If close to one-third European Google users decide to opt out of data sharing when GDPR comes into effect in May, it could translate to a 2% impact on the company’s ad revenue, according to a Deutsche Bank note to investors, reported by Business Insider. Deutsche Bank estimates the internet giant earns one-third of its revenue from the continent, and users opting out would reduce ad efficacy by 20%.
Tech giants like Google and Microsoft have had the resources and manpower to get up to speed, but global companies in general are lagging behind on compliance and may incur high costs that way. Only one-third of executives said their company has a GDPR plan in place, although in Europe around 60% reported having a compliance plan, according to an EY survey of global executives.
The adoption of forensic data analytics (FDA), which can help companies reach compliance, rose 51% year-over-year in 2017, according to the study. More focus is also being given to advanced FDA technologies. Close to 40% of respondents said they were planning on implementing robotic process automation as well as AI.
Dive Insight:
Complaints that businesses, at home and abroad, are woefully unprepared for the upcoming set of data privacy regulations are widespread. Many companies have hesitated because the cost of becoming compliant can push into the millions of dollars — although studies have shown that noncompliance is still more costly.
The narrative of GDPR tends to focus on costs associated with becoming compliant or the costs of noncompliance, which, given potential for fines of 4% of global turnover, is unsurprising. But companies could be facing losses on the compliance front too as users begin taking more control over data.
Companies collect a mind-boggling amount of data on the users or affiliates of their platforms. Just ask the woman who asked Tinder for the data it had on her and got 800 pages in response. But under GDPR, users will have more power to decide what information a business collects about them and the right to erasure.
Google makes the majority of its revenue off of its ad business, and such a hit in the European region could break a sweat on CEO Sundar Pichai’s brow. The company may have high hopes for its cloud segment eventually bringing in as much revenue as advertising, but it is still years out from that point, if it hits it at all.
But from the international behemoth to SMBs that handle much smaller amounts of personal data, the shift in the relationship is starting to favor people and data over companies.
Automation of internal processes has led many IT groups to streamline process steps, decrease costs and improve overall business functions. Because of this, a comprehensive IT automation strategy is an easy decision to make for any company looking to not only keep up, but stay ahead of the curve.
Unfortunately, the drive to end-to-end IT infrastructure automation hit a speed bump called the cloud. While the cloud won’t directly replace automation, it does affect what tools and investments an organization makes in its on-site data center. No one will dispute the benefits of an IT automation strategy, but what remains to automate if you move business services to a SaaS or another cloud deployment model?
IT automation trends
It’s critical to invest in data center resources, such as IT infrastructure automation, monitoring or other items, before cloud adoption, but with a cloud strategy, some of those things simply disappear or change into something else. IT infrastructure automation started as simple scripting and gave way to more complex languages and, eventually, workflows and orchestration. The challenge isn’t that the language changed. Organizations removed and relocated apps and infrastructure resources to someone else’s environment.
No cloud environment could exist without some form of automation; self-serviceis a pillar of the cloud. The difference between cloud automation and what’s in the data center is how much of it admins manage. When admins manage IT infrastructure automation on-site, they have complete control, but cloud customers are restricted to what is presented to them. While cloud-based automation and workflows boast some convenient features, it’s doubtful the admin portal will contain everything you had in earlier iterations of the IT environment, and that will take some adjustment.
The change won’t happen overnight
While cloud adoption — in particular SaaS and platform as a service (PaaS) — changes IT operations in many ways, don’t throw out everything that your team currently uses. Configuration management tools, for example, work with on-premises servers and cloud instances, and the major cloud service providers also offer configuration management as a service.
A smart IT automation strategy is still critical to the modern business. Review the many options presented to you as a customer of cloud services. It would be unwise for any cloud vendor to offer complete control of an environment wherein admins could make changes to shared, multi-tenant infrastructure. The move from an IT automation all-star to an automation customer won’t be pretty. No one likes to give up control or flexibility, but it is necessary as part of the move to cloud services.
Cloud and on-premises app deployments coexist in enterprise IT.
IT automation skills
No one likes to give up control or flexibility, but it is necessary to move to cloud services.
The new questions are: How much skill set overlap do admins have from on-site IT infrastructure automation to cloud services? Will the organization end up with an IT infrastructure automation skill surplus as cloud providers take over many tasks? Depending on the skills in an IT organization’s staff, the company might end up paying a lot of money for expertise that the organization no longer needs. Reallocate, or even retrain, the admins that want to learn new skills, such as cloud management; some might not want to. This struggle is not unique when it comes to IT personnel and internal services being replaced by the cloud-based offerings.
Not all automation jobs will disappear. Cloud vendors need IT automation experts now more than ever. But for companies moving to the cloud, internal demand for granular automation knowledge will fade. Infrastructure automation specialists will see a substantial decrease or changeover.
Time to invest in automation resources
Set an IT automation strategy based on a realistic time frame for your organization’s move to the cloud and what kind of cloud service the majority of workloads will go to: infrastructure as a service, PaaS or SaaS. Additionally, determine if an automation setup purchased or improved today could pay for itself before the move to the cloud. If the cloud migration doesn’t go smoothly, delays could make on-premises automation more attractive. These are tough discussions that rarely yield clear answers.
By 2030, a major portion of ERP-related work may be handled by machines. These systems will increase in capability as the amount of data grows and as AI advances. Human-machine interactions will play a major role in business, and well before then.
The importance of human-machine interactions to business was ranked very high by the respondents participating in research by Dell Technologies and the Institute for the Future. The report is based on a survey of nearly 4,000 business leaders. More than eight in 10 (82%) overwhelmingly agreed that they “expect humans and machines will work as integrated teams within their organization inside of five years.”
Further out, by 2030, smart machines will play an important role in ERP. Three of the top four functions that will be offloaded to machines, this survey found, are ERP-related: inventory management, financial administration — invoicing, purchasing orders, etc. — and, in fourth place, logistics. Troubleshooting was number three.
But overall, there is a lot of uncertainty about the technological future.
When asked if “automated systems will free up our time,” the response was split down the middle, with half agreeing and the other half disagreeing.
The answers also indicate questions about capabilities. For instance, respondents were asked whether “technology will connect the right person to the right task, at the right time.” Only 41% agreed, and the remainder disagreed. Respondents were evenly divided around this statement: “Not sure what the next 10-15 years will look like for our industry, let alone our employees.”
In an interview, Danny Cobb, Dell Technologies corporate fellow and vice president of global technology strategy, discussed human-machine interactions and other survey findings.
Cobb sees a wide range of qualitative and quantitative processes and technologies — AI, context and pattern recognition, voice and image recognition — gaining enterprise use. His responses were excerpted and edited.
In 2030, more and more tasks will be offloaded to machines. Three of the top four are ERP-related: inventory management, financial administration and logistics. What does this mean?
Danny Cobb: It’s hard to imagine that that’s the first thing someone thinks about [inventory management, financial administration] in their digital transformation agenda, but it also paints a picture: We’re not as far along or sophisticated as we may think we are if those are still some of the topics that come up.
Does this mean that things like inventory management will be more automated? That something like image recognition might be used to track product as it moves through the supply chain?
Cobb: That’s right. You see image recognition, drone technology and robotic technology assisting with that function. You see maybe more global logistics functions that might be operating in a hybrid cloud or a multi-cloud way that gives a broader insight into all the inventory and material capability of an enterprise, 24/7 and around the globe.
ERP systems will be handling a lot more data from a much wider range of sources. What do those systems begin to look like in the future?
It may not be strictly a physical presence, a personal robot sitting in that room with me, but artificial intelligence itself will complement the team’s function and will provide a useful value.
Danny CobbCorporate fellow and VP of global technology strategy, Dell Technologies
Cobb: At the edge of an enterprise — the edge being wherever the first unit of intelligence begins to exist — there might be a stream of telemetry. It might be all this inventory data. It might be all the input from these drones, or from a global logistics system, or from multiple systems because of supplier-to-supplier linkages. These systems now need to be much more intricately linked than ever before. There is an opportunity for an entirely new platform to come into existence — the intelligent edge of the enterprise that handles this telemetry, that handles any of the immediate compute or storage needs. It takes that information and shares it appropriately with a core data center that might contain additional intelligence from the rest of the enterprise. The edge technologies do the first stage of work, and then, those migrate upstream to a set of core technologies that are responsible for further analysis, long-term storage or broader distribution.
How much data will we be getting from these alternative sources, and what are the challenges to processing it?
Cobb: Artificial intelligence, machine learning sorts of capabilities are going mainstream because the amount of compute that we have has caught up or is catching up with the amount of useful data that’s there to be analyzed. Other instrumented systems [such as autonomous vehicles, building automation systems, jet engines] are throwing off a tremendous amount of data, and we can now afford to process it as it’s being generated. We can now embed processing in just about anything.
A high percentage of those surveyed for this report expect to see more human-machine interactions by 2030. What does that mean?
Cobb: It may not be strictly a physical presence — a personal robot sitting in that room with me — but artificial intelligence itself will complement the team’s function and will provide a useful value. It’s that sort of digital partnership.
How useful is it to think about the world 15 years or so from today?
Cobb: They [customers, users] need to start getting a blueprint that helps them address some of these opportunities or manage some of these risks. What research like this does is to give customers a vehicle for thinking about this. What are the new roles that are going to be created? What are the skill sets that need to come into existence? How might that impact job satisfaction?
Over the past few years, we’ve heard from numerous pundits who have painted a very dystopian picture on the demise of white-collar jobs at the hands of automation.
It makes for attention grabbing headlines but when you examine the facts, automation is about liberation from the mundane and driving digital transformations in the enterprise. It’s true that some jobs will be eliminated by automation, however we don’t see the new digital worker as a replacement for humans, but as an enabler to become an intrinsic part of the fabric of a future workplace.
In the past couple of months, industry experts have stated that digital workforces will assume responsibility for mostly rote, repetitive, and productivity-busting tasks, not entire jobs. In fact, a recent Everest Group blog said, “the fear about the impact on jobs is way overblown.” It also stated that, “it is highly likely it will impact slices of jobs and/or departments that will allow for those employees to be transitioned to higher-value tasks.
This evolution should encourage enterprise executives to consider technologies like Robotic Processing Automation (RPA), fueled by bots to provide automation for repetitive and rules-based tasks that involve structured data. This, of course, makes sense. Who wouldn’t want more time to make a real difference for their company and their customers?
Collaboration Between Employees and Software Robots
A recently-published KPMG report, “Rise of the Humans 2,” indicates that the concept of humans and robots working together to deliver an outcome is becoming increasingly important. Indeed, when employees and smart, enterprise-grade RPA robots – meaning those that understand context, derive meaning, and anticipate change to deliver better and faster outcomes – work collaboratively, it can make for a very powerful partnership.
The removal of monotonous tasks not only makes for happier employees, but also allows them to take on higher-value roles. Shop Direct, one of U.K.’s largest pure-play digital retailers, for example has fraud advisors handling phone calls from distressed customers identifying and verifying fraudulent and genuine purchasing activity. Needless to say, it’s quite a lengthy process.
The company introduced a blended process with manual interventions, where the customer is still calling in and speaking to a person. The RPA-enabled process, robots take care of all the administrative verification box-ticking and new customer account establishment, significantly accelerating time to solution. And the time saved allows the now upskilled fraud analysts to have a more customer-centric conversation. Shop Direct has been able to return 328,000 hours annually (and rising) back to the business thanks to RPA. It is a win-win situation for both customers and employees.
Working together with IBM we are seeing firsthand the power of automation being made accessible to all. We’re helping joint customers like Walgreens deploytheir strategic digital workforces. As a global Blue Prism partner, IBM offers clients deep expertise and a full range of automation solutions—from infrastructure to applications and business processes, in both on-premise and outsourced implementations—fully supported by IBM services and Watson’s AI capabilities.
The primary goal of truly smart software robots is to deliver a wide range of benefits to the business. These fall into three categories:
1. Driving Top Line Value:
Reducing customer churn with faster execution of customer service requests
Increasing insights with reporting of process anomalies
Achieving faster time to market through automated launch processes.
2. Improving Bottom Line Profit:
Reducing “cost to serve” by automating manual processes
Lowering operational risk by collecting every nuance of ever process transaction
Defending against fraud with real time anomaly reporting, while lowering the cost of compliance.
3. Reducing Risk:
Saving and reporting every step that occurs in every process
Ensuring adherence to stringent HIPPA requirements.
What we’ve noticed is that the forward thinking, contemporary enterprises are already imagining the digital worker as an augmentation to our human workforce. A new workforce that allows every person to be far more productive, collaborative and supportive to customers by giving them access to an infinite resources of execution 24 hours a day, offering new levels of service.
I’ve been selling RPA – which includes surface integration, robotic desktop automation (RDA), user interface (UI) optimization, and robotic process automation (RPA) – for over 30 years, and I’ve seen it revolutionize the way a company does business, but only when incorporated into a properly designed system. I’ve seen organizations get many times a purchase’s value in return by using RPA to bridge the gap between their systems, but I’ve also seen the opposite. Too many companies bought RPA software and incorporated it into their systems without significantly re-engineering their business process enough to receive the full benefit, despite my efforts and their internal champions’ efforts.
What Are They Missing?
Today, integration, case management, process reengineering, and newer advanced technologies like natural language processing are coming together to deliver new levels of strategic automation. RPA is a part of this growing toolset, but by itself, it will not ever deliver “end-to-end” transformation. It’s very good at the tactical replication of “manual” human work against older legacy systems. Just as an automatic car is not the same as an autonomously driving intelligent vehicle, RPA by itself is not end-to-end automation.
In fact, the word automation means something significantly bigger. It means that people are using technology to deliver transformation and simplification. Demand for change is coming in faster than ever. Real change now comes from the consumption side (either B2-B or B2-C). Buyers want to engage differently, and enterprises must change to react (and, hence, compete) with how their customers want to transact, which is at any time and on any device.
Use a Strategy that Reworks Your Workflow
Strategic automation technologies are growing at a faster pace today than tactical ones. Did you know you can start building and maintaining new strategic applications significantly faster and cheaper than you’ve ever been able to do so, often in days or weeks? You can build end-to-end journeys and processes without coding by using model-driven platforms that scale to meet IT needs while allowing for business-driven change. That spells freedom for so many organizations seeing complexity in their tools rocket their agility and usability to obsolescence.
Today, you can build and deploy on your choice of platform, be it a public cloud, private cloud, or mixed (or change your mind and simply switch later). You can deliver applications to any current or future device without even thinking about it, even engaging with new channels like chatbots and virtual assistants. Within this single platform, you have AI built in, and it’s making real-time recommendations for the Next Best Action at every interaction point, often even before your customer reaches out to you.
Wrapping AI in front or around RPA does not make RPA any more intelligent. In fact, most RPA advertised as AI or “cognitive” is simply just a repackaging of ever-evolving Optical Character Recognition (OCR) technologies. Know your market. Who actually delivers on promises today with real, functioning technology?
Make Sure You Really Do Tap RPA’s Power
RPA is powerful. It can replicate repetitive, manual tasks to make an employee’s experience better and more productive. Using AI as a feed to RPA by allowing robots to consume unstructured and structured data is a very real and very good thing. AI, machine learning, and intelligent OCR are all things that can help drive more automation of old manual tasks through RPA, but fundamentally you’re still just automating the same old manual tasks. You’re repaving the cowpath with increasingly better tar, but you aren’t building a new superhighway. However, redesigning experiences by embedding AI at the heart of every customer interaction to drive real-time change will have a more significant impact on your business over the medium and long term.
Use RPA to accelerate your transformation, to connect systems that are otherwise not connectable, or to make your employees happier and more productive. But please do not believe that RPA is your endpoint for transformation. The real work is much harder — and the impacts far more significant.
Nothing gets the Silicon Valley-obsessed media more excited than watching the online mud-wrestling of two tech titans, especially when the fight is over the hottest topic of the day: Will AI destroy our jobs or will it be a force for good?
It all started with Elon Musk declaring that “robots will be able to do everything better than us,” creating the “biggest risk that we face as a civilization.” To which Mark Zuckerberg responded that the “naysayers” drumming up “doomsday scenarios” are “pretty irresponsible.” Musk retorted on Twitter (where else?) “I’ve talked to Mark about this. His understanding of the subject is limited,” and Zuckerberg blogged on Facebook (where else?) that he is “excited about all the progress [in AI] and it’s [sic] potential to make the world better.”
And so it goes. I don’t agree with the notion that only people who are actually doing AI can comment on AI and I’m sure both Musk’s and Zuckerberg’s understanding of AI is not limited. Like the rest of us, however, they inject into the debate their own biases, perspectives, and ambitions. It may help anyone interested in the question of what AI will do or not do to our jobs and civilization to study its history (you may want to start here), to look for evidence refuting what we believe in, and to assessments of the current and future impact of AI technologies that are based on relevant data analyzed with minimal assumptions.
Surveys, interviews and conversations with the people that actually make decisions about creating or eliminating jobs are an example of the latter category and they often serve as the basis for market landscape descriptions and better-informed speculations from industry analysts. A recent case in point—and recommended reading—is “Automation technologies, Robotics, and AI in the Workplace, Q2 2017” from Forrester’s J.P. Gownder (his blog post on the report is here).
Gownder and his Forrester colleagues discuss in detail (33 dense pages instead of 140 characters) a dozen “automation technologies”—all based on what we now generally refer to as “artificial intelligence”—that were selected because they play a role in either eliminating or augmenting jobs, require long-term planning for maximum impact, and (most importantly, in my opinion), generate questions from Forrester’s clients. In addition to assessing the developmental stage and long-term impact on jobs and businesses, Forrester provides definitions of the AI technologies/categories they discuss, valuable simply because definitions are often sorely missing from discussions of “artificial intelligence.”
Here is my summary of the 6 AI technologies that will have the most impact on jobs—positive and negative—in the near future:
Customer Self-Service: Customer-facing physical solutions such as kiosks, interactive digital signage, and self-checkout. Improved by recent innovations (better touchscreens, faster processors, improved connectivity and sensors), it is also entering new markets and applications—a prime example being the experimental Amazon Go convenience store. Example vendors: ECRS, Four Winds Interactive, Fujitsu, Kiosks Information Systems, NCR, Olea Kiosks, Panasonic, Protouch Manufacturing, Samsung, and Stratacache.
AI-Assisted Robotic Process Automation: Automating organizational workflows and processes using software bots. Analyzing 160 AI-related Deloitte consulting projects, Tom Davenport found it to be one of the fastest growing AI applications, an observation confirmed by Forrester. Example vendors: Automation Anywhere, Blue Prism, Contextor, EdgeVerve Systems, Kofax, Kryon Systems, NICE, Pegasystems, Redwood Software, Softomotive, Symphony Ventures, UiPath, and WorkFusion.
Industrial Robots: Physical robots that execute tasks in manufacturing, agriculture, construction, and similar verticals with heavy, industrial-scale workloads. The Internet of Things, improved software and algorithms, data analytics, and advanced electronics have contributed to a wider array of form factors, ability to perform in semi- and unstructured environments, and the “intelligence” to learn and operate autonomously. A rising sub-category is collaborative robots (cobots), working safely alongside humans. Example vendors: ABB, Aethon, Blue River Technology (agriculture), Clearpath Robotics (autonomous, multiterrain), Denso, FANUC (traditional robots and cobots), Kawasaki, Kuka, Mitsubishi, Nachi Robotics, OptoFidelity, RB3D (cobots), Rethink Robotics (cobots), and Yaskawa.
Retail and Warehouse Robots: Physical robots with autonomous movement capabilities used in retailing and/or warehousing. Picking up objects is still the biggest challenge, but retailers such as Hudson’s Bay and JD.com, and of course Amazon, are investing in potential solutions. Example vendors: Amazon Kiva Systems (structured environments), Fetch Robotics (unstructured), Locus Robotics (unstructured), and Simbe Robotics (retail scanning robots for product restocking).
Virtual Assistants: Personal digital concierges that know users and their data and are discerning enough to interpret their needs and make decisions on their behalf. Developed for the consumer market just a few years ago, these assistants can be used by companies in a business-to-consumer setting (e.g., answer questions at home or augment the work of call center employees) or inside the business organization (e.g., serve as subject matter experts or support business processes). Example vendors: Amazon Alexa, Apple Siri, Dynatrace for ITSM, Google Now and Google Assistant, IBM Watson conversational interface, IBM Watson Virtual Agent, IPsoft Amelia, Microsoft Cortana, Nuance Communications Nina, and Samsung Bixby.
Sensory AI: Improving computers ability to identify, “understand,” and even express human sensory faculties and emotions via image and video analysis, facial recognition, speech analytics, and/or text analytics. Example vendors: Affectiva, Amazon Lex, Amazon Rekognition, Aurora Computer Services, Caffe, Clarifai, Deepomatic, Ditto, Equals 3 Lucy, FaceFirst, Google Cloud Platform APIs, HyperVerge, IBM Watson Developer Cloud, KeyLemon, Linkface, Microsoft Cognitive Services, Microsoft Cortana Intelligence Suite, ModiFace, Nuance Communications, OpenText, Revuze, Talkwalker, and Verint Systems.
The first 4 categories have been around for a while (Forrester calls them “mature”) but have recently become energized by hardware and software innovations. It is interesting to note that the key reason for the recent excitement about and fear of AI—the rapid advancement in a number of narrow AI tasks (e.g. object identification) due to improvements in deep learning techniques—has not contributed greatly to the newly-found sexiness of these 4 categories. But deep learning has been a key contributor to the nascent success of the other 2 hot categories—virtual assistants and sensory AI. My general conclusion from these observations is that the excitement (and fear) generated by specific “triumphs” of AI technologies can obscure for us a very fundamental fact of technology adoption throughout history, including recent history—it takes a very long time. This has important implications for our assumptions and projections regarding the question when will AI eliminate (lots of) jobs.
It’s tough to make predictions about the timeframe and magnitude of job elimination, especially when we consider the future of employment (to paraphrase a very wise man). But the difficulties inherent in saying anything about the future, especially the future of jobs in a dynamic, constantly evolving, and multi-faceted economy (e.g., persistent low wages may postpone the adoption of robots), have never stood in the way of people writing and/or analyzing and/or speaking for fame and fortune (or more simply, for continuous employment).
The current cycle of here-are-authoritative-numbers-on-how-many-jobs-will-be-eliminated-by-AI started 4 years ago by two Oxford academics (47% percent of jobs in the US are at risk of being automated in the next 20 years). Forrester’s analysts could not resist the much in-demand forecasting exercise and, in what became “one of the five best-read among all reports at Forrester,” estimated that automation will destroy 17% of US jobs by 2027. But, unlike many other commentators on the subject, they also looked at the glass-half-full and estimated that automation will add 10% of new jobs to the US economy by 2027, for a net loss of 7%.
Whether it will be 7% or 47% or any other quantitative or qualitative speculation about the future impact of AI on employment, the debate over when and how muchdoes not even take into consideration the question of if. Will robots really “be able to do everything better than us,” as Musk believes, and not just in 20 or 100 years, but anytime in the future? I know, it’s tough to make predictions, especially about the future of technology. What is certain is that inquiry minds steeped in the scientific ethos, such as Musk’s, should consider all possibilities and avoid making dogmatic statements, either of the AI-will-destroy-civilization type or AI-will-cure-all-diseases kind. Why not consider the possibility that intelligent machines will not take over because they will never be human and that the futile quest for “human-level intelligence” has actually slowed down progress in AI research?
There is no question that we will continue to see in the future the same disruption in the job market that we have witnessed in the last sixty-plus years of computer technology creating and destroying jobs (like other technologies that preceded it). The type of disruption that has created Facebook and Tesla. Facebook had a handful of employees in 2004 and today employs 20,000. Tesla was founded in 2003 and today has 33,000 employees. Whether AI technologies progress fast or slow and whether AI will continue to excel only at narrow tasks or succeed in performing multi-dimensional activities, entrepreneurs like Zuckerberg and Musk (and Jack Ma and Vijay Shekhar Singh Sharma and Masayoshi Son) will seize new business opportunities to both destroy and create jobs. Humans, unlike bots and robots (now and possibly forever), adapt to changing circumstances.
Change management is often the most difficult part of an IT project, regardless of the technology that’s being implemented. Many companies struggle with getting buy in from key stakeholders and encouraging adoption when end users have been doing things a particular way for years and are resistant to change.
This is especially true when implementing solutions that are as transformative as automated Enterprise Content Management (ECM) and Enterprise Information Management(EIM). New technologies have the power to dramatically improve productivity, profitability and innovation. They also can cause significant changes to staffing needs, workflow and day to day operations. This often makes it challenging to ensure that the change goes smoothly and that the company exploits the full benefits of automation.
More effective change management
In order to make sure new automation solutions have the high-level backing to be implemented successfully and that employees actually use them, companies should follow change management best practices and tailor their approach to the unique challenges presented by automation. This means working closely with key people at all levels of the company, addressing major challenges and focusing on the benefits that the new solutions will bring.
1. Address challenges head on – Automation is a disruptive technology that will have a major impact on job outlook and the way people work. Companies shouldn’t avoid these issues but instead embrace them and focus on positives. Although automation tools will change the way work is being done, it also frees workers time to focus on more creative and innovative areas. New tools also allow workers to perform their tasks faster and with fewer headaches, reducing stress and making work more enjoyable.
2. Emphasize integration – Many people postpone adoption because they are afraid their productivity will take a hit while learning new systems. Modern ECM solutions are designed to integrate well with existing systems, letting users access the content and information they need directly from the Enterprise Resource Planning (ERP) or Human Resource Information System (HRIS) screens they are familiar with. This means that users don’t have to learn an entirely new system and casual users can work within their Outlook applications without even opening the ECM application.
3. Leverage flexibility – By focusing on the flexibility of automated ECM solutions, companies can help their employees see the benefits of using them. They often offer multiple end user clients and ways to access content, from mobile apps and web browsers to Outlook. This allows users to access their data and work with the system however it suits them.
4. Make training a priority – When implementing any new solution, effective training is critical, helping to educate about benefits, ensure effective usage and encourage adoption. In-house teams and consultants from the solution provider can help with implementation and training, easing the burden on your own IT staff.
5. Monitor progress and make improvements – No solution implementation is perfect in its first weeks or months. The project team should keep working past rollout, monitoring adoption rates and problem areas while developing solutions to address any issues.
Companies that leverage the power of automated EIM and ECM solutions can benefit from major increases in productivity and lower labor costs, but their efforts will not be effective unless they implement effective change management programs. By taking steps to minimize negative impacts of the change and smooth the transition, your organization can increase adoption and ROI for the solutions.
In 1900, 30 million people in the United States were farmers. By 1990 that number had fallen to under 3 million even as the population more than tripled. So, in a matter of speaking, 90% of American agriculture workers lost their jobs, mostly due to automation. Yet somehow, the 20th century was still seen as an era of unprecedented prosperity.
In the decades to come, we are likely to see similar shifts. Today, just like then, many people’s jobs will be taken over by machines and many of the jobs of the future haven’t been invented yet. That inspires fear in some, excitement in others, but everybody will need to plan for a future that we can barely comprehend today.
This creates a dilemma for leaders. Clearly, any enterprise that doesn’t embrace automation won’t be able to survive any better than a farmer with a horse-drawn plow. At the same time, managers need to continue to motivate employees who fear their jobs being replaced by robots. In this new era of automation, leaders will need to identify new sources of value creation.
Identify Value At A Higher Level
It’s fun to make lists of things we thought machines could never do. It was said that that only humans could recognize faces, play chess, drive a car, and do many other things that are automated today. Yet while machines have taken over tasks, they haven’t actually replaced humans. Although the workforce has doubled since 1970, unemployment remains fairly low, especially among those that have more than a high school level of education. In fact, overall labor force participation for working age adults has risen from around 70% in 1970 to over 80% today.
Once a task becomes automated, it also becomes largely commoditized. Value is then created on a higher level than when people were busy doing more basic things. The value of bank branches, for example, is no longer to manually process deposits, but to solve more complex customer problems like providing mortgages. In much the same way, nobody calls a travel agency to book a simple flight anymore. They expect something more, like designing a dream vacation. Administrative assistants aren’t valuable because they take dictation and type it up on a typewriter, but because they serve as gatekeepers who prioritize tasks in an era of information overload.
So the first challenge for business leaders facing a new age of automation is not try to simply to cut costs, but to identify the next big area of value creation. How can we use technology to extend the skills of humans in ways that aren’t immediately clear, but will seem obvious a decade from now? Whoever identifies those areas of value first will have a leg up on the competition.
Innovate Business Models
Amazon may be the most successfully automated company in the world. Everything from its supply chain to its customer relationship management are optimized through its use of big data and artificial intelligence. Its dominance online has become so complete that during the most recent Christmas season it achieved a whopping 36.9% market share in online sales.
So a lot of people were surprised when it launched a brick and mortar book store, but as Apple has shown with its highly successful retail operation, there’s a big advantage to having stores staffed with well trained people. They can answer questions, give advice, and interact with customers in ways that a machine never could.
Notice as well that the Apple and Amazon stores are not your typical mom-and-pop shops, but are largely automated themselves, with industrial age conventions like cash registers and shopping aisles disappearing altogether. That allows the sales associates to focus on serving customers rather than wasting time and energy managing transactions.
Redesign Jobs
When Xerox executives first got a glimpse of the Alto, the early personal computer that inspired Steve Jobs to create the Macintosh, they weren’t impressed. To them, it looked more like a machine that automated secretarial work than something that would be valuable to executives. Today, of course, few professionals could function without word processing or spreadsheets.
We’re already seeing a similar process of redesign with artificially intelligent technologies. Scott Eckert, CEO of Rethink Robotics, which makes the popular Baxter and Sawyer robots told me, “We have seen in many cases that not only does throughput improve significantly, but jobs are redesigned in a way that makes them more interesting and rewarding for the employee.” Factory jobs are shifting from manual tasks to designing the work of robots.
Lynda Chin, who co-developed the Oncology Expert Advisor at MD Andersonpowered by IBM’s Watson, believes that automating cognitive tasks in medicine can help physicians focus more on patients. “Instead of spending 12 minutes searching for information and three with the patient, imagine the doctor getting prepared in three minutes and spending 12 with the patient,” she says.
“This will change how doctors will interact with patients.” she continues. “When doctors have the world’s medical knowledge at their fingertips, they can devote more of their mental energy to understanding the patient as a person, not just a medical diagnosis. This will help them take lifestyle, family situation and other factors into account when prescribing care.”
Humanity Is Becoming The Scarce Resource
Before the industrial revolution, most people earned their living through physical labor. Much like today, many tradesman saw mechanization as a threat — and indeed it was. There’s not much work for blacksmiths or loom weavers these days. What wasn’t clear at the time was that industrialization would create a knowledge economy and demand for higher paid cognitive work.
Today we’re seeing a similar shift from cognitive skills to social skills. When we all carry supercomputers in our pocket that can access the collective knowledge of the world in an instant, skills like being able to retain information or manipulate numbers are in less demand, while the ability to collaborate, with humans and machines, are rising to the fore.
There are, quite clearly, some things machines will never do. They will never strike out in Little League, get their heart broken, or worry about how their kids are doing in school. These limitations mean that they will never be able to share human experiences or show genuine empathy. We will always need humans to collaborate with other humans.
As the futurist Dr. James Canton put it to me, “It is largely a matter of coevolution. With automation driving down value in some activities and increasing the value of others, we redesign our work processes so that people are focused on the areas where they can deliver the most value by partnering with machines to become more productive.”
So the key to winning in the era of automation, where robots do jobs formerly performed by humans, is not simply more efficiency, but to explore and identify how greater efficiency creates demand for new jobs to be done.
We got so sick of this nonsense, we just went out and surveyed 400 enterprise automation and AI decision makers across the Global 2000, split across IT and business operations functions, and hit them with some very straight poignant questions about their attitudes, satisfaction levels and genuine plans for both AI and Automation across their business operations.
But let’s start with the hype: AI and Machine Learning is now one of the most critical strategic directives being dictated from the C-Suite onto the operations function
81% of operations leaders are feeling the pressure from their bosses to reduce the reliance on mid/higher skilled labor, viewing AI and Machine Learning as increasingly important or even mission-critical directives to drive this. Only cost reduction beats this out as a priority, but as we all know, we can’t reduce costs much further without investing in our digital underbellies:
What’s clear is that enterprises are frantically evaluating their talent (81%) and looking to collapse these silos in the middle/back offices to improve their customer experiences. And they see AI, Machine Learning, and process automation as the levers to achieve this.
So let’s summarize the key findings from the study, and you can download your copy here :
Automation is the number one strategic priority four-fifths of enterprise C-Suites are placing on their operations. Enterprises see AI and machine learning (81%) and process automation and robotics (82%) as important C-suite directives toward operations strategy – higher than any priority other than cost reduction.
98% of enterprises have an automation agenda, but a third already have embedded it into their service delivery. Every organization today needs to have an automation strategy and that is reflected in the responses in our survey; only 2% suggest not having a strategy as of now, while 20% are in the process of formulating their strategy. Already, 31% of enterprises are integrating automation into the fabric of their service operations. Others are setting up dedicated CoEs (18%) and working with service providers (13%).
Corporate leadership and IT are most active driving the automation agenda. Decision making is increasingly being led by the CEO (54%), CIO/IT Director (57%), and CFO/Finance Director (35%). Additionally, a diverse group of automation influencers and stakeholders emerge, notably the finance department (49% consider as influencers), procurement (47%), data center managers (51%) and purchasing managers (48%).
Deployments of RPA as well as AI starting to scale out with varying degrees of maturity. RPA is seeing rapid adoption and AI will become mainstream in two years. More than 70% of customers are planning to deploy RPA over the next two years and more than 50% believe that AI will be applicable for a broad set of processes within the same timeframe. Therefore, investments, planning, and training of talent around the notion of Intelligent Automation is pivotal for staying competitive.
Many customers are in an automation dichotomy: they want automation to drive long-term quality and agility, but need rapid cost takeout to sell the ROI. For a significant number of enterprises, their automation strategies are expected to deliver, primarily, better quality of operations (52%), more workforce agility and scalability (49%), and superior data accuracy (48%). Only a minority of respondents are seeking short-term cost savings (21%) or a way to displace employees (12%). However, when you ask what is inhibiting automation adoption, the top criterion is that the “Immediate cost savings are not high enough” (35%), indicating a disconnect in expected benefits and business case.
Satisfaction with initial automation deployments is mixed as customers struggle to define success and execute against it. Only a little over half the enterprises (58%) that have gone down the RPA path are satisfied with the level of business value and cost savings from their implementations thus far. Enterprises that have yet to explore technologies like RPA point to struggles with establishing business cases (41%), while 30% expect that automation capabilities will be absorbed by enterprise applications in the next five years. In addition, many enterprises struggle with developing an effective centralized governance structure for automation initiatives, citing that projects are too siloed, don’t have success milestones established, and lack organized training to use the tools effectively.
Despite the growing pains, RPA is starting to be used effectively in this era of innovation and the current satisfaction results reflect this. IT operations have the most satisfied clients for both cost savings (70% satisfied) and business value (72% satisfied), followed by marketing (70% satisfied with cost) and procurement (63% satisfied with business value). Regardless of the level of satisfaction on cost and business value as of today, operations leaders are making incremental progress, one process at a time. In the interim time between sawing off broken processes and legacy systems and replacing them with costly new systems and services, RPA seems to be helping enterprises get some level of access to new business value from their current processes.
Automation Centers of Excellence (CoE) proving a major success. Of organizations with the CoE approach, 88% believe that the automation CoE has been effective in delivering business value (scores of 4 or 5 on a 5-point scale). HfS has been hearing advisors in the RPA arena claim many clients are failing miserably with their CoEs, but this data proves, beyond doubt, these are scare tactics and those customers who are centralizing automation projects into one governance team are already reaping significant benefits.
Forrester
The Technology Beacon 