Note: This is a guest blog from Everest Group on how automation and cognitive technologies will impact the future of work.

Enterprises are facing unprecedented disruption from multiple sources – political, economic and regulatory forces, as well as rapidly evolving technology and customer expectations. As enterprises try to keep pace with this dynamic environment, a key tool in their armory is transformation of operations/service delivery by leveraging technology. To this end, technology is impacting more aspects of service delivery and helping businesses improve efficiency and enhance customer experiences. New disruptive technologies, such as digital colleagues and Artificial Intelligence (AI), also alter paradigms for talent and workforce management substantially.

As enterprises try to keep pace with this dynamic environment, a key tool in their armory is transformation of operations/service delivery by leveraging technology.

This blog is focused on the disruptive impacts of technology on workforce strategy that enterprises will need to be cautious of as they move forward with a broad-ranging transformation of their service delivery organizations. We will describe the various aspects of the workforce that technology is likely to disrupt and ramifications for enterprises.

Disruptions to Workforce Strategy

Talent management models

While enterprises are not targeting automation efforts at reducing headcount, there will be a net reduction in the number of jobs during the next few years. One set of estimates indicates that nearly 75 million jobs may be displaced by a shift in the division of labor between humans and machines, while 133 million new roles may emerge that are more adapted to the new division of labor between humans, machines and algorithms.

While the shorter-term impact (the next 3-5 years) is largely going to be on more “transactional” jobs (e.g., basic accounting, banking account set-up, basic eligibility checks for mortgages, etc.), we anticipate that AI-focused technologies will also lead to many complex/judgment-intensive roles (e.g., reporting, quantitative modeling, credit card dispute resolution, etc.) becoming automated. As a result, automation will cause serious disruption to talent management models:

    • 98% of the most aggressive/successful adopters of automation we surveyed in 2018 said they did not let go of employees affected by automation, and 81% of such enterprises said they upskilled/reskilled these employees. However, continued automation of transactional jobs and new strides in automation of complex/judgment-intensive jobs will pose questions for enterprises’ ability to upskill/reskill large sections of workforce quickly enough. Firms will need to make massive investments into training infrastructure plus consider gamifying and personalizing training programs to make them relevant, appealing and self-paced.
    • The current impact of automation is on jobs that are viewed as monotonous and repetitive (and ones that employees don’t look forward to supporting, in general). When jobs that are perceived as desirable/enriching are automated, proactive change management is required.
    • Firms will need to carefully plan their future talent management, in sync with the expected impact of technology, in order to stay competitive. Desirable skills of the future combine hard skills (e.g., knowledge of finance, ability to code in Java, etc.) with soft skills (e.g., creativity, problem solving, pattern recognition and ability to work in a global environment). Proactive and intentional demand management also helps avoid laying off masses of employees (which could lead to negative brand perception and loss of subject matter experts).

Delivery locations’ portfolio strategies

Automation, next-gen talent models and next-gen technology (such as Agile, DevOps and DevSecOps) lead to disruptions in enterprises’ plans for services delivery locations – as they upend strategies around hubs, spokes and Centers of Excellence (CoEs):

    • Most next-gen technologies promote or are based on close collaboration between team members, clients, business leads and target markets, and hence clash with traditional models based on a geographically-distributed workforce.
    • As an example, pure Agile methodologies require cross-functional teams to be based in the same physical location – these conflict with the functional hub/spoke/CoE model (e.g., a testing CoE/spoke separate from a development hub) and requires new thinking (e.g., DevOps hub or CoEs organized by product/business).
    • On the other end of the spectrum, the talent-as-a-service model means enterprises will work with a fragmented delivery portfolio, with a sizeable proportion of their workforce based outside delivery centers — thereby increasing overhead around facility costs and management bandwidth.
    • Automation and greater leverage of platforms lead to reduced scale demand (in terms of FTEs). This could lead to some centers rendered sub-scale post automation, thereby driving consolidation in portfolios and reorganization of hub/spoke/CoE models — a trend already seen in many large enterprise clients.

Workforce orchestration

The rapid advent of technology in workflows has liberated employees from the shackles of a physical “office” and is enabling people to work from anywhere and at any time. There are also examples of startups that offer “talent as a service (TaaS)” — essentially an “Uber-ization” of skills. There are various impacts of these trends:

    • A globally distributed and fragmented workforce will require adequate investment into ensuring similar culture, standards of quality and information security/data protection across the workforce.
    • Effective leverage of the workforce outside the organization (e.g., gig workers, freelancers, crowdsourcing workers and TaaS) will mean enterprises preempt challenges around adherence to corporate values and alignment to the same vision/goals as the proprietary workforce.

Hiring and retention

Firms are transforming large phases of their talent hiring engines with technology such as analytics, RPA and AI/ML/NLP across sourcing/screening, applicant tracking and assessment. There is also evidence of some firms leveraging analytics-led tools to assess and rate retention of employees. These tools take into consideration multiple leading indicators such as employees’ year-over-year performance, level of interaction with management, overall engagement level and resolution status of key issues to rate employees on their propensity to depart.

As organizations rush to digitalize talent management, they will need to be cautious:

    • Technology (e.g., scoring models, skill maps, etc.) that enables recruitment will need to be kept up-to-date with the latest (and in some cases forward-looking) demands in terms of skills. Not doing so will lead to a talent/skill debt even before selected candidates join the organization.
    • As organizations or their partners train and re-train these models, they will also need to be cautious not to inadvertently pass on any unconscious biases (related to gender, culture, ethnicity, age, etc.) to these models/bots. These models need to be aligned with any diversity and inclusion-related targets/aspirations that companies have set for themselves. (For a recent real-world and worst case example, read up on Microsoft’s experimental AI-driven Twitter bot Tay that had to be shut down after 16 hours of chats.)
    • As technology penetrates more (and increasingly judgment-intensive) streams of the talent management spectrum, one imperative is to ensure adequate emotional quotient (EQ) in technology tools – in essence, capturing human sensitivities beyond tangible data and having the ability to recognize exceptions. This could be especially relevant as hiring/retention/performance measurement are subjective matters; moreover, roles for which companies will need to hire/evaluate will be increasingly complex and multi-dimensional.

Key Calls to Action for Enterprises

  1. Think about “end-states” for service delivery organizations with robust and forward-looking demand prediction that spans the entire organization – all business units, functions, geographies (onshore/nearshore/offshore), front-office/back-office and sourcing models (outsourcing/in-house). A word of caution: These end-states will need to be reviewed at regular intervals to make sure strategies keep pace with market dynamics.
  2. Organize service strategy around talent and workforce themes and invest in creating a future-ready workforce:
    1. Invest in reskilling and upskilling the workforce. Focus on not only technical skills but also soft skills.
    2. Be open to innovative models of tapping talent – leveraging startups, gig workers and crowdsourcing workers, among others.
    3. Shape talent pool by collaborating with academia, industry, training and recruitment firms.
  3. Finally, the cliché “change is the only constant” is true more than ever before – hence enterprises need to invest enough resources into change management, be it with people, systems, facilities, or cultures:
    1. The foremost imperative of this factor is ensuring transparent communication with employees about the technology roadmap and likely impact on jobs and skills.
    2. Adapting to a collaborative and open culture is next – given enterprises will need to leverage dynamic talent models (contingent workforce, crowdsourcing and collaboration with startups) to stay ahead in the evolving talent wars.
    3. Re-orienting from an efficiency and costs mindset to a topline/revenue and outcome-based one.

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