Industry and academia – the recipe for AI innovation

Academia and industry at first glance appear to be strange bedfellows. One focuses on the theoretical and conceptual, whilst the other is driven by the practicalities of deadlines, goals and ultimately, profit.

I’m in the privileged position of working on both sides of the fence. I’m Professor of Computer Science at the University of San Francisco as well as Chief Scientist at SnapLogic, a provider of application and data integration software. I have worked on AI in both of these roles, and throughout my 20 year career I’ve come to the realisation that, when it comes to driving innovation, these two distinct spheres need to work together.

AI promises to be the most important technology of the future, and if the lofty ambitions and out-of-the-box thinking of academia can find synergy with the can-do attitude, urgency and resources of industry, we’ll see an explosion in its applications. In fact, I believe that AI and ML technology won’t just be a nice feature but will be a requirement for all applications go forward.

Data, Data everywhere

This collaboration between industry and academia has been growing for some time and, like most things in technology, it all boils down to the data. For the first 10 to 15 years of my career I was on the traditional academic track, and whenever we were undertaking research or publishing papers, there was always one consistent stumbling block – we lacked real world data.

That all changed around 10 years ago with the emergence of social and search. The Google’s, Twitter’s and Facebook’s of the world were challenged by data growth problems that exceeded the capacity of conventional database technology, so they built custom solutions to house their vast datasets.

Of course, the large search and social companies didn’t curate all of this user and behavioural data for the distinct purpose of fuelling AI development, but they saw the potential that it held. The tipping point came when the likes of Twitter first invited academics to analyse this data. Suddenly and unexpectedly, a treasure trove of real world data was made available to academics. All that theoretical machinery that we had been developing in academia could be realised as real recommendations and meaningful predictive applications. Ph.D.s in academia started going into industry and working with real data, fuelling further theoretical developments and so on and so forth.

Many of the latest and most promising developments in AI have been forged through this symbiotic relationship, and if it can be strengthened, we’ll see many more breakthroughs in the pipeline.

Cultural Cross-pollination

The positive effects of academia and industry’s relationship aren’t solely limited to datasets. Cultural cross-pollination is, in my opinion, a vital aspect for driving further innovation in the field.

I’ve briefly alluded to this above, but it’s hard to imagine cultures much more different than industry and academia. As someone who was, for the majority of their career, primarily an academic, I’ve experienced first-hand how these two worlds differ, and how, on an individual level, being exposed to the other side of the coin is an enriching experience.

This is perhaps best illustrated with an example. I first joined SnapLogic in 2010, when it was a much smaller company than it is today. This was my first foray into industry, and I was tasked with building a prototype for a machine learning project to be implemented into our data integration platform. The prospect of putting my code in front of my new colleagues, industry veterans, unsettled me.

As someone who had been coding since the age of twelve or thirteen, it wasn’t a lack of skills or experience which caused this reaction, rather the prospect of doing so in a wildly different environment to that which I was used to. In academia, code is rarely reviewed. There isn’t an audience, it’s just an aspect of the job you complete. In industry it’s much more purpose-driven. There are goals and deadlines and milestones. Your work must deliver value for customers.

In the end everything was fine, and that code I wrote still exists in the platform to this day, but it shed some light on how sheltered we are in academia from the realities and stresses of industry. By spending time in industry I learned how to adapt to a different type of coding environment, and am a more well-rounded professional as a result.

These cross-cultural benefits are something I’m bringing into my classroom to better prepare my students for a career in industry. I’m now teaching some of the realities of building production software, which many academics might forego. We’re also bringing some of my students to work on-site at SnapLogic, on real-world AI projects, for course credit.

By exposing these younger people to industry in the earliest parts of their careers, rather than far later on as I did, the next wave of computer scientists will be equipped with not only the curiosity of the academic, but the practicality and work ethic of industry. This, I hope, will spur the next great developments in AI.

The AI future

I strongly believe that AI will have a huge impact on the world of business in the years to come. It will make companies more efficient, freeing up resources to be invested in other areas to enhance existing products or develop new ones. It will change job roles and allow employees to focus on more human tasks that rely on human skills, such as emotional insight, rather than burdened with rote, repetitive tasks.

However, it’s going to be an incremental process, and it won’t happen overnight. If we’re to have any hope of accelerating the timeframe, we’re going to need academics and industry pulling in the same direction, pooling their talents and resources and working in sync. The building blocks for this relationship are already in place, and both sides stand to benefit.

Source: and academia – the recipe for AI innovation

Thinking Through How Automation Will Affect Your Workforce

Today, executives have to cut through a lot of hype around automation. Leaders need a clear-eyed way to think about how these technologies will specifically affect their organizations. The right question isn’t which jobs are going to be replaced, but rather, what work will be redefined, and how? Based on our work with a number of organizations grappling with these issues, we’ve found that the following four-step approach can help.

1. Start with the work, not the “job” or the technology. Much work will continue to exist as traditional “jobs” in organizations, but automation makes traditional jobs more fluid and an increasing amount of work will occur outside the traditional boundaries of a “job.”

Optimally integrating humans and automation requires greater ability to deconstruct work into discrete elements — that is, seeing the tasks of a job as independent and fungible components. Deconstructing and then reconfiguringthe components within jobs reveals human-automation combinations that are more efficient, effective, and impactful. AI and robotics increasingly take on the routine aspects of both blue and white collar jobs, leaving the non-routine to humans. That challenges the very essence of what organizations retain as human work. The reconfiguration of these non-routine activities will yield new and different types of jobs.

2. Understand the different work automation opportunities. AI can support three types of automation: robotic process automation (RPA), cognitive automation, and social robotics.

RPA automates high volume, low complexity, routine administrative “white collar” tasks — the logical successor to outsourcing many administrative processes, further reducing costs and increasing accuracy. Optimizing RPA can only be done when the work is deconstructed. For example, RPA will seldom replace the entire “job” of a call center representative. Certain tasks, such as talking a client through their frustration with a faulty product or mishandled order will, for now, remain a human task. Others, such as requesting customer identification information and tracking the status of a delivery are optimally done with RPA.

Cognitive automation takes on more complex tasks by applying things like pattern recognition or language understanding to various tasks. For example, the Amazon Go retail store in Seattle has no cashiers or checkout lanes. Customers pick up their items and go, as sensors and algorithms automatically charge their Amazon account. Automation has replaced the work elements of scanning purchases and processing payment. Yet other elements of the “job” of store associate are still done by humans, including advising in-store customers about product features.

Social robotics involves robots moving autonomously and interacting or collaborating with humans through the combination of sensors, AI, and mechanical robots. A good example is “driverless” vehicles, where robotics and algorithms interact with other human drivers to navigate through traffic. Deconstructing the “job” reveals that the human still plays an important role. While the human “co-pilot” no longer does the work of routine navigation and piloting, they still do things like observing the driverless operation, and stepping in to assist with unusual or dangerous situations. Indeed, it is often overlooked that the human co-pilot is actually “training” the AI-driven social robotics, because every time the human makes a correction, the situation and the results are “learned” by the AI system.

3. Manage the decoupling of work from the organization. The future global work ecosystem will offer alternative work arrangements including each of the three automation solutions, along with human work sources such as talent platforms, contingent labor, and traditional employment. The human work that is created or remains after automation will not fit easily into traditional jobs, nor will it always be optimally sourced through employment. Work will need to be freed from “jobs within organizations,” and instead be measured and executed as more deconstructed units, engaged through many sources. Today’s supply chainstrack the components of products at both an atomized and aggregate level. Similarly, the new work ecosystem will develop a common language of work, enabling organizations not only to forecast and meet work demands from various sources, but to devise new reconfigurations of work elements that are best sourced in alternative ways.

4. Re-envision the organization. The combination of automation, work deconstruction, and reconfiguration will often redefine the meaning of “organization” and “leadership.” The “organization” must be reconsidered as a hub and capital source for an ecosystem of work providers. Those “providers” include AI and automation, but also include “human” sources such as employees, contractors, freelancers, volunteers, and partners. The optimal combination of these providers seldom appears if you frame the question as, “In which jobs will AI replace humans?” Only when you look within those jobs, as described above, will you discover the human-automation combinations that redefine work and how it should be organized.

AI will significantly disrupt and potentially empower the global workforce. It won’t happen all at once or in every job, but it will happen, and leaders will need an automation strategy that realizes its benefits, avoids needless costs, and rests on a more nuanced understanding of work.

Source: HBR-Thinking Through How Automation Will Affect Your Workforce

AI will create 800,000 jobs and $1.1 trillion revenue by 202

Contrary to the bleak picture painted by critics, a new IDC study of more than 1,000 organisations worldwide shows that AI will be in the workplace “sooner than we think”, and will have a positive impact on productivity, revenues, and job creation.

From 2017 to 2021, the Salesforce-sponsored study predicts that AI-powered CRM activities will boost business revenue by $1.1 trillion, and create more than 800,000 direct jobs and 2 million indirect jobs globally, surpassing those lost to AI-driven automation.

The business revenue boost will be led primarily by increased productivity and lowered expenses due to automation, which account for $121 billion and $265 billion of the $1.1 trillion sum, respectively, according to the study.

Keith Block, vice chairman and COO at Salesforce, said the impact of AI for the CRM market will be “profound” in that it will enable “new levels of productivity”.

“The convergence of increased computing power, big data, and breakthroughs in machine learning have meant artificial intelligence is set to transform the lives of workers, especially those that are already using CRM technology, by helping them be more productive in their development of more meaningful connections with customers,” added Robert Wickham, RVP of Innovation and Digital Transformation at Salesforce APAC.

“What IDC’s research shows is the picture is more nuanced than doom and gloom predictions.”

While the trillion-dollar figure might appear far-fetched to some, the A Trillion-Dollar Boost: The Economic Impact of AI on Customer Relationship Managementreport states that it’s a “conservative” prediction, because a lot of resources go into IT implementations.

“The spending on IT software, services, and hardware itself is often small compared with spending on staff (IT and operational), operations, non-IT capital goods, and more. In fact, spending on external IT — which now permeates most enterprises in the world — represents less than 1 percent of the world’s business revenue and generally less than 5 percent in even the most IT-rich enterprises,” the IDC-Salesforce report states.

“IDC research shows that even in cloud-based solutions, any single implementation will require additional spending — on other cloud services, consulting, networking, security, and more.”

Block recommended that companies looking to embrace AI should create new workforce development programs to equip employees with the skills necessary in the future.

The study also estimates that Salesforce customers will account for $293 billion, or about 26 percent, of this revenue boost, and more than 155,000, or about 19 percent, of the net-new jobs by 2021.

2018 will be a landmark year for AI adoption, according to the IDC-Salesforce study, with more than 40 percent of the organisations surveyed indicating that they will adopt AI within the next two years.

The types of AI that these organisations are looking to adopt include machine learning (25 percent), text analysis (27 percent), voice/speech recognition (30 percent), and advanced numerical analysis (31 percent).

IDC expects worldwide spending on “Cognitive/AI systems” — which includes hardware, software, and services — to grow from around $8 billion in 2016 to $46 billion in 2020.

The research firm also forecast that 75 percent of enterprise and ISV development will include AI or machine-learning functionality in at least one application. AI-powered CRM activities — such as accelerating sales cycles, improving lead generation and qualification, personalising marketing campaigns, and reducing customer support expenses through chatbots — will cover a large spectrum of use cases, according to IDC.

The United States is expected to lead the way in new business revenue growth through AI implementation, accounting for $596 billion of the $1.1 trillion GDP impact, followed by Japan at $91 billion; Germany at $62 billion; the United Kingdom at $55 billion; and France at $50 billion.

Australia came in last, with the study estimating that AI-powered CRM activities will create more than 16,000 new direct jobs and AU$19 billion in increased revenue over the next five years. Improved productivity in Australia accounts for $4 billion of the revenue boost.

The CRM giant itself has invested heavily in artificial intelligence, announcing in May a new $100 million Salesforce Platform Fund aimed at accelerating the development of artificial intelligence-powered applications and components on the Salesforce platform.

The company has also been boosting its AI capabilities, integrating its “Einstein AI” technology with its various clouds as an add-on in March. It also launched Einstein Vision, a set of APIs that allow developers to bring image recognition to customer relationship management and build AI-powered apps.

In May, Salesforce revealed that it was testing a version of its Einstein AI service internally to help project sales and give guidance.

In Salesforce’s first-quarter earnings conference call, CEO Marc Benioff said an internal version of the Einstein Guidance feature has made the AI another member of the management team.

“Every question that I possibly could have, I’m able to ask Einstein. And I think for a CEO, typically the way it works is, of course, you have various people, mostly politicians and bureaucrats, in your staff meeting who are telling you what they want to tell you to kind of get you to believe what they want you to believe,” Benioff said in the earnings conference call.

“Einstein comes without bias. So because it’s just based on the data, and it’s a very exciting next-generation tool. And to have Einstein guidance has transformed me as a CEO.”

It’s not clear, however, when the Einstein Guidance feature will be rolled out broadly.

Salesforce is not the only CRM vendor investing in AI; MicrosoftSAP, and Oracleare also betting on the AI-powered CRM market, especially as enterprise data explodes, making it more compelling for sales and marketing teams to have access to intelligent tools that can sift through that data, and identify and tailor experiences to the prospects with the highest propensity to make a purchase.

Source: ZDNet – AI will create 800,000 jobs and $1.1 trillion revenue by 2021

AI technologies affect all corners of business, IT

All areas of IT must brace for AI impact

Throwing artificial intelligence at your data to answer business questions is like using a tornado to blow out a match.

In other words, just because artificial intelligence tools can provide answers doesn’t mean you should use them. If good old business intelligence tools do the job just fine, stick with what you know. But AI is a great way to uncover information hidden within vast amounts of data — as long as you’re willing to use the information that surprises you, according to Jana Eggers, CEO of Nara Logics, a synaptic intelligence company based in Cambridge, Mass.

“If you aren’t willing to learn, don’t do an AI project. Do a regular analytics project,” Eggers said during her presentation at the TDWI Accelerate conference in Boston earlier this year.

That’s sound advice in a time when all we hear about is the power and promise of AI technologies like cognitive computing, natural language processing and machine learning. Using AI judiciously can save companies a whole lot of time and money on a tech that’s exciting but may not be appropriate for the job. It’s also important to carefully consider where and when to use AI because artificial intelligence affects nearly all areas of IT, along with the people, processes and corporate culture underlying the business.

AI technologies require vast amounts of data, collected from sensors, applications or the mobile devices in users’ hands. Collecting all that data to feed AI systems requires a tremendous amount of storage — so much so that companies are moving their data warehouses to the cloud. Companies also need staff members with data science skills to make sense of the data, developers who know how to work with AI — the list goes on.

The articles in this guide provide deep insight into the dos and don’ts of AI, how AI affects the data center and IT staffing, tips for figuring out the ROI of AI, and more.

Source: technologies affect all corners of business, IT

Cognitive Computing Energizes the Enterprise

Today’s intelligent systems can learn from customer data to discover and provide insights that drive better experiences, heighten employee engagement, and inspire companies to innovate

Like it or not, humans and robots are increasingly being forced to coexist in the same environment, and there’s little anyone can do to prevent it.

This is becoming especially apparent in the CRM space, where customer service, sales, and marketing professionals are all starting to feel the impact of technological advancements in their respective fields. More software vendors are now offering software that incorporates artificial intelligence (AI), machine learning, deep learning, cognitive computing, chatbots, intelligent assistants, augmented reality, and a slew of other innovations, designed to perform the otherwise time-consuming tasks that needlessly tie up humans. The technologies also promise to process CRM data, along with other structured and unstructured data, more quickly to learn from behavioral patterns, discover solutions, and surface recommendations and insights to help customer-facing professionals do their jobs more efficiently and improve interactions with customers.

Though AI has been around for decades, the applications are coming out in droves. In April, an IDC forecast predicted that this year, worldwide spending on cognitive and artificial intelligence systems would increase by 59.3 percent to reach $12.5 billion. Of that amount, 9.8 percent is likely to encompass automated customer service agent tools. This suggests that these systems have really come into their own and can now be used by organizations of all sizes and experience levels.

There is a lot of buzz in the air, but the hype is not unfounded. To keep up in today’s competitive business landscape, companies that automate areas that can be simplified for the sake of a better customer experience will lead the way. And experts agree that all companies should at least be figuring out where to invest to get the ball rolling.


Michele Goetz, principal analyst at Forrester Research, says it’s important to understand what makes a solution “cognitive” or “intelligent,” and what doesn’t.

Chatbots, for example, have typically worked in a programmatic, or deterministic, fashion, meaning they can identify certain keywords or phrases that will indicate to which type of agent a case should be routed within the call center. A financial institution’s chatbot might pick up the term “loan” or “account balance” and then link that customer to the agent most qualified to help.

But “today’s intelligent chatbots—you can think of them as cognitive agents—are very different,” Goetz says. These systems, she points out, go steps beyond, as they can take voice-of-the-customer information, call center notes and recordings, facts, email info, and other components of a conversation before moving the case into recognition analysis. At this point, the assistant can understand the types of questions being asked, why they are being asked, when those interactions are taking place, and the results that they produce.

When a customer asks about his account balances, an intelligent assistant can reason that he might be interested in seeing his last five transactions. Or it might surmise that the customer could be concerned about potential fraud. Registering this, it can surface information to the agent who will take the call, or to the customer directly. “It’s much more evolutionary. It’s aware. It’s adaptive, and that’s where the intelligence comes from,” Goetz says.

This kind of functionality can be applied in many ways to benefit customers. A financial institution can use the technology to help customers with portfolio reallocation. In such scenarios, the intelligent assistant can guide the financial adviser as he works. It can observe the conversations, acting as a search mechanism that helps the adviser find the right information across different systems and resources, so he can make more intelligent recommendations faster. Instead of an agent having to write down the customer’s information during a call and get back to him in a week with a proposal, the interaction can now happen in near real time.

In many cases, all of this can happen behind the scenes, unseen by the customer. “The customer doesn’t necessarily know that this is happening,” Goetz points out. In many cases, too, the agent can train the machine, increasing the likelihood that the recommendations it makes are better and smarter over time.

Vince Jeffs, director of strategy and product marketing at Pegasystems, stresses the importance of the instant feedback loop in teaching intelligent assistants. There are plenty of cases where machines can’t quite solve these problems yet, he says, noting that some cases still have to be escalated to humans. “But they can be assisted by these machines, and the agents can guide the machine on whether or not its recommendations were good or not.”


While the buzz might signal something that is still quite a ways into the future, a reality in which many customer interactions are completed by machines is not that far off. Most companies—or at least those that don’t have astronomical budgets—shouldn’t expect a very high level of sophistication just yet. But there are many tools they can implement to save their employees time on routine tasks.

Customer support is a common area of investment, experts agree. A number of technologies can help “reduce the strain” on call centers as more people contact them for help and advice.

“If you can have an automated agent that is able to answer 50 percent to 60 percent of the questions in a timely way, provide good responses to customers, and doesn’t make them upset, that’s a win-win for everybody,” says Dave Schubmehl, a research director at IDC covering AI and cognitive systems.

Schubmehl points to Autodesk’s use of IBM Watson’s Conversation tool to develop a digital concierge as an example of a company that got it right. The agent, referred to as “Otto,” can handle 60 percent of web-based customer service inquiries. Autodesk improved support ticket resolution time by 99 percent and significantly upped customer satisfaction.

According to Schubmehl, to make it work, the company had to organize its knowledge base. The system was trained to handle common customer and partner issues, including resetting passwords or rebooting a program after it failed. More difficult cases that weren’t covered by the knowledge base could get handed off to a live agent.

“It is important we provide our customers with consistent quality paired with the shortest response and resolution time,” said Gregg Spratto, vice president of operations at Autodesk, in a statement. “Our collaboration with IBM Watson allows us to expand the Otto concierge service and deliver prompt, effective, and authentic engagement to our customers.”

Goetz mentions a similar case involving a company that sells insurance through employers and sees its heaviest traffic during open enrollment periods. In the past the firm had to train temporary staff members to handle those kinds of calls and answer questions about insurance types and policies. “The quality of customer service would significantly decrease, and it was inconsistent in terms of how customers were supported,” Goetz says. Furthermore, since they were contracted employees, the temporary agents were not as invested in their jobs as full-time employees. With IBM Watson, the company was able to move to first- and second-tier support levels when volumes peaked, pushing the more sophisticated cases to live agents.

Another major benefit of automation through intelligence is in reducing the “grunt work” and freeing people up to do more interesting work that requires more complex skill sets and critical thinking, according to Schubmehl. After all, it can get depressing just helping one customer after another recover passwords or log in to their accounts all day.

This is in line with research from an Aberdeen Group study commissioned by Inbenta, which found that companies incorporating cognitive technologies to support customer service interactions have seen an 81 percent improvement in employee engagement rates.

“There’s been a lot of conversation about virtual agents and artificial intelligence eliminating jobs, but what we actually saw is like any other tool: It’s going to be valuable in improving [agents’] performance and their interest and satisfaction while working,” says John Forrester, Inbenta’s chief marketing officer. “Instead of dealing with repetitive, boring questions that customers might have, they’d be dealing with something that’s more in-depth and engaging.”

While customer service jobs tend to have higher turnover rates than other fields, technology can help businesses with retention, as employees are likely to be more enthusiastic and eager to learn new skills and tackle greater challenges.


Behind the scenes, cognitive automation can be useful as well. Robotic process automation (RPA) can learn routine tasks that service agents repeat throughout the day.

“Robotic process automation has been around for about 15 or 20 years, but it’s primarily been rules-based, where somebody programs the rules for the agent,” Schubmehl says. “Now we’re getting to the point where you can have AI sitting there, watching the person doing the work, and then essentially developing the script automatically that the robot should act according to.”

That’s a relatively new area, but a hot one. Technology vendors like Pegasystems, WorkFusion, NICE, Automation Anywhere, and BluePrism are all adding RPA capabilities to their tool sets. “I think we’ll see that happen more frequently in 2017 and probably 2018,” Schubmehl predicts.

And while the call center might be the most obvious area and the one that most companies will want to look at first, sales professionals can also benefit from tools that simplify otherwise tedious processes, thus freeing them to tend to other parts of their jobs. Schubmehl points to Conversica as one vendor leveraging AI to simulate human voices and carry out conversations in place of sales reps, keeping leads warm so that when they turn hot again, they can be routed to a live salesperson.

In marketing, AI technologies can be used to learn customer preferences and identify the offers, images, and deals that are most likely to appeal to customers and to identify whether they should be sent via email or other channels and devices.


Goetz recommends getting started by testing and creating proofs of concepts. She says that companies in retail, consumer packaged goods, or manufacturing have a number of consumable applications from which to choose. A pilot can cost $50,000 to $100,000; implementation could range from $500,000 to $1 million, she says, but the proof of concept has already been given in many cases, leading to return on investment as high as 20 percent or 25 percent.

“Many organizations right now are doing piloting and testing and really working out to see the feasibility, and that entry point tends to be very low,” Goetz says.

Steve Laughlin, vice president and general manager of IBM’s global consumer industry division, points out that his company’s Watson APIs can be accessed via the BlueMix app development platform to help create and test programs before they are actually deployed.

Among the uses he’s seen already, some companies are turning the APIs into programs to help them understand personality insights from written texts on social media or to analyze images that customers have already seen to determine the next set of images they will likely want to see.

Staples, for one, recently used IBM Watson APIs to create a real-life Easy Button based on its plastic marketing icon. After doing design thinking with B2B customers of all sizes, the office supply superstore chain understood that there was a demand for an office assistant’s assistant to help stock up on supplies. The button responds to voice commands. “Right now, you can go into the supply room and say, ‘Hey, Easy, we need more copier paper, and by the way, it looks like we’re short on blue pens,’” Laughlin says.

“A crazy thing happened, and people started asking Easy for things that Staples doesn’t do, and it’s created a whole pipeline of new ideas for them to work with,” Laughlin adds. “So it’s become not only an innovation for them to engage with customers, but it’s become a source of ideas from customers for potentially new innovations.”

While IBM’s Watson is likely the most prominent technology in this space, other solutions, such as CognitiveScale’s Augmented Intelligence, Digital Reasoning’s Synthesis, and Narrative Science’s Quill, are emerging as competitive options.

Schubmehl suggests several other considerations when starting out. The first is getting the right stakeholders in place and then getting them all together to identify the business processes or functions that AI will replace. With that step taken, a company can set up a shortlist of service providers and discuss the potential outcomes for the application.

“One of the things that you’ve got to remember is that if you’re going to do an AI system, it’s going to be based on data, so you need to make sure that you have access to all of the data that’s necessary to drive the system,” Schubmehl adds. “A lot of organizations don’t have any real strategy about how they keep track of their knowledge, so really getting a handle on that first is something they should think about.”

Then companies need to engage subject matter experts in the design and implementation process, Schubmehl says. If you’re designing for the call center, you need to have the call center people involved in the design and the development. “If you don’t, you’re essentially asking for trouble,” because the design might not align with their needs, he cautions.

Goetz agrees that the design element is key, noting that adopting artificial intelligence calls for a desire to provide a human experience. Companies should ask, she says, “how do I reproduce, or almost replicate, what a human experience is going to be?”

“There are plenty of instances where we, as humans, still want human interaction,” Pegasystems’ Jeffs agrees.

In fact, designing an AI system, Goetz says, should mimic the process companies use to design a customer experience program.

“Sometimes they speak, so the tone, the way that they speak—is it very formal, is it conversational, and at what age groups are they speaking—those sorts of things really go into creating the best success in adopting artificial intelligence for customer engagement and customer experience,” Goetz says. “You can’t underestimate the design component that goes into that, and that’s how businesses have to approach this technology. You cannot think this is just another scoring engine or analytic engine that runs under the hood of your CRM or website or advertising platform.”

Source: destinationcrm-Cognitive Computing Energizes the Enterprise

Automation will destroy, then save outsourcing

For those of you who made our New York Digital OneOffice Summit a couple of weeks ago, we had a rumbustious mix of seasoned outsourcing buyers, service provider leaders, advisors and robo vendors under one roof to cogitate, discuss and argue where the hell the industry known as outsourcing and operations is truly heading. Let’s just lay down what the hell is really happening in the only unvarnished way we know how…

There is a fast realization that the outsourcing industry has reached a phase of almost insufferable tension. Why?

Several of the RPA solutions vendors are painting an over-glamorous picture of dramatic cost savings and ROI. RPA software firms are claiming – and demonstrating – some client cases where ~40% of cost (or more, in some cases) is being taken off the bottom line. While some of these cases are genuine, there are many RPA pilots and early-phase implementations in the industry that have been left stranded because clients just couldn’t figure out the ROI and how to implement this stuff. This isn’t simply a case of buying software and looping broken processes together to remove manual efforts… this requires real buy-in from IT and operations leaders to invest in the technical, organizational change management, and process transformation skills.

Buyers are backed into a corner with broken delusions of automation grandeur as their CoEs fail. Buyer leaderships are being fed all this rosy information and are under incredible pressure to devise and execute an RPA strategy, with some sort of set of metrics, that they can demonstrate to their operations leadership. Many are quickly discovering they simply do not have the skills inhouse to set up automation centers of excellence and are frantically turning to third parties to help get them on the right track.

Outsourcing consultants are selling RPA before they can really deliver it. Sourcing advisors are claiming they are now “RPA experts” who can make this happen, while struggling to scale up talent bases that can understand the technology and deal with the considerable change management tensions within their clients. RPA is murky and complex, and not something you can train 28-year-old MBAs to master overnight. Meanwhile, we are seeing some advisors simply do some brokering of RPA software deals for small fees, only to make a hasty exit from the client as they do not have the expertise to roll-out effective implementation and change management programs.

RPA specialist consultants few and far between. Pure-play RPA advisors are explaining this is not quite so easy and requires a lot more of a centralized, concise strategy. There are simply not enough of these firms in the market, especially with Genfour having been snapped up recently by Accenture. With only a small handful of boutique specialists to go around, these firms can pick and choose their clients and command high rates.

Service providers will set the pace, but many will destroy each other in the process. Service providers are claiming they can implement whatever RPA clients need, but are not willing to do it at the expense of reducing their current revenues. Meanwhile, smart service providers are aggressively implementing RPA into their own operations to drive down their delivery costs and reduce their own headcount. So we can expect to see providers aggressively attacking competitive clients with automation-led solutions that should create unbearable pricing pressures for service providers looking to retain the talent they need to implement this stuff. Hence, services providers will be hell bent on destroying each other and the winners will be those who eventually succeed in winning more work than they lose amidst all the destruction. This is a war of many battles being fought – and the winners will be those who are in this for the long haul, who can absorb some short-term losses to pick up the larger spoils further down the road when they have a fully equipped intelligent automation delivery capability that can deliver highly-competitive and profitable As-a-Service offerings.

The good news is that half of today’s buyers want to turn to service providers to make this work

When we privately polled 60 senior outsourcing buyers, at the recent HfS New York Summit, on what would improve the quality and outcomes of their current services relationships, the answer was pretty conclusive – half want to work with their providers to rollout their automation and cognitive roadmaps, while only a third think they should pull back work in-house to figure this stuff out for themselves:

The Bottom-line: The automation gauntlet is now in full effect and the casualties will mount up as the outsourcing industry plays out its most perilous battle for survival yet. But all is not lost if we eye a longer-term prize…

So we’ve reached crunch time. Whichever way we look at it, RPA has created a lethal environment, which was only just coming to terms with providers and buyers working together to get the basics of delivery right. Most outsourcing buyers have to look to automation to save their jobs and please their ambitious leaders, no longer content with the ~30% they saved on offshore-centric outsourcing just a few short years ago (see our recent State of Outsourcing and Operations data on 454 major buyers).

So, in the meantime, for all the reasons outlined above, this industry will literally go into a destructive war over automation. The skills to make automation a massively profitable reality are few and far between, while greedy corporate leaders demand cost savings that simply are not achievable if their organizations fail to make the necessary investments and partnerships to make this achievable. Did companies become amazing at HR overnight because they bought an expensive Workday subscription? Or amazing at sales and marketing because they slammed in a Salesforce suite? So why should they become amazing at cost-driven automation simply because they went and bought some licenses from an RPA vendor promising bot farms and virtual labor forces?

RPA and Intelligent Automation has sparked a major war in the worlds of outsourcing and operations, where many battles are being fought – and the winners will be those who are in this for the long haul, who can absorb some short-term pain in order to benefit from the larger spoils further down the road. While automation is killing outsourcing today – costing many people their jobs, their reputations and destroying the profitability of legacy engagements, those who can hunker down, focus on self-contained projects where they can fix one broken process at a time, can get stakeholders onside by demonstrating meaningful, impactful outcomes without major resource investments, will be the winners. Start with one process at a time, prove how to fix in, then onto the next, then the next… that is the only true way to be successful in this destructive automation-infested world.

Source: will destroy, then save outsourcing

Robotics, AI And Cognitive Computing Are Changing Organizations Even Faster Than We Thought

The world of AI, robotics and cognitive computing are changing business even faster than we thought.  JPMorgan Chase & Co now uses software to perform the mind-numbing job of interpreting commercial loans, reducing 360,000 hours of lawyer time each year.  AI software can now identify leukemia in photos and X-rays, learning faster than technicians. reduced new hire training to only two days because of its newest robotics used in shipping. And the stories go on and on.

Is this real and widespread around the world?  The answer is yes, and the pace is quickening.

Our just-released research (Deloitte Human Capital Trends 2017) shows that companies are not waiting for such technology to be perfected: they are implementing it now. Thirty-eight percent of companies in our new research (10,400 respondents from 140 countries) believe that robotics and automation will be “fully implemented” in their company within five years , and 48% of these companies say their projects are going “excellent or very well.”

Will this technology create massive unemployment? Are we entering a “jobless” economy where only software engineers and designers have jobs?

Our research says no. Among the companies we surveyed, 77% believe automation results in “better jobs,” 50% are investing in retraining workers to work side-by-side with machines and 33% expect people to do “more human tasks” augmented by robotics and AI. In fact only 20% of businesses believe automation will result in job loss.

As Automation Increases, Organizational Redesign Becomes The #1 Issue

It’s clear that the way we work has changed, yet or organizations have not yet caught up.  Business productivity remains low, employee engagement is flat (Bersin by Deloitte research with Glassdoor), and workers feel more overwhelmed and over-worked than ever. In fact, research by Project: PTO shows that US workers took almost a week less vacation in 2015 than they did in 1998.

What’s really going on? In a simple phrase our organizations have become a “network of teams,” and they no longer function well in the functional hierarchy of the past. The concept of a formal “job” with a job description is starting to go away. We now hire people to do “work;” we source them for skills and capabilities (not necessarily credentials); and we manage people around projects, customers, and products, not “roles.”

ING Bank, a pioneer in the implementation of automation and organizational redesign, just eliminated several layers of management and is now creating agile teams in every part of the organization. GE, Cisco, IBM and Deloitte are doing the same.

When we asked companies to prioritize their talent challenges, the #1 issue was “building the organization of the future,” which 88% of companies cited important and 59% rated urgent. Are companies ready?  The answer is no. Only 11% of these companies told us they understand how to make this happen.

As one analyst put it, “ Organizations that are designed for success in the 20th century are doomed for failure in the 21st. ” A good rule for us all to remember.

How do we redesign the organization to deal with increased automation at work? How do we empower teams to be agile, purposeful and engaging? And how do we change the workplace so people can be more productive, energetic, and focused?

While the answers to these questions are complex, I believe we have unlocked many of the secrets. The just-released report, Deloitte Human Capital Trends 2017, titled “Rewriting the rules for the digital age,” describes the top ten issues, and gives a set of “new rules” for each.

(This study included a detailed survey with more than 10,400 respondents from 140 countries and dozens of detailed interviews with business and HR leaders around the world.)

Source:, AI And Cognitive Computing Are Changing Organizations Even Faster Than We Thought

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