AI = The Future of “H” in HR : Introducing AIQRATE’s consulting offerings for CHRO/CPO/HR leaders

AI = The future of “H” in HR . In today’s competitive businesses , the role of AI in planning, operations & strategy has transformed from being a competitive differentiation to a competitive necessity . The age of “ trust me , this will work” is over. In the current business mandate , where HR is held accountable for delivering business results , it has become imperative to harness the power of AI . AI can elevate HR from a tactical support function to a strategic transformative function . HR business function disruption thru Talent Sciences : business capability of using AI and algorithmic modeling to drive HCM decision making will form the backbone of HR function.

Introducing AIQRATE’s consulting offering for Chief Human Resource Officer (CHRO) / Chief people officer (CPO) / Chief Talent officer (CTO) /HR Leaders working across Enterprises , GCCs , SMBs , Startups , Public Institutions : 

1. AI master class session : Contextualized for CHRO , CPO : demystify AI , AI strategy canvas , AI landscape & wide applications , HR vale chain interventions
2. AI advisor on-demand : Build AI led decision making strategies and processes across the HR value chain and strategic interventions
3. AI talent mapping strategies : Execute AIQRATE “T-REX” framework for building enterprise wise AI skilling & learning regime
4. AI led interventions for CHRO/CPO : Reimagine HR domain , HR business function problems and scenarios leveraging AIQRATE consulting expertise
5. Analytics to AI maturity assessment : Gauge your enterprise AI adoption maturity with AIQRATE “Elevate” transformation journey framework

AIQRATE’s extensive yet bespoke consulting offerings for CHRO/CPO/HR leaders focuses on building AI led strategies on talent workforce decisions and tracking performance of HR strategic initiatives and also on building data driven discovery algorithms on improving HR process efficiencies and outcomes. 

AIQRATE’s attempts to gear up HR leaders to the future of work and our curated offerings will enable navigate four broad shifts for HR leaders :

1. Accentuate strategic business acumen

2. Augment AI driven expertise for decision making

3. Amplify “transformation driven impact “ within the HR business function.

4. Accelerate “innovation driven culture” within the HR team

Reach out to us at consult@aiqrate.ai for detailed view and approach on our extensive AI consulting offerings for CHRO/CPO/HR leaders .

The excitement around artificial intelligence is palpable. It seems that not a day goes by without one of the giants in the industry coming out with a breakthrough application of this technology, or a new nuance is added to the overall body of knowledge. Horizontal and industry-specific use cases of AI abound and there is always something exciting around the corner every single day.

However, with the keen interest from global leaders of multinational corporations, the conversation is shifting towards having a strategic agenda for AI in the enterprise. Business heads are less interested in topical experiments and minuscule productivity gains made in the short term. They are more keen to understand the impact of AI in their areas of work from a long-term standpoint. Perhaps the most important question that they want to see answered is – what will my new AI-enabled enterprise look like?

The question is as strategic as it is pertinent. For business leaders, the most important issues are – improving shareholder returns and ensuring a productive workforce – as part of running a sustainable, future-ready business. Artificial intelligence may be the breakout technology of our time, but business leaders are more occupied with trying to understand just how this technology can usher in a new era of their business, how it is expected to upend existing business value chains, unlock new revenue streams, and deliver improved efficiencies in cost outlays. In this article, let us try to answer these questions.

AI is Disrupting Existing Value Chains

Ever since Michael Porter first expounded on the concept in his best-selling book, Competitive Advantage: Creating and Sustaining Superior Performance, the concept of the value chain has gained great currency in the minds of business leaders globally. The idea behind the value chain was to map out the interlinkages between the primary activities that work together to conceptualize and bring a product / service to market (R&D, manufacturing, supply chain, marketing, etc.), as well as the role played by support activities performed by other internal functions (finance, HR, IT etc.). Strategy leaders globally leverage the concept of value chains to improve business planning, identify new possibilities for improving business efficiency and exploit potential areas for new growth.

Now with AI entering the fray, we might see new vistas in the existing value chains of multinational corporations. For instance:

• Manufacturing is becoming heavily augmented by artificial intelligence and robotics. We are seeing these technologies getting a stronger foothold across processes requiring increasing sophistication. Business leaders need to now seriously consider workforce planning for a labor force that consists both human and artificial workers at their manufacturing units. Due attention should also be paid in ensuring that both coexist in a symbiotic and complementary manner.
• • Logistics and Delivery are two other areas where we are seeing a steady growth in the use of artificial intelligence. Demand planning and fulfilment through AI has already reached a high level of sophistication at most retailers. Now Amazon – which handles some of the largest and most complex logistics networks in the world – is in advanced stages of bringing in unmanned aerial vehicles (drones) for deliveries through their Amazon Prime Air program. Business leaders expect outcomes to range from increased customer satisfaction (through faster deliveries) and reduction in costs for the delivery process.
  • Marketing and Sales are constantly on the forefront for some of the most exciting inventions in AI. One of the most recent and evolved applications of AI is Reactful. A tool developed for eCommerce properties, Reactful helps drive better customer conversions by analyzing the clickstream and digital footprints of people who are on web properties and persuades them into making a purchase. Business leaders need to explore new ideas such as this that can help drive meaningful engagement and top line growth through these new AI-powered tools.

  AI is Enabling New Revenue Streams

  The second way business leaders are thinking strategically around AI is for its potential to unlock new sources of revenue. Earlier, functions such as internal IT were seen as a cost center. In today’s world, due to the cost and competitive pressure, areas of the business which were traditionally considered to be cost centers are require to reinvent themselves into revenue and profit centers. The expectation from AI is no different. There is a need to justify the investments made in this technology – and find a way for it to unlock new streams of revenue in traditional organizations. Here are two key ways in which business leaders can monetize AI:

  • Indirect Monetization is one of the forms of leveraging AI to unlock new revenue streams. It involves embedding AI into traditional business processes with a focus on driving increased revenue. We hear of multiple companies from Amazon to Google that use AI-powered recommendation engines to drive incremental revenue through intelligent recommendations and smarter bundling. The action item for business leaders is to engage stakeholders across the enterprise to identify areas where AI can be deeply ingrained within tech properties to drive incremental revenue.
  • Direct Monetization involves directly adding AI as a feature to existing offerings. Examples abound in this area – from Salesforce bringing in Einstein into their platform as an AI-centric service to cloud infrastructure providers such as Amazon and Microsoft adding AI capabilities into their cloud offerings. Business leaders should brainstorm about how AI augments their core value proposition and how it can be added into their existing product stack.

  AI is Bringing Improved Efficiencies

  The third critical intervention for a new AI-enabled enterprise is bringing to the fore a more cost-effective business. Numerous topical and early-stage experiments with AI have brought interesting success for reducing the total cost of doing business. Now is the time to create a strategic roadmap for these efficiency-led interventions and quantitatively measure their impact to business. Some food for thought for business leaders include:

  • Supply Chain Optimization is an area that is ripe for AI-led disruption. With increasing varieties of products and categories and new virtual retailers arriving on the scene, there is a need for companies to reduce their outlay on the network that procures and delivers goods to consumers. One example of AI augmenting the supply chain function comes from Evertracker – a Hamburg-based startup. By leveraging IOT sensors and AI, they help their customers identify weaknesses such as delays and possible shortages early, basing their analysis on internal and external data. Business leaders should scout for solutions such as these that rely on data to identify possible tweaks in the supply chain network that can unlock savings for their enterprises.
  • Human Resources is another area where AI-centric solutions can be extremely valuable to drive down the turnaround time for talent acquisition. One such solution is developed by Recualizer – which reduces the need for HR staff to scan through each job application individually. With this tool, talent acquisition teams need to first determine the framework conditions for a job on offer, while leaving the creation of assessment tasks to the artificial intelligence system. The system then communicates the evaluation results and recommends the most suitable candidates for further interview rounds. Business leaders should identify such game-changing solutions that can make their recruitment much more streamlined – especially if they receive a high number of applications.
  • The Customer Experience arena also throws up very exciting AI use cases. We have now gone well beyond just bots answering frequently asked questions. Today, AI-enabled systems can also provide personalized guidance to customers that can help organizations level-up on their customer experience, while maintaining a lower cost of delivering that experience. Booking.com is a case in point. Their chatbot helps customers identify interesting activities and events that they can avail of at their travel destinations. Business leaders should explore such applications that provide the double advantage of improving customer experience, while maintaining strong bottom-line performance.

  The possibilities for the new AI-enabled enterprises are as exciting as they are varied. The ideas shared in this article are by no means exhaustive, but hopefully seed in interesting ideas for powering improved business performance. Strategy leaders and business heads need to consider how their AI-led businesses can help disrupt their existing value chains for the better, and unlock new ideas for improving bottom-line and top-line performance. This will usher in a new era of the enterprise, enabled by AI.

Organizations across the globe today are grappling with a data deluge and with the increasing reliance on mining data to carve out actionable insights and drive strategic imperatives, the relevance of building the right ecosystem of Analytics professionals is becoming commonplace. Qualified analytics professionals are scarce, though in great demand and generally command higher salaries than the industry normal because of their specialized skills.

For most, analytics is still in the realms of software tools and creating highly visual dashboards/reports/charts etc. But there’s definitely more to it than what meets the eye. Analytics has lot more to than just jazzing up data; it can enable fact-based business decisions based on that data. It primarily means working closely with the business stakeholders to uncover gaps in the business and using the knowledge to work with data appropriately, to come up with useful insights and recommendations the organization can focus on, to increase top-line or rationalize costs at a high level.

And many a times, the general perception about great talent directly correlates to the pedigree of an individual. Most organizations, especially in analytics space, are extra careful about their hiring channels when it comes to onboarding Analytics talent. And more often than not, we are generally biased to absorbing talent which has a strong pedigree credentials (academic excellence, b-school or t-school grade or tier et al) and fall prey to such generalized notions about building great teams. Unfortunately, Analytics is a different ball game altogether and successful career in Analytics has more to do with the underlying fundamental behavior of an individual. It’s an interplay of multidisciplinary skills ranging from mathematics, to statistics, computer science, communication and not to mention the business knowhow. Pedigree may be just a guiding beacon to highlight potential but definitely not a key ingredient to governing success. Let me shed some light on what it takes to build a successful career in analytics:

Intellectual Quotient

Successful people in the analytics industry today have that inquisitiveness and high curiosity attitude ingrained in their natural DNA. For any given situation they are presented with, they can think through and formulate the right set of questions, the “why’s” “what’s” & “how’s” which is key to succeeding in a professional setup. Even before jumping to the data analysis piece, it’s crucial to understand the business problem at hand, crafting out the specifics of the probable solution approaches and most importantly questioning the underlying assumptions being undertaken.

Especially ‘big data’ is more about the questions being put forward than the data itself.  No data can speak for itself unless appropriately questioned.  Success on dealing with ‘big data’ projects requires a thorough understanding of the problem, narrowing down the right questions, getting those answered by SME’s or business experts on right forums, making sure you harness the right amount of data to answer the questions at hand and then eventually communicating the solution to the target audience (which may be clients or the internal stakeholders).

Driven by Numbers

Being accustomed to using mathematical concepts and mathematical tools is commonplace in analytics space.  Mathematics & statistics forms the basic foundation here and if for any reason this word strikes fear in your heart, think again! As you progress your career in Analytics and if you aspire to be truly a Data scientist, few additional skills shall be instrumental to your success: Machine learning, statistical modeling, experiment design, Bayesian inference, Supervised learning: decision trees, random forests, logistic regression Or Unsupervised learning: clustering, dimensionality reduction, Optimization: gradient descent and variants etc. The key aspect to note here is that most of these skills are picked up during the job or as special trainings and not directly linked to an individual’s pedigree. The number-crunching attitude forms the basis here and this is something inherent to an individual irrespective of which institute or academic background they hail from.

Ability to see the Holistic picture

Data here is just a means to an end and behind the scenes there’s a larger business problem at hand being dealt with. Unless there’s absolute clarity on what the client is actually intending to solve, you might end up looking at the wrong place or assimilate wrong pieces of information which may not be of any use. At times, the client isn’t quite sure about the problem they intend to seek answers to which may derail the whole exercise. Getting clarity on what’s the root cause driving actions is crucial.

There may be too many variables under consideration at the same time, but being able to see through clearly and importantly, being able to identify the next steps based on the larger intent is imperative. For instance, if the individual is assigned a problem pertaining to pricing analytics in an FMCG industry, it is very important for them to understand the dynamics between marketing, pricing, sales, promotions etc. work in this industry before. If it’s about evaluating the effectiveness of a marketing campaign for an FMCG product, domain knowledge shall help in narrowing down the key 10 or 100 variables that need thorough consideration from amongst the thousands available at disposition.

Again this ties back to our initial premise of inherent inquisitiveness of an individual to get the right set of questions framed and answered before any detailed analysis begins. Asking the “Why” questions at every juncture may help to uncover the latent objectives which client may not be articulate well in certain cases.

Orientation to Detail

Cognitive “attitude” and willingness to search for deeper knowledge about everything is a common strain running across all successful analytics professional. Though a bird’s eye view is good to have to better understand the larger business problem being tackled but at the same time balancing it against the specifics which need further drill-down is crucial. While dealing with voluminous stacks of structured or unstructured data, it’s easy to lose sight of specifics which be of immense value in crafting a solution to the original problem. Having that “hawk’s eye” to suddenly fish out significant patterns which may be of interest to business is a must have. Visualizing data through various plotting methods (box plots, histograms, correlation matrix et al) can help uncover those meaningful nuggets which the business is interested in.

Ability to Interpret within the Realms of Business Context

End of the day, it’s important to realize that numbers won’t speak for themselves unless the right set of tools/techniques/methodologies are employed to present the data in a consumable form. Numerous tools in the industry today have plethora of features to simplify data interpretation but the understanding of which visualization technique is most suited to give you the right picture, given the data in question and business problem at hand is the prowess of a well-acquainted analytics professional; one who knows his toolbox in & out. In some cases histograms may deem fit to understand the distribution of data and at the same time the box plot may get you a better idea of how the majority of data points are spread across the spectrum, or if there are any outliers. Domain expertise & business knowhow can help leapfrog your analysis to a different level altogether, help interpret the results in the business context, assess usefulness of results, bringing out insights which may not be that obvious to common folks.

Communication and Visualization

You may be a champion in your rarefied field, but you may not succeed as an analytics professional unless you can’t communicate the value of your analysis in simplistic terms, a language which the client or business user understands. Communicating the value to business people and asking the right set of questions on what’s important is table stake. Ability to convince that what you’ve done is viable and will deliver business value is something one should be excelling at.

Umpteen times there are disparate pieces of information which a good analytics professional should be able to connect and able to convey a compelling story which makes sense to the target audience. As an analogy, a leading insurer was observing overall dipping sales and post analysis it came to notice that customer service in certain pockets or geographies has dwindled because of inappropriate handling of customers over certain touchpoints. The analytics team was able mine the sales data for pain points, narrow down upon the areas with stagnant or negative sales growth and also uncover pattern between unsatisfactory customer comments over social channels (FB page, twitter handle etc). Survey results again hinted that certain geographies had observed lack of customer empathy as a major factor impeding lead conversion and high attrition. Sales data, social data and survey results in totality were able to narrow down upon those specific areas of concerns mapped to respective geographies, which now the business could pursue to chart out a customer experience roadmap for targeted geographies & remedial measures to mitigate potential bottlenecks identified.

To sum it all, a pedigree can convey so much so about an individual’s ability to succeed in building a thriving analytics career. It’s more about those innate capabilities, domain/analytics experience one garners on the job and regular trainings which forms the secret sauce to a differentiating career trajectory in analytics.

Numerous studies in the past have shown that an engaged workforce significantly out-produces an unmotivated one. And even a greater number of those studies reinforce the common notion that employee creativity is the key to innovation and eventual customer or client satisfaction. To a great degree, for most industries and organizations, talent is one of the most treasured asset and to an extent a key differentiator in the marketplace. Every company needs creative team, decision makers and visionaries, but on a similar note, it’s equally important to have a motivated workforce who can give their all for the cause day-by-day. As clichéd it may sound, Analytics truly is one of the industries where the average churn or attrition rate has typically been on the higher side (as compared to industry average in general) due to great demand for Analytics experts in the industry across the globe and dearth of skills required. I presume all my readers would concur on the perennial challenge of analytics talent crunch most businesses are grappling with today. And even if you find the so-called right analytics resource that align with your skills requirements, you’re bound to chart out engaging career prospects and develop that talent over what could amount to a decade before that person is achieving optimum results for your company.

Analytics is all about working with an extremely talented & creative set of people, who need constant care and attention from their leaders & mentors.  And undoubtedly, most businesses yearn to maximize output and increase topline, they need to have their team working as effectively as possible. But how should they go about this? A generally accepted phenomenon that happy workers tend to be productive ones – meaning there are clear benefits to keeping people engaged and motivated.

When it comes to job satisfaction, financial rewards may be lower on the list than most people think and as I reflected in my previous posts. Being happy with the job seems to depend more on the intangibles: feeling part of a team and being valued and appreciated consistently outrank money when employees are polled about job satisfaction. It’s all about keeping employees highly engaged & give them the due respect they deserve. Engaged analytics talent learns, grows, displays high leadership quotient, deliver heightened ROI and significantly decrease your organization’s turnover rate. However, keeping a team that approaches work with vigor and passion intact is easier discussed than executed.

Every other company which employs analytics services or is outsourcing it wants to hire and keep the best of the breed talent – how do you stay a step ahead? Clarity on company’s mission that they are driven to lead themselves is all commonplace and is a sanity in Analytics as with every other industry. And if an employee isn’t engaged creativity is not present! So what truly keeps this evolved species engaged enough?

• Where in the Organization your Analytics Talent sits

First & foremost, as leaders, we must acknowledge that Analytics talent is different from rest of the organizations and placing them rightly within the organization is crucial to ensure they are truly able to create the real impact. Bright minds cannot be chained or siloed or put in a bureaucratic hierarchy, lest they tend to lose their sheen and may end up attriting or be sitting ducks stuck up in the usual business stream where their skills are definitely not put to the best of use. Analytics talent should be led by analytic leaders who know this industry in & out; because they are the ones who understand how this talent needs to be groomed & nurtured, and shielded from the political bureaucracies and the analytics leaders should effectively communicate those differences throughout the organization constantly. Separate HR policies, working culture and operating rhythm is required to give these prodigies a conducive environment to deliver their best.  They should acknowledge different processes for them, they should have different technical ladders, different job expectations. It must be thoroughly acknowledged that they have different motivations and is the organization in the capacity to carve out that special niche for thriving Analytics talent. Whether you keep them together in their own close knit group so they can keep their skills sharp with constant interaction with each other, or should they be spread across company’s business units because that is where they must have an impact?

It’s important for your analytics talent to garner complementary skill sets. Obviously, you intend to build a team with eclectic mix of skills instead of having all people who are good at data massaging or modelling or all people who do optimizations or visualizers. Analytics is a broad space and there are umpteen specialties, and piecing together these different puzzle elements is the key to generating impactful business insights. As an analytics leader because you understand the space, you understand these specialties. So to be a good leader of such a talented team, you really have to focus on the individuals on your team and help them succeed.

• A Robust Career Path

Talent always needs a clear future vision on their career trajectory & growth within the organizations with distinctive career paths through career ladders and lattices. A clarity/transparency on roles, career tracks and skill expectations has to be in place to affirm that your analytics professionals have a well thought through career roadmap charted out. Structured capability building, systematic learning and development frameworks could be crucial to ensuring that career architecture plan is laid out appropriately.

• A Rock-solid Training Regime

An effective training and development plan is one of the best ways to convey to your valued employees the commitment and faith you have by investing in them. And, when you consider this investment in the long run with a way higher ROI, the expense of a solid training program suddenly seems measly. With the pace the Analytics industry is maturing, it’s crucial that your talent is in tune with the current needs in the industry, has hands-on experience with the topical tools & technologies and is abreast with the latest and greatest techniques being used today to deliver business impact. Just to substantiate with an example here, R being open-source has tons of pre-built libraries & many more keep getting added to the repository, avoiding duplicity of effort and ensures optimal procedures/techniques are being employed which have proven to be effective & accredited by experts in the wider public analytics community. Smart talent is always hungry for more and it’s imperative for the analytics leaders to keep feeding their talent with all the trending skills/tools and keep their talent’s arsenal up-to-date.

• Empower them

Give your talent the wings of freedom, to innovate, to be creative, basically get out of their way and empower them, give them the requisite tools to deliver. Don’t keep them shielded from the real-world all the time & get them the exposure to be deeply embedded in the C-suite. The senior executives want to make decisions based on data and they trust this talent tremendously. Get them in front of the clients or the C-suite and give them the platform to talk to them directly as to what they are doing. That’s what keeping them there, as they feel that they are in the driver’s seat helping businesses navigate in a highly competitive, relentless environment. Highlight in appropriate forums about their achievements & the impact they have made.

• Business Exposure

Analytics professionals need to be spending a sizable time(between 6-10 years) to industry-specific challenges before they can have the right context  to understand the problem well and know exactly know what’s needed & what not to go about finding a solution. This is among the scariest challenges which majority Analytics leaders across the globe are trying to figure out and still poses a big question mark for most on how to get their analytics talent a business bent of mind. Continuous exposure to the business, ongoing domain-specific trainings are hence an important ingredient to their success & keeping them relevant

• Challenging Work Environment

Throw all sorts of challenges and varied projects at your talent. Be it as far away from their comfort zones & let them drown in it. This is what they truly enjoy doing and they love finding their way out from the middle of nowhere. Organize week long hackathons, competitions & give them stringent timelines to deliver. Give them the opportunity to make the impossible possible. The sense of achievement & gratification after working their way out from challenges unimagined in their wildest dreams is what keeps them going. Let them mingle up with the wider Analytics talent beyond the walls of the organization; let them participate in global competitions organized by Kaggle, 5^th Elephant, VCCircle, Analytics Vidya etc. Motivate them to join interesting discussions pertaining to Analytics online (twitter, blogs, linkedin communities) and make a contribution, be an expert advisor in the area they specialize in.

• Be the “Employer of Choice”

Value your talent, get them the requisite support and developmental environment from mentors & other experienced professional in your Analytics team, pay them well, recognize their contributions, and give them the opportunity to rise up the career ladder. Start collaborating with academic institutions from where you hire, increase interventions, do guest lectures, seminars, workshops, mentoring sessions, case competitions, “Shadow a Leader” program etc to be constantly engaged with your pipeline talent.