Author: Yugal Joshi

Busting Four Edge Computing Myths | Blog

Interest in edge computing – which moves data storage, computing, and networking closer to the point of data generation/consumption – has grown significantly over the past several years (as highlighted in the Google Trends search interest chart below). This is because of its ability to reduce latency, lower the cost of data transmission, enhance data security, and reduce pressure on bandwidth.

Interest over time on Google

 

But, as discussions around edge computing have increased, so have misconceptions around the potential applications and benefits of this computing architecture. Here are a few myths that we’ve encountered during discussions with enterprises.

Myth 1: Edge computing is just an idea on the drawing board

Although some believe that edge computing is still in the experimental stages with no practical applications, many supply-side players have already made significant investments in bringing new solutions and offerings to the market. For example, Vapor IO is building a network of decentralized data centers to power edge computing use cases. Saguna is building capabilities in multi access edge computing. Swim.ai allows developers to create streaming applications in real time to process data from connected devices locally. Leading cloud computing players, including Amazon, Google, and Microsoft, are all offering their own edge computing platform. Dropbox formed its edge network to give its customers faster access to their files. And Facebook, Netflix, and Twitter use edge computing for content delivery.

With all these examples, it’s clear that edge computing has advanced well beyond the drawing board.

Myth 2: Edge computing supports only IoT use cases

Processing data on a connected device, such as a surveillance camera, to enable real-time decision making is one of the most common use cases of edge computing. This Internet of Things (IoT) context is what brought edge computing to the center stage, and understandably so. Indeed, our report on IoT Platforms highlights how edge analytics capabilities serve as a key differentiator for leading IoT platform vendors.

However, as detailed in our recently published Edge Computing white paper, the value and role of edge computing extends far beyond IoT.

Edge computing

For example, in online streaming, it makes HD content delivery and live streaming latency free. Its real-time data transfer ability counters what’s often called “virtual reality sickness” in online AR/VR-based gaming. And its use of local infrastructure can help organizations optimize their web sites. For example, faster payments processing will directly increase an e-commerce company’s revenue.

Myth 3: Real-time decision making is the only driver for edge computing

There’s no question that one of edge computing’s key value propositions is its ability to enable real-time decisions. But there are many more use cases in which it adds value beyond reduced latency.

For example, its ability to enhance data security helps manufacturing firms protect sensitive and sometimes highly confidential information. Video surveillance, where cameras constantly capture images for analysis, can generate hundreds of petabytes of data every day. Edge computing eases bandwidth pressure and significantly reduces costs. And when connected devices operate in environments with intermittent to no connectivity, it can process data locally.

Myth 4: Edge spells doom for cloud computing

Much of the talk around edge computing presents that the current cloud computing architecture is not suited to power new age use cases and technologies. This has led to attention grabbing headlines about edge spelling the doom of cloud computing, with developers moving all their applications to the edge. However, edge and cloud computing share a symbiotic relationship. Edge is best suited to run workloads that are less data intensive and require real-time analysis. These include streaming analytics, running the inference phase for machine learning (ML) algorithms, etc. Cloud, on the other hand, powers edge computing by running data intensive workloads such as training the ML algorithms, maintaining databases related to end-user accounts, etc. For example, in the case of autonomous cars, edge enables real-time decision making related to obstacle recognition while cloud stores long-term data to train the car software to learn to identify and classify obstacles. Clearly, edge and cloud computing cannot be viewed in exclusion to each other.

To learn more about edge computing and to discover our decision-making framework for adopting edge computing, please read our Edge Computing white paper.

Application Modernization for Digital Transformation: The Rise of Good Technical Debt | Blog

Many organizations today treat technical debt like a pariah. They equate it with legacy systems, worry about how subsequent changes will be complex, time consuming, risky, and cost prohibitive, and consider it something that should be avoided in their journey to becoming a digital enterprise.

What they do not realize is that the debt is not bad in and of itself. Indeed, because speed-to-value is critically important in digital businesses, teams may intentionally take planned shortcuts in order to accomplish the task as quickly and responsibly as possible. As long as the teams understand what they are doing and compromising on, and have suitable plans to address it soon, assuming this debt can be a smart move.

Where enterprises err with technical debt is poorly managing it.

In order to manage it suitably and safely in a digital transformation environment, they should classify it into five major buckets.

The Rise of Good Technical Debt

Planned debt

This is when people knowingly become indebted. It is like buying a house on a bank loan. You know you must repay the loan, and you plan for it accordingly. The defining feature of this type of debt is that the team knows it has the capabilities and resources to “pay” it back. This is a good debt that helps you quickly achieve business objectives.

Blind debt

This is a dangerous debt where system teams do not even know they are building the debt themselves. This is generally the result of poor practices within the team, unplanned and haphazard development, and a fundamentally broken organizational culture. This often also happens during M&As when the acquirer does not know what kind of mess it is getting into.

Acquired debt

This type of debt is unavoidable in business environments. Many systems that were developed in the past with improper technology platforms, tools, coding practices, governance models, and frameworks build technical debt over time. These legacy systems hold valuable information for enterprises aspiring to become digital businesses, and cannot simply be jettisoned. Instead, they need to be made “debt free” in a prioritized manner.

Dead debt

This is probably the worst of all kinds, because, irrespective of corrective measures taken, the systems have degraded so far that they do not support digital initiatives. Therefore, rip and replace becomes the only option. Enterprises need to be careful with identifying this debt as they may confuse it with other types of debt that can be “repaid.” They may end up spending good money after bad, with no way out.

Mirage debt

Not many enterprises think about this one. It appears during system analysis, when architects and others mistakenly believe they have technical debt, when in reality they do not. If there is any, it is in small components, not the system itself.

What should enterprises do to address technical debt?

They should start by understanding that modernization should be of system components, not the systems themselves. Then, they should look at each of their systems and identify the components that can meet future digital demand, and those that could potentially create problems. Once they have catalogued all the components, they need to invest in reducing each one’s technical debt in the most appropriate way. For example, we have seen enterprises successfully build component capabilities outside the main system and exposing APIs for backward integration. This can work across core functionalities as well as user interfaces.

Our research with over 190 application leaders suggests that 75 percent plan to continue to invest and modernize their applications. There is no reason to fear technical debt as long as you understand what you are getting into. For digital businesses, taking on good technical debt can be a strategic choice. Though processes have their value, enterprises that are driven by processes rather than innovation, and are scared of risking short-term technical debt, will struggle in the digital world.
What has been your experience with application modernization? Please share with me at [email protected].

Digital Transformation: The Perils of the “Get Digital Done” Culture | Blog

The “Just-in-time” methodology focuses on achieving an outcome through defined structured processes that also build organizational capabilities. “Somehow-in-time” focuses on somehow achieving an outcome, irrespective of the impact it has on the broader enterprise.

Most enterprises reward leaders who embrace “get it done” approaches. Unfortunately, the ideology is becoming part and parcel of more enterprises’ digital transformation initiatives. And while “get it done” may seem like a glamorous virtue, it is detrimental when it comes to digital.

Get Digital Done Doesn’t Build Organizational Capabilities

Everest Group research suggests that 69 percent of enterprises consider the operating model a huge hindrance to digital transformation. Leaders are in such a hurry to achieve the intended outcomes that they neglect building a solid operating model foundation that can enable the outcomes on a consistent basis across the enterprise. This leaves each digital initiative scampering to somehow find resources, somehow find budgets, and somehow find technologies to get it done. And because no new organization capability – think digital vision, talent, or leadership – is developed – these initiatives do not help build sustainable businesses.

Get Digital Done Rewards the Wrong Behavior and People

Much like enterprises’ fascination with “outcome at all costs” creates poor leaders, digital transformation initiatives are plagued with the wrong incentives for the wrong people. Our research suggests that 73 percent of enterprises are failing to get the intended value from their digital initiatives. The key reason is while the leaders are expected to “somehow” complete them, there is no broader strategic agenda for them to scale it beyond their own fiefdoms. Our research also indicates that while enterprises want to drive digital transformation, 60 percent of them lack a meaningful digital vision. They’re obsessed with showing outcomes, and cut corners to achieve them. They take the easier way out to get quick ROI, instead of getting their hands dirty and addressing their big hairy problems.

Get Digital Done Does not Align People towards Common Goals

Obsession with outcomes makes leaders leverage their workforce as “tools” for a project rather than partners in success. Because the employees are not given a meaningful explanation of the agenda and the impact, they become execution hands rather than people who are aligned towards a common enterprise objective. This ultimately causes the initiative to fail. No wonder our research indicates that 87 percent of enterprises that fail to implement change management plans see their digital initiatives fail.

To succeed in their digital transformation journeys, enterprises must put their “get it done” obsession away in a locked drawer and focus on three critical areas:

  • Build a digital foundation: Although easier said than done, this requires a revamp of internal communication, people incentives, and a shared vision of intended goals. Each business unit should have a digital charter that aligns with the corporate mandate of leading in the tech-disrupted world. And it requires strategic, yet nimbler, choices on technology platforms, market channels, brand positioning, and digital vision.
  • Have realistic timelines: Expectation of quick ROI is understandable. However, a crunched timeline can backfire. Enterprises must work towards a pragmatic timeline, and incentivize their leaders to meet it without bypassing any fundamental processes.
  • Involve different stakeholders: Our research shows that a shocking 82 percent of enterprises believe they lack the culture of collaboration needed to drive digital transformation. That means the initiatives become the responsibility of just one leader or team. And that simply won’t work. Instead of driving everything independently, the leader or team should be an orchestrator of the organization’s capabilities. This is the key reason more enterprises are appointing a Chief Digital Officer, as one of that role’s key responsibilities is serving as the orchestrator. Additionally, the team needs to leverage the organization’s current capabilities, and enhance them for the future. It should build a charter for its digital transformation initiative that includes impact on fundamental organizational capabilities such as talent, business functions, compliance, branding, and people engagement.

In their race to “get it done” and appease their end customers, enterprises have forgotten the art of building organizational capabilities that will sustain them in the future and create meaningful competitive advantage. And they can’t succeed unless they change their approach and ideology.

Does your organization have a “get it done” culture, or has it built the right organizational capabilities to achieve true transformation with digital? Please share with me at [email protected].

Stop Trivializing AI: It Is Not Just Automation | Blog

AI is certainly being used to attempt to solve many of the world’s big problems, such as health treatment, societal security, and the water shortage crisis. But Everest Group research suggests that 53 percent of enterprises do not – or are not able to – differentiate between AI and intelligent automation and what they can do to help them compete and grow. This trivialization of AI is both eye opening and frustrating.

While it’s true that automation of back-office services is one strong case for AI adoption, there are many more that can deliver considerable value to enterprises. Examples we’ve researched and written about in the past year include intelligent architecture, front-to-back office transformation, talent strategies, and AI in SDLC.

It’s been said that “audacious goals create progress.”  So, how should enterprises think more creatively and aspirationally in their leverage of artificial intelligence to extract real value? There are three ingredients to success.

Think Beyond Efficiency

Enterprises are experimenting with AI-driven IT infrastructure, applications, and business services to enhance the operational efficiency of their internal operations. We have extensively written about how AI-led automation can drive 10-20 percent more savings over traditional models. But enterprises have far more to gain by experimenting with AI to fundamentally transform the entire landscape, including product design customer experience, employee engagement, and stakeholder management.

Think Beyond CX

Most enterprises are confusing putting bots in their contact center with AI adoption. We discussed in an earlier post that enterprises need to get over  their CX fixation and drive an ecosystem experience with AI at the core. Our research suggests that while 63 percent of enterprises rank CX improvement as one of their top three expectations of artificial intelligence, only 43 percent put newer business model among their top three. We believe there are two factors behind this discouraging lack of aspiration: market hype-driven reality checks (which are largely untrue), and enterprises’ inability to truly grasp the power of AI.

Think Beyond Bots

While seemingly paradoxical, humans must be central to any AI adoption strategy. However, most enterprises believe bot adoption is core to their AI journey. Even within the “botsphere,” they narrow it down to Robotic Process Automation (RPA), which is just one small part of the broader ecosystem. At the same time, our research shows that 65 percent of enterprises believe that AI will not materially impact their employment numbers, and that bodes well for their realization of the importance of human involvement.

And, what do enterprises need to do?

Be Patient

Our research suggests that 84 percent of enterprises believe AI initiatives have a long gestation period, which undoubtedly leads to the business losing interest. However, given the nature of these technologies, enterprises need to become more patient in their ROI expectation from such initiatives. Though agility to drive quick business impact is welcome, a short-sighted approach may straight jacket initiatives to the lowest hanging fruits, where immediate ROI outweighs longer term business transformation.

Have Dedicated AI Teams

Enterprises need AI champions within each working unit, in appropriate size alignment. These champions should be tech savvy people who understand where the AI market is going, and are able to contextualize the impact to their business. This team needs to have evangelization experts in who can talk the language of technology as well as business.

Hold Technology Partners Accountable

Our research suggests that ~80 percent of enterprises believe their service partners lack the capabilities to truly leverage artificial intelligence for transformation. Most of the companies complained about the disconnect between the rapid development of AI technologies and the slowness of their service partners to adopt. Indeed, most of these partners sit on the fence waiting for the technologies to mature and become enterprise-grade. And by then, it is too late to help their clients gain first-mover advantage.

As AI technologies span their wings across different facets of our lives, enterprises will have to become more aspirational and demanding. They need to ask their service partners tough questions around AI initiatives. These questions need to go far beyond leveraging AI for automating mundane human tasks, and should focus on fundamentally transforming the business and even creating newer business models.

Let’s create audacious goals for artificial intelligence in enterprises.

What has been your experience adopting AI beyond mundane automation? Please share with me at [email protected].

AI for Experience: From Customers to Stakeholders | Sherpas in Blue Shirts

Everest Group’s digital services research indicates that 89 percent of enterprises consider customer experience (CX) to be their prime digital adoption driver. But we believe the digital experience needs to address all stakeholders an enterprise touches, not just its customers. We touched on this topic in our Digital Services – Annual Report 2018, which focuses on digital operating models.

Indeed, SAP’s recent acquisition of Qualtrics and LinkedIn’s acquisition of Glint indicates the growing importance of managing not only CX, but also the digital experience of employees, partners, and the society at large.

AI Will Usher in the New Era of the Digital Experience Economy

Given the deluge of data from all these stakeholders and the number of parameters that must be addressed to deliver a superior experience, AI will have to be the core engine powering this digital experience economy. It will allow enterprises to build engaging ecosystems that evolve, learn, implement continuous feedback, and make real time decisions.

 

Exhibit experience economy AI blogAI’s Potential in Transforming CX is Vast

Today, most enterprises narrowly view the role of AI in CX as implementing chatbots for customer query resolution or building ML algorithms on top of existing applications to enable a basic level of intelligence. However, AI can be leveraged to deliver very powerful experiences including: predictive analytics to pre-empt behaviors; virtual agents that can respond to emotions; advanced conversational systems to drive human-like interactions with machines; and even to deliver completely new experiences by using AI in conjunction with other technologies such as AR/VR, IoT, etc.

Digital natives are already demonstrating these capabilities. Netflix delivers hyper personalization by providing seemingly as many versions as its number of users. Amazon Go retail stores use AI, computer vision, and cameras to deliver a checkout free experience. And the start-up ecosystem is rampant with examples of cutting-edge innovations. For instance, HyperSurfaces is designing next-gen user experiences by using AI to transform any object to user interfaces.

But focusing just on the customer experience is missing the point, and the opportunity.

 AI in the Employee Experience

AI can, and should, play a central role in reimagining the employee journey to promote engagement, productivity, and safety. For example, software company Workday analyzes 60 data points to predict attrition risk. Humanyze enables enterprises to ascertain if a particular office layout supports teamwork. If meticulously designed and tested, AI algorithms can assist in employee hiring and performance management. With video analytics and advanced algorithms, AI systems can ensure worker safety; combined with automation, they can free up humans to work on more strategic tasks.

AI in the Supplier and Partner Experience

Enterprises also need to include suppliers and other partners in their experience management strategy. Using predictive analytics to automate inventory replenishment, gauge supplier performance, and build channels for two-way feedback are just a few examples. AI will play a key role in designing systems that not only pre-empt behaviors/performance but also ensure automated course correction.

AI in the Society Experience

Last but not least, enterprises cannot consider themselves islands in the environment in which they operate. They must realize that experience is as much about reality as about perception. Someone who has never engaged with an enterprise may have an “experience” perception about that organization. Some organizations’ use of AI is clearly for “social good.” Think smart cities, health monitoring, and disaster management systems. But even organizations that don’t have products or services that are “good” for society must view the general public as an important stakeholder. For example, employees at Google vetoed the company’s decision to engage with the Pentagon for use of ML algorithms for military applications. Similarly, employees at Microsoft raised concerns over the company’s involvement with Immigration and Customs Enforcement in the U.S.  AI can be leveraged to predict any such moves by pre-empting the impact that a company’s initiatives might have on society at large.

Moving from Customer to Stakeholder Experience

As organizations make the transition to an AI-enabled stakeholder experience, they must bear in mind that a piecemeal approach will not work. This futuristic vision will have to be supported by an enterprise-wide commitment, rigorous and meticulous preparation of data, ongoing monitoring of algorithms, and significant investment. They will have to cover a lot of ground in reimagining the application and infrastructure architecture to make this vision a distinctive reality.

What has been your experience leveraging AI for different stakeholders’ experiences? Please share with us at [email protected] and [email protected].

 

Using AI to Build, Test, and Fight AI: It’s Disturbing BUT Essential | Sherpas in Blue Shirts

Experts and enterprises around the world have talked a lot about the disturbing concept of AI being used to build and test AI systems, and challenge decisions made by those systems. I wrote a blog on this topic a while back.

Disquieting as it is, our AI research makes it clear that AI for AI with increasingly minimal human intervention has moved from concept to reality.

Here are four key reasons this is the case.

Software is Becoming Non-deterministic and Intelligent

Before AI emerged, organizations focused on production support to optimize the environment after the software was released. But those days are going to be over soon, if they aren’t already. The reality is that today’s increasingly dynamic software and Agile/DevOps-oriented environments require tremendous automation and feedback loops from the trenches. Developers and operations teams simply cannot capture and analyze the enormous volume of needed insights. They must leverage AI intelligence to do so, and to enable an ongoing interaction channel with the operating environment.

Testing AI Biases and Outcomes is not Easy

Unlike traditional software with defined boundary conditions, AI systems have very different edge scenarios. And AI systems need to negate/test all edge scenarios to make sense of their environment. But, as there can be millions of permutations and combinations, it’s extremely difficult to manually assure or use traditional automation to test AI systems for data biases and outcomes. Uncomfortable as it may be, AI-layered systems must be used to test AI systems.

The Autonomous Vehicle Framework is Being Mirrored in Technology Systems

The L0-L5 autonomous vehicle framework proposed by SAE International is becoming an inspiration for technology developers. Not surprisingly, they want to leverage AI to build intelligent applications that can have autonomous environments and release. Some are even pushing AI to build the software itself. While this is still in its infancy, our research suggests that developers’ productivity will improve by 40 percent if AI systems are meaningfully leveraged to build software.

The Open Source Ecosystem is Becoming Indispensable

Although enterprises used to take pride in building boundary walls to protect their IP and using best of breed tools, open source changed all that. Most enterprises realize that their developers cannot build an AI system on their own, and now rely on open source repositories. And our research shows that 20-30 percent of an AI system can be developed by leveraging already available code. However, scanning these repositories and zeroing in on the needed pieces of code aren’t tasks for the faint hearted given their massive size. Indeed, even the smartest developers need help from an AI intelligent system.

There’s little question that using AI systems to build, test, and fight AI systems is disconcerting. That’s one of the key reasons that enterprises that have already adopted AI systems haven’t yet adopted AI to build, test, and secure them. But it’s an inevitability that’s already knocking at their doors. And they will quickly realize that reliance on a “human in the loop” model, though well intentioned, has severe limitations not only around the cost of governance, but also around the sheer intelligence, bandwidth, and foresight required by humans to govern AI systems.

Rather than debating its merit or becoming overwhelmed with the associated risks, enterprises need to build a governing framework for this new reality. They must work closely with technology vendors, cloud providers, and AI companies to ensure their business does not suffer in this new, albeit uncomfortable, environment.

Has your enterprise started leveraging AI to build, test, or fight AI systems? If so, please share your experiences with me at [email protected].

Digital Initiatives Yielding Sour GRAPES? Gaps in Reality and Promises | Sherpas in Blue Shirts

GE’s search for a buyer of GE Digital, its apparent “non-core” business, and UBS’ sale of its Smart Wealth digital wealth management platform are causing the old guard to rejoice and claim that digital businesses are bogus and hogwash. Even Everest Group’s research suggests that 78 percent of enterprises fail to scale their digital initiatives, and don’t realize the benefits they envision.

It is easy to naysay the naysayers. But these developments do merit a discussion. Many enterprises are investing in digital transformation initiatives, and they have a lot to lose if they don’t do it well.

So, what is plaguing enterprises’ digital transformation agenda?

Not Moving the Revenue Needle

Most of the industrial enterprises we engage with as part of our research believe that, even in the coming two decades, 80-90 percent of their business will come from their so called “core” products. Though they acknowledge that their core products are not static and continue to be increasingly connected, software-driven, and service oriented, the incremental impact on revenue is not yet clear. Their business modeling and simulations provide numbers that are sufficient to fund digital initiatives, but are insufficient to move the revenue needle.

Digital Fatigue

Enterprises are realizing they have overdone some of their digital initiatives. Because business impact continues to be hazy, leadership is asking difficult questions. Our research suggests that 45 percent of enterprises fail to get funding for digital projects as the decision makers and purse string holders consider them vanity pursuits. Moreover, even strategic initiatives are struggling as the return on investment horizon is becoming longer as time progresses. Leadership is losing patience.

Challenges in CX to Business Attribution

Our research suggests that 89 percent of enterprises believe digital initiatives improve customer experience (CX). However, they struggle to attribute this improvement to business success. Therefore, business success becomes a secondary metric for such initiatives. Moreover, many enterprises confuse customer service – e.g., contact centers – with customer experience, which thwarts their ability to drive meaningful digital transformation.

We discuss another major reason for the gaps in digital promises versus reality in our research on digital operating models. Various enterprises assumed that digital transformation would create completely different businesses or business models for them. A prime example for comparison was about Google, a search and advertising company, getting into autonomous vehicles. Another was Amazon, an online retailer, getting into cloud services. These enterprises also assumed that they would disrupt their entrenched competition in their own and allied industries, just as Uber and Airbnb did.

Related: Important Lesson For Companies Undertaking Digital Transformation

However, I believe enterprises need such a dose of reality in order to separate the chaff from the wheat. As tech vendors, consultants, and system integrators brand everything digital, enterprises need a solid business case for digital transformation lest they spend precious money on worthless pursuits.

Enterprises’ needs of the hour are to develop a realistic digital transformation plan, rely on incubating multiple projects, be willing to fail fast, and leverage broader industry ecosystem. They must also remember that technology disruption always come with high risks.

Not acting is not an option, as the cost of doing nothing significantly outweighs the initial failures your enterprise may experience. Failing today is better than becoming irrelevant tomorrow.

What has been your digital journey experience? Please share it with me at [email protected].

The Paradox in the Industrial IoT Outcome Economy: Collaborating with Partners, Mistrusting Customers | Sherpas in Blue Shirts

The fundamental principle of the Industrial IoT (IIoT) outcome economy is that the customer pays for outcomes, not assets. One example is that a mining company would pay an equipment vendor for the amount of coal it mines, instead of buying the capital expensive equipment itself. Another example is a car tire company getting compensated for the fuel efficiency its tires deliver – the outcome – instead of selling the tires themselves. Both of these are already a reality in the world of IIoT.

We’re seeing an increasing number of these aspirational outcome economy examples in our IoT research. Out of 150 IIoT adoption use cases, 20 percent had some element of outcome-led engagement.

But we also see a major paradoxical challenge. In such engagements, the seller needs to strongly collaborate with its partners to deliver a product in an outcome model. At the same time, however, the seller needs to have a watertight contract with its customers to eliminate any type of legal liability. And yes, this, to a certain extent, equates to mistrusting customers.

For example, think about a company that provides drilling-as-a-service. Under the agreement, the customer pays, say, $10 per five holes drilled. In this scenario, the drilling-as-a-service company must meticulously define in a legal contract the size and depth of the hole, the type of surface, when a hole is considered “to pay” versus “not to pay,” maintenance charges, etc. This is an extremely complex contractual engagement, as compared to the general practice of a customer buying a drill and the seller booking the revenue in its books.

Four Key Issues to Address for Success

  1. Strong seller balance sheet: The cost of developing the asset itself – the drill, the tire – needs to be funded. As the seller is not selling the asset anymore, it needs to put this cost in its own balance sheet. This essentially implies that the seller must have a strong balance sheet depth; otherwise, the arrangement won’t work or will push the seller out of business. This depth of balance sheet requirement also holds true in cases in which the seller pays its partners – e.g., the company that supplies the rubber used in tires – in the “traditional” manner yet only gets paid by its customers based on outcomes
  2. Simple catalogue contracting: The customer must have a simple contract to go through, clear T&Cs, and a catalogue of offerings that are, and are not, available in an outcome model. Sellers should not try to customize their offerings or create innovative contracts, at least initially, as they won’t be scalable or sustainable
  3. Definition of outcome: Unless the seller clearly defines, in the contract, the outcome the customer is buying – and includes guardrails for scenarios that fall into gray areas –legal and financial issues will absolutely, without question, arise. And that would spell potential disaster not only for the given seller, but also for the promises of the outcome economy
  4. Use case identification: Some industrial assets are better sold under a traditional model – and some are well-capable of being delivered via an outcome-based model. Sellers must carefully examine which of their assets are appropriate for an as-a-service delivery model. Once they decide, they must educate their customers on what it is, how it works, and what its upsides and downsides are.

The IIoT outcome economy holds great promise. And if the above four issues are sufficiently addressed, it can succeed. But if sellers fail to do so, and, instead, treat their customers as just consumers instead of partners in the journey, they will be doing this once in a generation opportunity a great disservice.

Musings from AWS Summit: Make Infrastructure Irrelevant Again | Sherpas in Blue Shirts

I attended the Amazon Web Services (AWS) Summit in Mumbai earlier this month, and two things about the event itself really stood out. First, regardless of the fact that the Summit was held in India, it was organized on a global scale with global flavor, which ensured that attendees heard about AWS’ global aspirations and strategy. Second, although the company’s leadership rightly spoke about their great services portfolio and how and why it is the best, they never ridiculed or demeaned any competitor. This is a mark of a great company that’s in it for the very long haul.

Not surprisingly, the key message I could sense was that enterprises should not own their infrastructure, but instead leave it to cloud vendors – read, AWS – that will make sure it runs smoothly without the need for any second thoughts. In short, make infrastructure irrelevant.

Here are my three key take-aways from the content at the Summit.

I Learned: Partners Used to Sort of Matter…Now, They Really Matter

AWS has always positioned itself as a partner-friendly cloud vendor. At the Summit, its focus on succeeding with partners was very evident through the services it demonstrated and the messages it delivered. However, AWS’ current mindset is about building great services that enterprises would want to consume through pull demand, rather than through extensive leverage of channel partners. Thus, while partners today may not be as important as AWS may want them to think, they will be increasingly vital as AWS further expands to enterprise-class customers. This means it will be in AWS’ best interest to nurture its relationships with its partners.

I Re-learned: On-premise is Here to Stay…Cloud or No Cloud

AWS is a smart company that realizes there will always be a case for certain enterprise workloads to remain on-premise. The Summit sponsor was VMware, the king of on-premise. With its “VMware on AWS” offering becoming available globally, VMWare and AWS need each other. Though AWS largely stayed away from embracing “hybrid is the model of future,” it did reluctantly admit that all enterprise data centers aren’t going anywhere. However, AWS plans to make enterprises’ journey to the cloud simple and seamless. Its strong partnership with VMware is a testimony to that.

I Un-learned: New Services Have Miles to Go…Which They Will

From DynamoDB to serverless to AI/ML services, AWS shined a spotlight on everything new. While most of its new services are witnessing massive double – even up to 5X – growth, they aren’t yet meaningfully contributing to AWS’ US$18 billion top line. Most of its business continues to be the traditional EC2, S3, and similar services. Talking to AWS clients and partners made me believe that most of them have grand plans for adopting these new services. And almost all of them appreciate the hand holding AWS has provided to make their journey less painful.

Though AWS never admitted it, it was apparent that it realizes the vast potential in this market. Out of its 125+ services, very few are consumed at a massive scale. This implies there is a lot of headroom for AWS, despite that it’s already clocking a run rate of US$20 billion. This is very similar to its online business which, despite its size, is only ~4 percent of U.S. retail. Given such potential, it is no surprise that Amazon is investing heavily in AWS. Indeed, most of Amazon’s operating profits in recent quarters have been from AWS.

The cloud market is in flux, and with the first and second generations of DR/back/email migrations now over, the next battlefield is the business process and AI/ML workloads. AWS has strong plans to lead this market as well. It will be interesting to observe how it shapes the cloud world. Can it influence it the way it did online retail? AWS certainly has the vision, capability, and aspirations. Only time will tell.

Bored of Directors: No Technologists on Board = Impending Doom | Sherpas in Blue Shirts

The US Congress’ recent grilling of Facebook CEO Mark Zuckerberg led to a flurry of articles on how the “oldies” asking questions had no idea how Facebook worked or what meaningful questions to ask. Most people stated that the congressmen had zero background in technology, and were asking generic feel good questions that didn’t require incisive answers or meaningful preparation.

Juxtapose this to any large enterprise in the world. Market interactions suggest that less than 5 percent have a technologist on their board of directors. Their high echelon spots are filled with management, finance, or, at best, operational executives. So, how can the board members advise or question their companies around their technology advancement? Can they conceive of or initiate discussions around the enterprise becoming a platform business? (Would they even understand what that means?) How can they critique or support such technology-heavy discussions?

The obvious answer is, they can’t.

Although board members aren’t required to actively build strategy for the company – that is left for the CEO and the team – they are certainly required to intervene when they see the company is losing direction or possibly isn’t doing enough. Because they have no clue about what is happening in the technology world in the digital age, they can’t ask questions around digital strategy. In turn, they can’t be fully effective in their roles. And that can spell doom for the company.

Who’s to Blame?

While some of it falls to the board members, the technologists in the company – such as CIOs and CTOs – must share the blame for not being invited to the board, or at least regular boardroom discussions. They haven’t been able to succinctly explain digital disruption in a business sense that gets the board’s attention. Instead, they primarily focus on cost-centricity or supporting the business in newer initiatives. And they explain minute details around technologies and vendor management, which don’t give the board members the grounding they need (and honestly, aren’t interested in.)

What Should Technologists Do?

In order to provide boards with what they really need to know, technologists need to up their game and focus on the business impact of technologies, not just the business case.

First and foremost, they need to change their cost center mindset…something that’s been said, attempted, and failed in the past. However, in today’s environment, with digital technologies transforming, enhancing, and destroying businesses, IT has a real chance to become a force to reckon with. It needs to enhance its self-perception and treat itself as a business driver, not a support center. Though running the business activities may continue to take most of IT team members’ time, IT leaders must proactively suggest and address the change-and-transform activities.

Technologists will also be well-served by investing time in learning “story telling.” Board members don’t have the time, patience, or need to understand a long-winded argument. They are interested in learning the story behind the argument, and how it helps the business. Technologists who learn to use stories will be much more adept at driving their point home. This will ensure that the board has a relook at technologists’ role, and sooner than later invite them to join the board.

What Should Enterprises Do?

A board of directors’ role continues to be steering a company in the right direction. However, the days of developing a long-term strategy and intervening at exception are truly over. In the digital age, enterprises need iterative and evolutionary strategies that are dynamic and flexible enough to both respond to changing market dynamics and create newer dynamics.

For this to happen, company management needs to move beyond getting members of the “old boy’s school club” on their board. It must challenge the culture of celebrating technology ignorance. And it must vigorously look for gaps in current members’ understanding of technology disruption, and whether or not they are capable of deliberating technology disruption and how the company can harness it for competitive advantage.

Board members should be selected – indeed retained – only if they truly understand the business issues in today’s digital age. If they don’t, the enterprise they represent is doomed.

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