from hard parts to smart parts.” We are calling this shift Aftermarket 4.0 consistent with the general trends across the industry and its sectors, including light passenger vehicle, heavy and commercial vehicle and through the supply base.

To emphasis this shift, an inaugural meeting of the Aftermarket Mobility council with general interest of the supply base participating in this space was held, and SAP was asked to participate in the keynote for this event.  Why does this matter to SAP?  The Aftermarket 4.0 space aligns to the following key pillars of automotive strategy for SAP and its clients:

  1. Customer centricity means the shift from personally owned vehicles to those enabled through CASE (Connected Autonomous Shared Electrified) technologies and operated in fleets. Forecasts for personally owned vehicle sales from IHS Markit, Frost & Sullivan, and McKinsey views traditional vehicle growth rates of +2-4% until 2030. Yet overall vehicle use will explode. Digitizing aftermarket for high usage fleets represents a +$500 Billion opportunity for aftermarket (McKinsey, 2016 – based on current vehicle forecast models)
  2. Digital Smart Products – enabling these CASE technologies – will force the shift from hard parts to smart parts. This year’s aftermarket study illustrated that over 70% of AASA members have begun to plan for products leveraging CASE technologies and 60% of members intend to reflect those in their business demand models in the next five years with 40% already reflecting this change today.
  3. Digitizing the Supply Chain remains a very hot process for aftermarket with significant improvement potential in both inventory and logistics. The ability to manage forecasts, inventory levels, and service levels consistently on an “when needed, where needed” basis. Particularly at the distribution center and warehouse level of the segment, this time criticality remains essential.
  4. The Changing Workforce will shift how we do our business from diagnostics to part tracking to basic work place organization. It will also impact the driver interest in using the products with more do it for you (DIFY) fleet service versus do it yourself (DIY) as more and more vehicle users drive less and less for pleasure.
  5. New Business Models will impact every aspect of the aftermarket business. Digital services to and from the vehicle will represent a $1.2 trillion opportunity by 2030 according to McKinsey (2016). SAP engaged 18 months ago with Mojio – an application and services delivery platform which leverages vehicle use data. This in combination with other digital platform software makers elevated SAP to status as a tier-2 automotive software product supplier. As such, the shift to Aftermarket 4.0 will happen whether we plan for it or not, with the winners being the companies that actively plan for the shift in the next five years.

To listen to a full play-by-play of the Aftermarket 4.0 shift, AASA Vice President Chris Gardner and I joined host Bonnie D. Graham in a post-event SAP Radio program.  You can listen to the show here and join the conversation in the comments section.

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Automotive Industry Gathers Under Clouds of Uncertainty, yet Optimistic about the Future

Last week’s passing of automotive legend Sergio Marchionne, former CEO of Fiat Chrysler, illustrated how quickly the shifts of automotive futures can truly be. Less than a week on the job, his successor – British American Mike Manley, head of the successful Dodge and Jeep brands – led the company earnings call which was more a somber wake than a call to action for the company five-year plan. Analysts mourned and tributes poured in. It was a surreal moment that touched many in the industry.

PHOTO: BILL PUGLIANO/GETTY IMAGES VIA WSJ.

While FCA dealt with their own succession issues, GM and Ford also provided earnings calls that – while strong in terms of sales forecasts – left a lot of uncertainty on the table in the face of margin resistance stemming from the US administration move on EU and China aluminum and steel import tariffs.

It is under these clouds of uncertainty – which manifested Sunday in physical form, canceling an always popular golf outing – which the industry convenes for the annual Center for Automotive Research (CAR) Management Briefing Seminars in Traverse City, Michigan this week.

The three things that attendees should be focused on as the industry continues to transition to new business models in preparation for the industry shift to connected and autonomous vehicles range from tactical to strategic to regulatory. Going from back to front:

  1. EPA and Tariffs provide uncertainty. Look for clues from speakers about the shift in EPA regulatory standards (big announcement seems to always be a week away) which could roll back some Obama era requirements for overall brand fleet fuel efficiency levels. As noted earlier the dynamic positing on steel and aluminum tariffs make for active discussion particularly now that two American based automotive stalwarts have shot cautionary earnings messages over Washington and the markets.
  2. New business models taking hold. While each automaker will arrive at their own business model for their operations moving forward, a common response emerging. Last week, Daimler and Ford joined GM, Toyota and other OEMs in declaring a three-pronged business model. In this business model, companies wil move to create new entities and/or operations to govern connected vehicles, mid-size to heavy commercial vehicles, as well as the traditional model of light passenger vehicles built for personal consumer use. Each segment demands certain key requirements in support of these different business models. It will be interesting to hear industry leaders address their businesses in these terms.
  3. Talent management and succession planning. While the automotive industry continues in its quest to attract and retain highly skilled talent, the passing of Mr. Marchionne conveys a renewed importance in active and current transition planning. While executive transition planning is common, going deeper into the organization to work through the parabolic combinations of how an organization – with deaths, retirements and new business models – will function is now more important than ever. Watch to see if more comment is made on this topic by industry leaders.
  • What do you think will be the big topics discussed this week? Leave a comment on this post. As always follow our blog and event news #CARMBS.
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