Augmented reality: Field service proof points in the enterprise

Augmented reality

Image supplied by Oracle

Although we read about augmented reality in the popular press, the focus tends toward consumer expereinces like Pokemon Go and Snapchat. Although the consumer side of AR is huge, there are important applications in the enterprise.

Because the conference was in Las Vegas, the demo showed a field service technician using AR on a mobile phone to repair a broken slot machine. The demo is instantly compelling because of the visuals and shows a practical enterprise use case for augmented reality.

A presentation at Oracle’s recent Modern Customer Experience conference demonstrated augmented reality applied to field service management. It’s one example where the value of AR is obvious and dramatic.

Although the field service management industry has been innovating around knowledge delivery to technicians for decades, powerful mobile hardware combined with ubiquitous connectivity and AR software changes the game.

ALSO READ: Augmented reality: An enterprise business imperative

To gain an in-depth view of how AR is changing field service, I put questions to Shon Wedde, Oracle’s Senior Director of CX Product Management, and Joshua Bowcott, Product Manager, Oracle Service Cloud. They also captured the sequence of screens in the gallery embedded above.

When is augmented reality most suitable for field service?

Customers adopt AR for various field service applications across all industries. Traditionally, AR emerged where massive pieces of equipment were used — like in oil and gas — as well as in M2M (machine-to-machine) situations, along with factory assembly lines. Companies selling complex and connected equipment across industries like manufacturing, medical, and automotive industries, have realized the importance adopting AR for field service.

Augmented reality is most suitable when it involves connected, complex equipment in a data-rich environment.

The concept of AR has been around for years. What’s new is our ability to take IoT and customer service technologies, such as policy automation and workflow, and integrate them into an AR scenario. Policy automation guides dynamic animation, and IoT data provide real-time feedback, creating a rich environment for AR and field service technicians to work.

We should also note that AR applications go beyond field service scenarios-enriching not only B2B and B2C interactions, but also internal company training, self-service, and assisted-service experiences as well. We explain those in more detail below.

What type of equipment does the field service technician need?

A field service technician can use any mobile device, including cell phones, tablets, goggles, etc.

How does the equipment vendor create the augmented reality content used by field service technicians?

AR content relies on information that already exists. Companies like PTC ThingWorx utilize existing product CAD drawings, scaling them to match real life animation with PTC software. A field service technician will pull existing information from the contact center’s knowledge, base, as they do today.

For example, an AR-equipped mobile device can “point” at a connected piece of equipment, such as a slot machine, and determine its make and model. The slot machine’s problems are also transferred via IoT data. The system uses this information to filter the contact center’s existing knowledge base for articles that pertain to this particular instance, eliminating the technician’s need to manually figure out the machine’s make, model, and where the problems originate.

From there, service solutions like Policy Automation guide a technician step-by-step with animation, to resolve the slot machine’s issue. If a replacement part is needed, a technician can use integrated commerce functionalities to order that part, specific to the machine’s make and model. Finally, the entire experience is captured and logged alongside the customer’s profile and history with the device and the company.

We should note that the Oracle Policy Automation interview and the user’s answers dictate which AR experience is loaded. [The gallery embedded above] only shows one path [of many possibilities.]

What are the primary applications today for augmented reality in field service?

Companies across all industries are using AR, especially in those maintaining assets. They are B2B as well as B2C companies. Oracle has seen use cases from wind farms, control systems, and medical equipment to household appliances and motorcycle manufacturers.

AR applications extend far beyond field service. There’s a massive shift underway as AR emerges as a new consumer user interface. Facebook is now delivering AR as part of its core platform and we see mainstream AR technology in apps like Snapchat, for example.

Consumers are becoming increasingly comfortable with AR as a new self-service channel. For example, a consumer wouldn’t call a field service technician to their house to fix a coffee machine. Instead, AR would walk the consumer through steps on his or her mobile device or tablet to diagnose the coffee machine issue and then either change a coffee filter or click to buy a new coffee filter. This AR scenario isn’t designed to enable an agent. Instead, it’s bringing the consumer directly into the self-service experience. He or she can interact with an agent via chat or video chat right on the device, as an assisted-service experience if needed.

Furthermore, combining AR with other Oracle technologies enables businesses to service equipment faster, without the need to dispatch a technician or expert, allowing for quicker resolution. One expert technician can capture an entire installation or service experience with AR and shared as a virtual reality training program, available to a company’s employees anytime and anywhere in the world.

Disclosure: Oracle is a consulting client

(Cross-posted @ ZDNet | Beyond IT Failure Blog)

Digital survival: The transformation imperative


Many discussions of digital transformation focus on marketing metrics such as page views, clicks, and even mentions in the press. Although marketing is important, this limited and unsophisticated view ignores underlying issues such as improving business speed, agility, and responsiveness to customers.

Only by rethinking business models can we meaningfully gain benefit from digital transformation in departments across the company including operations, supply chain, manufacturing, and customer service. Even finance and accounting should evolve as an organization responds to the changing expectations of modern consumers.

Genuine digital transformation – not the veneer of marketing, but the real thing – has become a business imperative. Over 50 percent of the Fortune 500 have disappeared in the last 15 years or so years, demonstrating the need for organizations to evolve.

Even when companies commit to a program of digital transformation, the results are often disappointing. Despite large investments in digital transformation, 59 percent of respondents in one survey companies reported that “digital transformation has not delivered high business impact at their organization.”

Against this backdrop of market confusion, I invited a digital transformation expert, practitioner, and author to participate as a guest on episode 208 of CXOTALK, a series of conversations with the world’s leading innovators.

Anurag Harsh is a founding executive of Ziff Davis (no relation to ZDNet), one of the largest publishers of technology content in the world. He is a prolific author (of multiple books) and puts theory into practice as a senior executive at Ziff Davis.

The conversation with Anurag Harsh spans the practical to the philosophical; the cultural and psychological to the technological. Among the wide-ranging topics, we discuss strategies for digital transformation, each targeting a specific business situation. From the structural swivel to the inverse acquisition, Anurag offers a prescription for many digital transformation situations.

Watch the video conversation embedded above, but also read the complete transcript of our talk. Here are edited excerpts and highlights:

Why is digital transformation so important?

In the last fifteen or sixteen years, more than half of all Fortune 500 companies have become insolvent, been acquired by another company, or stopped doing business altogether. And if you just look at last year, 50% of Fortune 500 companies declared a loss. So, the stride of transformation has become a revolution. Rivalries have deepened, and business models have been dislocated. The only constant is the growing severity of digital disruption.

Because of disruption, there’s despondency, and that’s compelling companies to want these digital initiatives. And they are investing a lot of money, which mostly results in disappointment due to the absence of concrete strategy. As markets shift downward, many companies try to counter the spiral by initiating frantic investments and digital initiatives. Some of them are hiring Chief Digital Officers, and some of them are looking at their CIOs and CMOs to counter these disruptive effects.

Describe your model for digital transformation?

There are five things that companies need to think about. These are terms I use a:

  • Structural swivel
  • Inverse acquisition
  • Offshoot
  • Coattail rider
  • Oiling the hinges

I will describe the first three now.

Structural swivel. If you talk to any CTO or CIO they have all legacy systems and techniques that can impede their ability to execute. By altering the company’s configuration to spotlight digital initiatives, executives can swiftly escalate the speed of transformation. It’s tactic that requires earmarking funds, and human resources to digital initiatives and placing digital executives in command of existing business processes.

For example, a local bank has started to swivel actively. Remember, this is the structural swivel we’re talking about.

It swiveled out of a conventional branch-driven model by venturing outside to recruit a CDO (Chief Digital Officer). The bank empowered this guy with complete corporate supervision, comprising all branches that were still the lion’s share of the bank’s income. All product, tech, sales outlets, and marketing units started reporting to the new CDO. To push for digital transformation, each regional division also hired a committed CDO at the same level as the local bank president. These changes were intended to assist the bank in obviously speeding and hastening its conversion to a soup-to-nuts digital enterprise and organizing a purely digital experience across all the business channels. That’s what I call a structural swivel.

Inverse acquisition. There are a lot of businesses that have unearthed quick wins ─ quick triumphs ─ by placing boundaries on the digital products so they can function autonomously and uninhibited by traditional processes. Just put them in a corner somewhere. It’s like, “Off you guys go!”

However, the moment a digital project shows its usefulness, shouldn’t subsequent tasks follow suit? Persevering or preserving the project’s autonomy restricts its influence on other businesses. So, one possibility is to absorb the traditional businesses into the new digital unit, spreading the transformation business-wide, and then compelling the rest of the company to abandon its archaic approaches. This is what I call “the inverse acquisition. ”

This tactic requires hard work. It comprises the comprehensive moving and resettlement of technology manifestos, company structures, and processes, and ultimately consumers from the traditional business to the new model. You must be cautious to ensure the company doesn’t collapse into disorder during the changeover.

I’ll give you an example. The British retail store, John Lewis, acquired in 2001. It inherited vital technology and talent that it used to erect its only e-commerce business quickly. John Lewis later commenced a gigantic undertaking to reconstruct its web and e-commerce framework, which involved assimilating over 30 existing tech systems. And then they launched an e-commerce site around 2013, fully connected with their supply chain, delivery conduits, and the physical stores. The 10-year long dedicated effort increased its online sales by close to 30%. So, inverse acquisition. That works.

Offshoot. It’s unrealistic always to expect a new digital operation to absorb the traditional business, especially if the digital business is not yet developed sufficiently to absorb a larger unit. Also, it may focus on too dissimilar a fragment of the value chain.

In these cases, you can grow those ventures by segmenting the separate fragments into distinct businesses that can develop outside the principal line of business.

There’s an example here as well. BBVA Compass, a Spanish bank, had a software development division called Globalnet that they used to fuel their technology initiatives for over a decade. A few years ago, Globalnet, this little software development division, transformed into a company called BEEVA, which is an offshoot for creating and marketing business web services.

Although BEEVA powered the base technology for BBBA ─ the Spanish bank’s transition into digital banking ─ bank executives understood the software division’s innate potential. As an independent services business, BEEVA helps other banks do what BBBA has done using BEEVA’s groundbreaking cloud technology platform.

In this instance, a structural swivel or inverse acquisition would not have worked. Why? Because the bank was ultimately a financial services company and its software division BEEVA was a web services unit with functionality that was different from the bank’s core business.

So, that’s what I call “offshoot.”

What is authenticity in digital transformation?

Consumers expect authenticity from corporations and individuals alike. As consumer psychology changes, digital and marketing must enter a new era where human needs — values and connections — define success and failure. This is a call to action for marketers and advertising executives to change their perspective towards consumers.

Companies can no longer see consumers as gullible moneybags or conquests. They must see consumers as community members, as human beings, who crave trust.

You see the theme here? We’re talking about technology and digital, but what I’m getting at is connection. Consumers crave trust: predictability, transparency, respect. I call it the relationship era.

Your corporates value must resonate at every level of infrastructure. It has to emanate outwards to the company’s employees, customers, suppliers, stakeholders, neighbors, and even your relationship towards Earth! Merely projecting an image is akin to falsity. Companies must steadfastly practice what they preach.

The public today cares not only about the cost and quality of products and services. People also care about the values and conduct of the providers. Trust, reliability, ethics often supersede quality and affordability.

Thank you to my colleague, Lisbeth Shaw, for assistance with this post.

CXOTALK brings you the world’s most innovative business leaders, authors, and analysts for in-depth discussion unavailable anywhere else. Enjoy all our episodes.

(Cross-posted @ ZDNet | Beyond IT Failure Blog)

McKinsey: AI, jobs, and workforce automation


For business people, AI presents a variety of challenges. On a technology level, artificial intelligence and machine learning is complicated to develop and demands rich data sets to produce meaningful results. From a business perspective, many business leaders have difficulty figuring out where to apply AI and even how to start the machine intelligence journey.

Making matters worse, the constant drumbeat of AI hype from every technology vendor has created a continual barrage of noise confuses the market about the real possibilities of AI.

To cut through this noise, I have invited many world-leading practitioners to share their expertise as part of the CXOTALK series of conversations with innovators.

For episode 219 of CXOTALK, I spoke with Michael Chui, a Principal at the McKinsey Global Institute (MGI), and David Bray, an Eisenhower Fellow who is also CIO at the Federal Communications Commission.

The McKinsey Global Institute has released a variety of research reports on topics related to Ai, automation, and jobs. For example, see this article on the fundamentals of workplace automation.

As you can see in the graphic below, Chui and his team examined a variety of industries looking at the impact of automation, including AI, on the workforce.

CXOTALK McKinsey - automation and AI

Image from McKinsey Global Institute

Another fascinating graphic showing automation potential and wages for US jobs:

CXOTALK McKinsey - automation, AI, and wages

CXOTALK McKinsey - automation, AI, and wages

Image from McKinsey Global Institute

The conversation between Michael Chui and David Bray covered key points about the relationship of business and the workforce to automation and AI – including investment, planning, and even ethical considerations.

You can watch our entire conversation in the video embedded above. An edited partial transcript is available below and you can read the complete transcript at the CXOTALK site.

How should organizations think about investing in AI?

Michael Chiu: More organizations have started to understand the potential of data analytics. Executives are starting to understand that data and analytics are either becoming a basis of competition or a basis for offering the services and products that your customers, citizens, and stakeholders need.

While there are often real technology challenges, we often find the real barrier is the people stuff. How do you get from an interesting experiment to business-relevant insight? We could increase the conversion rate by X percentage if we used this next product to buy an algorithm and this data; we could reduce the maintenance costs, or increase the uptime of this whole good. We could, in fact, bring more people into this public service because we can find them better.

Getting from that insight to capture value at scale is where organizations are either stuck or falling. How do you bag that interesting insight, that thing that you capture, whether in it’s in the form of a machine learning algorithm, or other types of analytics, into the practices and processes of an organization, so it changes the way things operate at scale? To use a military metaphor: How do you steer that aircraft carrier? It’s as true for freight ships as it is for military ships. They are hard things to turn.

It’s the organizational challenge of understanding the mindsets, having the right talent in place, and then changing the practices at scale. That’s where we see a big difference between organizations who have just reached awareness and maybe done something interesting and ones who have radically changed their performance in a positive way through data, analytics, and AI.

What are the adoption problems around AI and machine learning?

David Bray: The real secret to success is changing what people do in an organization, that you can’t just roll out technology and say, “We’ve gone digital, but we didn’t change any of our business processes,” and expect to have any great outcomes. I have seen experiments that are isolated from the rest of public service; and they say, “Well look, we’re doing these experiments over here!” but they’re never translating to changing how you do the business of public service at scale.

Doing that requires not just technology, but understanding the narrative of how the current processes work, why they’re being done that way in an organization, and then what is the to-be state, and how are you going to be that leader that shepherds the change from the as-is to the to-be state? For public service, we probably lack conversations right now about how to deliver results differently and dramatically better to the public.

Artificial intelligence, in some respects, is just a continuation of predictive analytics, a continuation of big data, it is nothing new because technology always changes the art of the possible; this is just a new art of the possible.

I do think there’s an interesting thing in which it could offer a reflection of our biases through artificial intelligence. If we’re not careful, we’ll roll out artificial intelligence, populating it with data from humans, [and] we know humans have biases, and we’ll find out that the artificial intelligence itself, the machine learning itself, is biased. I think that’s a little bit more unique than just a predictive analytics bias or big data.

Which business areas most suited to AI?

Michael Chiu: When we surveyed about 600 different industry experts, every single one of those problems we identified, at least one expert suggested it was one of the top three problems that machine learning could help improve. And so, what that says is potential is just absolutely huge. There’s almost no problem where AI and machine learning potentially couldn’t change and improve performance.

A few things that come to mind: One is a lot of the most interesting and recent research has been in this field called “deep learning,” and that’s particularly suited for certain types of problems with pattern recognition, often images, etc. And so those problems that are like image recognition, pattern recognition, etc. are some of those that are quite amenable and interesting.

So again, regarding very specific types of problems, predictive maintenance is huge. The ability to keep something from breaking; rather than waiting until it breaks and then fixing it, the ability to predict when something’s going to break. Not only because it reduces the cost. More important, is the thing doesn’t go down. If you bring down a part of an assembly line, you bring down the entire factory or often the entire line.

To a certain extent, that is an example of pattern matching. Sensors are the signals that reflect that something’s going to break, informing you to do predictive maintenance. We find that across a huge number of specific industries that have these capital assets, whether it’s a generator, a building, an HDC system, or a vehicle, where if you’re able to predict ahead of time before something’s going to break, you should conduct some maintenance. That is one of the areas in which machine learning can be quite powerful.

Health care is another case of predictive maintenance but on the human capital asset. Then you can start to think, “Well gosh! I have the internet of things.” I have sensors on a patient’s body. Can I tell before they’re going to have a cardiac incident? Can I tell before someone’s going to have a diabetic incident? That they should take some actions which could be less expensive, and less invasive, than having it turn into an emergent case where they must go through a very expensive, painful, and urgent care type of situation?

Again, can you use machine learning make predictions? Those are some of the problems things that can potentially be solved better by using AI and machine learning.

David Bray: There are opportunities for artificial intelligence and machine learning to help the public. I think a lot is going to happen first in cities.

We’ve heard about smart cities. You can easily see better preventive maintenance on roads or power generation and then monitoring to avoid brownouts. I think the real practical, initial, early adoption of AI and machine learning is going to happen first at the city level. Then we’ve got to figure out how to best use it at the federal level.

CXOTALK brings together the most innovative leaders in the world for in-depth conversations about leadership and innovation. See the complete list of episodes.

(Cross-posted @ ZDNet | Beyond IT Failure Blog)

Artificial Intelligence: Legal, ethical, and policy issues

Artificial Intelligence Legal, ethical, and policy issues

Image from Wikimedia

Artificial intelligence has entered mainstream consciousness surrounded by marketing hype, jargon, inflated expectations, and fear. Given the importance of AI, we have started a new series of CXOTALK videos, speaking with experts in areas such as technology, data science, ethics, and public policy.

This series kicks off with episode number 203 of CXOTALK and a conversation between one of the top legal experts in the world on AI ethics and a respected expert on public policy.

Kay Firth-Butterfield is an attorney, author, judge, and public speaker on topics related to AI and ethics. Kay’s experience in this field is quite amazing as you can see on her LinkedIn page. David Bray is a frequent guest on CXOTALK. He is an Eisenhower Fellow, Visiting Executive In-Residence at Harvard, and Chief Information Officer at the Federal Communications Commission.

The conversation offers a fascinating look at the implications of AI for society. It explores issues such as the speed of change due to advances in computing technology; loss of control and privacy; job destruction due to automation; and advice on law and public policy related to technology and AI.

The video embedded below is a summary of the entire 45-minute conversation. You can watch the entire video and read a complete transcript at the CXOTALK site.

Here is an edited version of the transcript taken from this summary video:

Why should we care about the legal, policy, and ethical issues of AI?

Kay Firth-Butterfield: One of the things that stick out in my mind is some research that McKinsey did recently, where they describe AI as a contributing factor to the transformation of society. And I just want to quote what they’re saying about the transformation of our society: that it’s happening ten times faster, and at three hundred times the scale, or roughly three thousand times faster than the impact of the industrial revolution. And you know, a lot of people compare this revolution to the industrial revolution. But, I think it’s the speed and the real, core underpinning that AI is contributing to that transformation of our society that makes these discussions so important.

David Bray: It’s not just about handing over judgment and decisions to a machine that a human would do otherwise. It is about the loss of a locus of control, either a loss of a locus of control for the individual. So, when you’re in an autonomous car, you know, you are not driving; the car is driving unless you have the ability to stop within milliseconds that might not be possible. It’s really about are we handing over control to an entity that we are willing to trust that will be as fair, if not fairer than a human. And that’s where it gets to what Kay said with Europe.

So, I think it’s just the scale at which it may be used, and the scale and the impacts of the decisions. There’s always been the ability to tailor your experience even before the Internet regarding what services were provided to you. People were making sense by hand what things you should receive in the mail regarding ads, or what was called “automated data processing in the 1970’s.

Privacy laws came back in the 1970’s when you started doing automated data processing. And again, these machines were nowhere near as fast as what we have today, but that somehow there could be a correlation of “This person lives at this address; they’re getting this type of heart medication; they also are on this type of insurance.” At what point do you need to say, “Well, those are correlations you shouldn’t draw unless that person is giving consent?” So I think artificial intelligence, much like those things that came before, it’s just the scale and the impact of what this machine might be able to make decisions that will impact your life will be. So you’re right it’s the same trend. But, I think it’s the sheer scope and impact that I think we need to take into consideration.

Are scale and pervasiveness the driving forces?

Kay Firth-Butterfield: Obviously, you know the seminal quote from Stephen Hawking on the first of May, 2014, when he said that this could be the best thing that we’ve ever done or our last. And I think that captured the attention of the media. And where there were lots of us thinking about these things before, it’s become so much part of a more public conversation now.

That’s a really important thing. One of the questions we have been talking about is taking some control for ourselves as individuals. And unless we empower people to do that through education, then people are not going to be able to take back that power. And so, and also I think that there’s an issue with what we’re seeing in social media at the moment. I have seen a lot on Twitter in the last two days that people are saying, “Oh well move. We have to defend our privacy.” And there’s a lot of fear of surveillance ─ switching to Tor and more secure uses of email and things like that. That is not a positive sign for the way that some people in our society are thinking about artificial intelligence.

What about robots, jobs, and the impact on people?

Kay Firth-Butterfield: AI, in my view, is a technology that will benefit mankind or humankind enormously. And, there are some great challenges that we have as humans and for our planet that we really can’t solve without AI. And so, we certainly don’t want to see a groundswell of opinion against AI by people who are losing their jobs to it. We’ve all read the Oxford Martin study, and the Bank of America [Merrill Lynch] study that says that 47% and I think 52% of jobs in America currently done will go to automation in the next 15 or 20 years. But we have to think about the complexity of job loss because we don’t know what the future jobs are going to be. But what we do know is that as people lose their jobs, and some think that hasn’t been done in the past, we need, and can use AI to retool and re-skill those that workforce to create the jobs of the future.

David Bray: As jobs are lost because they can be automated, what do we as society owe those people whose jobs have been displaced, to help them retool, retrain as best as possible for something else. And the jury is out as to whether more jobs will be created vs. destroyed as a result of artificial intelligence. So, we need to monitor them and be aware of it. We must also be aware of there is what’s called the “unemployment effect” on people’s health, which is we humans need to have a purpose. And so, a future in which we don’t need to work because artificial intelligence is doing everything may not be a nirvana as it sounds like because we won’t find purposes. Or we may find purposes in avocations as opposed to vocations. But that’s a collective conversation we need to have, which is, “Where are we going together as a society? How can we make sure we bring as many people along?” As Kay said, ideally make it so they’re not as fearful of artificial intelligence.

Kay Firth-Butterfield: As a historian by background, I worry about the analogies with the industrial revolution because the industrial revolution hurt a lot of people over a long period. And yes, we came through it and we developed something better. But, it looks as if this industrial revolution will be much faster, and we need to prepare not to hurt as many people very quickly.

David Bray: So Kay’s right. It’s going to happen in a much shorter period. It may be as big if not bigger change. And so, having again that conversation about what do we, as a society, owe each other is key to have now because we don’t know! And none of us know if the job we’re currently doing today in two or three years will be done better by machines.

What advice do you have for people writing laws?

Kay Firth-Butterfield: Well I think the advice to lawyers is that very soon, you will be receiving… You will see those cases coming across your desk, and you need to get up to speed around artificial intelligence. And, what’s going on in artificial intelligence now, I think just going back to that job creation thing, there are going to be a lot of jobs around, so we’re not going to kill all the lawyers by automating them just yet because we are going to see experts needed in court. For example, instead of cross-examining a driver, we might have to cross-examine an algorithm, a.k.a. an expert on the system. If you are in any business, you need to be looking at what AI can do for you, and what the impact of AI will be on your business. So there are two pieces of that because I genuinely believe that AI will change everything. And if you don’t start looking now, you will be too far behind.

How about advice for policymakers?

David Bray: Cloud computing in some respects is the appetizer, artificial intelligence and the Internet of Everything is going to be the main course that we’re going to be consuming over the next five years. And, I don’t know if I can necessarily give advice necessarily to policymakers, but I’ll say what Kay said. Any organization and any entity should recognize that this will disrupt how you operate and it’s a question of whether or not you are very intentional about it. Or, someone else is going to do it to you. So, start on that journey now. Start having conversations.

There’s one thing I want call out, looking at the OpenAI effort and other efforts that are trying to make this open and available to people. Try to either begin experimenting or if you don’t have the time to experiment, maybe have some of your employees begin to experiment with what’s possible. Because we’re only going to get the expertise we need to know in this era through the experiments that we need to do with artificial intelligence.

Please see the list of upcoming CXOTALK episodes. Thank you to my colleague, Lisbeth Shaw, for assistance with this post.

(Cross-posted @ ZDNet | Beyond IT Failure Blog)