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Yugal Joshi

Yugal Joshi is a member of the IT services team and assists clients on topics related to mobility, analytics, digital, cloud, and application services. Yugal’s responsibilities include leading Everest Group’s cloud and digital services research offerings. He also assists enterprises in adopting emerging technologies and methodologies such as, Agile, DevOps, software-defined infrastructure. To read more, please see Yugal’s bio.

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

By | Sherpas in Blue Shirts

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

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

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

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

 

AI’s Potential in Transforming CX is Vast

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

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

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

 AI in the Employee Experience

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

AI in the Supplier and Partner Experience

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

AI in the Society Experience

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

Moving from Customer to Stakeholder Experience

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

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

 

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

By | Sherpas in Blue Shirts

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

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

Here are four key reasons this is the case.

Software is Becoming Non-deterministic and Intelligent

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

Testing AI Biases and Outcomes is not Easy

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

The Autonomous Vehicle Framework is Being Mirrored in Technology Systems

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

The Open Source Ecosystem is Becoming Indispensable

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

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

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

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

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

By | Sherpas in Blue Shirts

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

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

So, what is plaguing enterprises’ digital transformation agenda?

Not Moving the Revenue Needle

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

Digital Fatigue

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

Challenges in CX to Business Attribution

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

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

Related: Important Lesson For Companies Undertaking Digital Transformation

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

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

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

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

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

By | Sherpas in Blue Shirts

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

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

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

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

Four Key Issues to Address for Success

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

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

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

By | Sherpas in Blue Shirts

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

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

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

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

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

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

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

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

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

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

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

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

By | Sherpas in Blue Shirts

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

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

The obvious answer is, they can’t.

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

Who’s to Blame?

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

What Should Technologists Do?

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

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

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

What Should Enterprises Do?

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

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

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

AI as-a-service: Big Tech Has Provided Platforms, But Where Will the Apps Come From? | Sherpas in Blue Shirts

By | Sherpas in Blue Shirts

Our digital services research suggests that 40 percent of enterprises have adopted AI in some shape or form. Of course, they’re relying on the foundational platforms from BigTech firms like Amazon, Google, Microsoft, and TenCent – and even from smaller tech start-ups –to drive meaningful business cases.

But while they can leverage Amazon Sage Maker or Microsoft Bot Framework to do the heavy lifting, they still need a meaningful application that operates on the platform in order to solve their business problems.

Enterprise Challenges with AI

Granted, tech vendors like Oracle, Salesforce, and SAP have made initial progress in integrating AI into their application platforms. But their products are very broad and focus on their own planned areas. And enterprises have multiple, complex requirements that fall outside the purview of these generic applications. Therefore, most enterprises must also build their own AI engines to get meaningful insights from these large-scale applications.

Essentially left on their own, enterprises have to build their own applications to address their needs. But Everest Group digital services research indicates that 60 percent of leading digital adopters struggle for the right talent. And because they lack high-caliber AI talent, they can only take scratch some of the surfaces necessary to create truly valuable apps that can deliver specific business outcomes.

Can Start-ups Help?

We believe this leaves the market wide open to an impending burst of start-ups that can build AI-led niche applications to solve industry-specific business problems. Areas like fraud detection in insurance, compliance management in financial services, and industry-oriented employee engagement and customer experience can significantly benefit from these types of applications. But the key to success here – for both enterprises and these start-ups themselves – will be a focus on building applications for specific business use cases, rather than broad-based platforms. Indeed, AI applications focused start-ups need to commoditize the platform and focus squarely on the application logic that leverages AI.

Enterprises will need to partner or invest in these start-ups to incentivize suitable AI-led applications. Going forward these enterprises should focus to procure off the shelf applications to drive business outcomes than over investing in AI platforms. Unlike today, which requires massive bandwidth to build on top of BigTech AI platforms, these applications will be easy to configure, train, and consume.

The Role of System Integrators

Given that system integrators (SIs) have a strong enterprise DNA and understand business processes, systems, and technologies very well, they can build these applications for enterprises leveraging a BigTech platform. Some of them have made early inroads in areas such as service desk, customer support, and IT operations. However, there is a massive opportunity for business applications and processes. SIs will need to develop point as well as platform-led AI applications that can be plug-and-play in an enterprise set-up. These applications must be pre-trained on industry-fed data for quick deployment and better time to value.

The Road Ahead

It is apparent that enterprises cannot leverage the power of AI on their own. They need to rely not only on large technology vendors, but start-ups and their service partners as well. Though each enterprise must have a pool of valued AI resources, they should not go overboard in investing in them. As AI is not enterprises’ core business, they’re better off letting it be done by companies that are experts.

However, if the AI industry continues to generate next-generation smarter platforms that are do heavy lifting for AI without creating meaningful applications, we will surely see one more AI winter in the near horizon.

AI Helping DevOps: Don’t Ask, Don’t Assume – KNOW What Users Really Want | Sherpas in Blue Shirts

By | Sherpas in Blue Shirts

With DevOps’ core goal of putting applications in users’ hands more quickly, it’s no surprise that many enterprises have started to release and deploy software up to five times a month, instead of their earlier once-a-quarter schedule. Everest Group research suggests that over 25% of enterprises believe DevOps will become the de-facto application delivery model.

However, there continues to be a disconnect between what business users want and what they get. To be fair to developers and IT teams, this disconnect is due, in part, to end-users’ difficulty in articulating their needs and wants.

Enter AI Systems

AI Systems have strong potential to help product management teams cut through the noise and zero-in on the features their users truly find most valuable. Consider these areas of high impact:

  1. Helping developers at run time: Instead of developers having to slog through requirements, feature files, and feedback logs – and likely miss half the input – AI-led “code assister” bots can help them, during the actual coding process, to ensure that the requested functionality is created
  2. Prioritizing feedback: Rather than wasting time on archaic face-to-face meetings to prioritize features requested in the dizzying amount of feedback received from users, enterprises should build an AI system to prioritize requests from high to low, and dynamically change them as needed based on new incoming data
  3. Stress testing feedback: After prioritization, AI systems should help enterprises segregate the features users really want, versus those they think they want. AI can do this by crunching the massive volume of feedback data though machine learning and finding recurring patterns that suggest consensus. The feedback data should also be fed back to business users to educate them on market alignment of demanded and desired features
  4. Aligning development, QA, and production: Through its inherently neutral perspective, an AI system can smooth through the dissonance among the different teams by crunching all the data across the feedback systems to outline disconnects and create the alignment needed to satisfy end-user needs
  5. Predicting features: While this is still far-fetched, enterprises and technology vendors should work toward AI solutions that can predict features that will be requested in the next sprint based on historical data. In fact, AI systems should be able to analyze data across other enterprises as well to suggest relevant features to developers. The predictions could then be validated with real feedback from beta users, and the AI system further trained based on the validations

There are multiple other areas in which AI can potentially assist in understanding what the users want. For example, as we discussed in earlier research, AI can help developers create secure, performance-tuned, and production-ready code without being bogged down by typical feedback on features from the field.

What about Budget?

The good news is such an AI system will not burn a massive hole in enterprises’ budgets and should not require the zillions of data points that most typical, complex AI systems do. I believe these systems can be based on simple log data, performance feedback cycles, feature files databases, requirements catalogues, and other already existing assets. If that’s the case, they have great potential to help enterprises develop software their end-users really want.

Have you deployed AI in your Agile DevOps delivery cycle? I’d love to hear about it at [email protected].

Will AI Take the “H” Out of HR? Not if Done Well | Sherpas in Blue Shirts

By | Sherpas in Blue Shirts

Most people talk about how AI will transform both the transactional and strategic HR functions across recruiting, performance management, career guidance, and operations. Technology vendors such as Belong.co, Glider.ai, Hirevue, MontageTalent, and Talla, are often quoted as transforming the HR functions across different facets.

So the burning question here is, will AI technologies eventually transform the HR function for good? Or will it dehumanize it? Let’s look at some fundamental issues.

HR Works within the Enterprise’s Constraints

Focuses on creating individual-centric training, incentives, performance management, and career development plans are noble. However, HR may well not have the budget, and the organization’s processes may well not allow these in reality. Most organizations have a fixed number of roles (bands) and employees are fit into them. And there is a fixed L&D budget, which is treated as a cost that prevents meaningful investment in programs for individual employees.

HR Hardly Understands Technology

Most of today’s enterprises are looking to hire “digital HR” specialists who understand the confluence of technologies and HR. Because very few exist, the businesses themselves need to teach and handhold non-digital HR people about the value of AI principles in their mundane tasks, such as CV/resume shortlisting, as well as in their creative work, such as performance management and employee engagement.

Senior Leadership’s Flawed Perception of HR

While every enterprise claims that their employees are their greatest asset, they don’t always perceive HR to be a strategic function. Many senior executives view HR as a department they need to deal with when team members are joining or leaving the organization, and that everything in between is transactional. This perception does not allow meaningful investments in HR technologies, much less AI-based services. As AI systems are comparatively expensive, they require senior leadership’s full support for business case and execution, and HR will likely not be on the radar screen.

HR’s Flawed Perception of Itself

Most HR departments consider themselves to be recruitment, training, and performance management engines. They fail to strategically think about their role as a crucial enabler of a digital business. Because most HR executives don’t perceive themselves to be C-level material, their view becomes self-fulfilling. Many HR executives also silently fear, that their relevance in the organization will be eliminated if seemingly rote activities are automated by AI.

I believe that AI systems provide tremendous opportunities for HR transformation – if the HR function is willing to transform. It needs to make a strong business case for adopting AI based on hard facts, such as delay in employee hiring, number of potential candidates missed due to timelines constraints, poor retention because of gaps in performance management, inferior employee engagement due to limited visibility into what they really want, and compliance issues.

However, there is a tightrope to be walked here. As HR is fundamentally about humans, AI should be assisting the function, not driving it. A chatbot, which may become the face of HR operations, is just a chatbot. AI should be leveraged to automate rote transactional activities and mundane HR operations, and help enhance the HR organization.

Unfortunately, many enterprises myopically and dangerously believe that AI should lead HR. Because HR is not about AI, those that do are bound to dehumanize HR and drive their own demise.

HR’s broader organizational mandate will have to change for AI adoption to truly succeed without dehumanizing the function and its purposes. Doing so will not be easy. Various enterprises may take a shortcut, such as deploying chatbots for simple HR operations, to appease their desire for a transformational moniker. But in today’s digital age, these organizations will be short lived. Enterprises that weave AI into their HR functions – akin to ambient technology – to fundamentally enhance employee experience, engagement, and creativity, will succeed.

Dig-It-All Enterprise: Dressing up Legacy Technology for Digital Won’t Work Anymore | Sherpas in Blue Shirts

By | Sherpas in Blue Shirts

I have long been a proponent of valuing the legacy environment, and I am still a great believer in legacy technologies. But despite the huge investments enterprises have made in their legacy environment, even though they’re desperately trying to use bolt-ons and lift and shift to avoid going the last mile, and regardless of their belief that their core business functions shouldn’t be disrupted, time is running out for piecemeal digital transformation where old systems are dressed up to support new initiatives. It simply won’t work any more. Why?

Digital enterprises need different operating models

Enterprises are finally realizing that there’s dissonance between the execution rhythm of a digital business and its legacy technology. Although they can spend millions to make the legacy technology run the treadmill to keep up with digital transformation, the enabling processes and people skills will never catch up. For this, enterprises will have to invest in fundamentally different operating models in the way technology is created and consumed, the way in which people are hired and reskilled, and the way in which organizational culture is evolving towards speed and agility.

Legacy technology is breeding legacy people

Our research suggests that 80 percent of modernization initiatives are simply lift and shift to newer infrastructure. In those that impact applications, less than 30 percent of the code is upgraded. Therefore, most technology shops within enterprises take comfort in the fact that their business can never move out of specific legacy technologies. They believe the applications and processes are so intertwined and complex that the business will never have the courage, or the budget, to transform it. This makes them lethargic, resulting in a large mass of people without incentive to innovate. Such established blind rules need to be challenged. Enterprises need to set examples that everything is on the table and a candidate for transformation. The transformation may be phased, but it will be done for sure. This will keep people on their toes, and incentivize them to upskill themselves and drive better outcomes for the business.

Legacy technology is simply not up to the challenge

Enterprises are realizing that there is a limit to which they can patch their technologies to beautify them for the digital world. Our research suggests that every one to two years enterprises realize their mistakes as the refurbished legacy technology becomes legacy again. They are now believing they will either have to take the hard route of going the last mile in transforming, or shut out their legacy technology and start from a blank slate. This is a difficult conundrum, as 60 percent of enterprises lack a strong digital vision and, therefore, are confused about their legacy technology future.

Organizations that continue to believe they can put band-aids on their legacy technology and call it digital have lessons to learn from Digital Pinnacle Enterprises. Our research suggests that these businesses, which are deriving meaningful benefits of their digital initiatives, are 36 percent more mature in adopting digital technologies than their peers. These enterprises understand the limitation legacy technologies put on their business. Though they realize they cannot get rid of the legacy technology overnight, they also understand they have to move fast or get outdone in the market.

The courageous enterprises that understand that legacy technology is hard to change, is built on monolithic architectures, requires humongous investment to run, and doesn’t allow the business the flexibility to adapt to market demand, and are willing to “Dig-It-All” for digital, will succeed in the long run.

What has your experience been with legacy technologies in digital transformation initiatives? It would be great to hear your views, whether good, bad, or ugly. Please do share with me at [email protected].