Are we preparing our Organisations to absorb smarter machines, as integral part of talent pool?

In the Second Machine Age, it’s not so clear whether humans will be complements or machines will largely substitute for humans; we see examples of both…… Erik Brynjolfsson, Schussel Family Professor of Management Science at the Sloan School.

Machines have always been integral instrument available to workforce to deliver work more efficiently and consistently. They performed routine and well-structured tasks, be it manual or cognitive, helping workers boost their outputs.  But with improvement in technologies such as AI, Cognitive computing, Natural Language processing, big data analytics, machines have become lot smarter.  Today some of the activities which were unquestionably human (being complex, non-routine), can also be performed with machines, not only efficiently but also with higher quality outputs. 

There are different projections about the extent to which Smarter Machines (SM) will be introduced in our work delivery model, working alongside or instead of human colleagues.  Carl Frey and Michael Osborne have conducted analysis of 702 occupations to rank their susceptibility to technological advancements linked risk.  According to their estimate, around 47% of total US employment is in high risk category.  

If this is to happen, are our organisations ready to embrace this challenge, which will manifest itself in terms of leadership expectations, performance measure, workforce strength, skill requirements or work related policies and processes?  Have we started thinking about the impact of these smarter machines on our organisation, and what should be our approach to managing it?  Is it time we start asking these questions?

New machines display significantly higher capabilities, when compared to traditional machines on three key dimensions:

SEARCH CAPABILITIES:  While traditional machines were able to search from within structured data sets in prescribed formats, SM are able to search from structured and unstructured data, coming from multiple sources, in different formats, almost in dynamic manner.

SOLVING CAPABILITIES: While traditional machines helped solve structured routine well defined and programmable set of problems, smarter machines are able to increasingly solve non-routine, context sensitive problems, with increasing accuracy with every usage.  SM can identify patterns and trends from large data sets and put forth advice with associated probabilities.      

SERVE CAPABILITIES: Machines can now serve responses in any format, and converse with humans as near humans with natural language as medium of conversation. 

Every other day, we see new examples of SM replacing human work, completely or partially, as enterprises experiment with them.  Some published examples are shared as case in point:

1.       USAA, a financial service company, uses smarter machine to handle its Armed Forces customers’ queries with regard to transitioning to civilian-life.  Machines uses data base of 2000+ questions and 3000+ military training documents as search base to solve customer questions and respond in natural language.  (IBV paper)      

2.       Associated Press is using smarter machine from company Automated Insights to autonomously create Quarterly Corporate Earnings stories, from data coming from Investment Research firm(ZackS). AI algorithm can process large number of financial announcements, press releases and other information and then offer personalised financial advice at large scale and lower cost. 

3.       Computer Assisted Translation technology is being employed to speed up the translation work, as it memorises earlier translations and use for preparing half cooked translated material for human translators to perfect.   

4.       Oncologists at Memorial Sloan-kettering Cancer Centre are using IBM Watson computer to provide chronic care and cancer treatment diagnostics using pattern recognition capabilities, with 600000 medical evidence reports and 1.5 million patient records and trials as referable data set. 

5.       Deep Knowledge Ventures (DKV), a Hong Kong venture capital fund appointed a computer algorithm named Vital  to its board of directors, claiming to be the first company of its kind to give a machine an "equal vote" when it comes to investment decisions. (BBC)

6.       Law firms rely on smart machines to scan thousands of legal briefs and precedents to assist in pre-trail research.

7.       Dr Mark Oleynik is working on creating automated kitchen housed by robotic chef that can create dishes like the professional chef it learns cooking from.  (Economist)

Yes, these examples may take time to go mainstream, but not as much time as earlier technologies have taken. These capabilities are growing exponentially and the costs of such machines are going downwards, fast, really fast!

While IT and Operational departments are busy identifying suitable business use-cases and associated technological solutions for incorporating these SM in the ways of delivering work, HR department needs to be proactively thinking of its implications for the organisation, leadership and workforce management.  Let us look at each of these areas:

Leadership: Leaders working alongside SM, that can throw unbiased data backed probabilistic recommendations, demands greater maturity and acceptance from the leaders. It can be a fight between domain expert taking out of experience and SM sharing advice that is based on pattern recognition from immense data from multiple sources.  Leaders need to be open to reconciling between gut feel and data based alternatives to problem solving. They need to be open to questioning their beliefs as domain experts and to re-examine counter intuitive suggestions coming out of machines. How do we empower leaders to have the strength of conviction to go their way, despite what SM advices? Are leaders going to be made accountable for going against SM advice, in case of things going wrong?  On the other hand, how will you keep domain experts motivated, if in most of the cases they have to go by SM advice.  Finally, leaders need to be smarter themselves in defining and asking right kind of questions to their smarter assistants (ie SM). 

Workforce Management: SM introduction will replace or redefine some jobs and consequent workforce requirements.  Those jobs that are high on creative intelligence, social intelligence or perception and manipulation seems to be least impacted, all others jobs are vulnerable. Tpo begin with, using the available thought-ware and general guidelines, we need to have view on the extent of staff that is working on vulnerable jobs.  It would have implications on the hiring vs contracting decisions, especially with medium term horizon. Also is there a way to reconfigure set of vulnerable jobs to ensure that certain category of revised jobs have greater creative intelligence, social intelligence and perception component? This readjustment effort takes time hence the need to take cognizance of the impending challenges and start thinking now.       

Skill Development:  The scope for skill development will have two clear objectives- enhancing skills among staff to work with SM and enhancing skills among employees to help tide over the transition as their existing jobs get make reconfigured/replaced and they have to look for alternate ways to stay employed and relevant. In the first category fall learnings with regard to natural language processing, database system and administration, and interface design, that would be spearheaded by technical department and supported by HR.  The second category include trainings with regard to critical thinking, evidence based decision-making, social intelligence and change management, where HR has to take lead.    

Use-case participation: SM introduction will most likely take the use-case based pilot approach to implementation.  While pilot may be focussed on validating the value generating potential of the new delivery model (using smarter machine), it is important for HR to also use the pilot to study the change impact analysis of the proposed solution.  For example, in the assisted decision making by manager, how will be the accountability of the wrong decision is tracked and established?  This may require some changes in the policies, as well.

Perception management: Also managing the expectations of the workforce on the efficacy of the new solution needs to be addressed as the SM effective benefits may take some usage to get to its full potential.  Here HR has to play its traditional role of managing naysayers that are quick to dismiss any new disruptive solutions and maintain the positive culture around the need to experiment and be open to new ways of working.    

SM idea shall be hitting our enterprise shore, in some form or other, and we need to be partnering with its carriers/sponsors to ensure organisation is ready to embrace it without much pain,

Do you agree?  Are there some other aspects of SM introduction that organisations should take note off? 

Share your views and comments, as always   

Change Management for Digital Transformation vs ERP implementation: What’s the difference???

Recently, one of my colleagues, a Change Management (CM) professional with experience in driving several ERP transformation programs, has taken up an assignment to help client undertake digital transformation, primarily aimed at internal operations.  He is deliberating on a question, which I think several of change management professional will encounter soon:

 

How is the CM approach going to be different for digital transformation program, compared to that in an ERP implementation project?

Here are a few initial reflections to set the stage and invite views, comments and experience sharing from this group: 

The key characteristics of Digital transformation program vary from the ERP implementation program in certain ways, including the following: (not exhaustive)

1.       Digital transformation program is often conceived in the form of vision that is quite wide, aspirational and all encompassing (customer interface, internal operational processes and operating model) supported by broad road map. The desired states are more often described in terms of value adding scenarios and differentiating services that are made possible by providing additional capabilities (collaborative, analytical, mobile etc) and their creative adoption by the employees. Business cases associated with ERP programs are a lot more definitive and with clear steady state targets.

2.       Digital transformation programs often add to and complement the existing technical capabilities and functionalities available to users to perform their regular work. For example, advent of Enterprise Social Network does not mean discontinuation of email system. Whereas ERP program often aims to automate manual / excel sheet work and to that extent replaces the old ways of working.  To that extent, an employee can live without participating in enterprise social network, but cannot bypass ERP based approvals to conduct daily business.

3.       The nature of risk linked to digital transformation program is largely linked to confidential information sharing which is perceived more severe than in typical ERP implementation program. 

4.       Leaders have no choice but to actively participate and lead by example, in case of digital transformation program. Hence their behavioural change/alignment is a pre-requisite. In case of ERP implementation program, public endorsement of its importance while delegating its actual usage to assistants is possible, but not in case of digital transformation program. After-all leader cannot delegate writing blogs, podcasts, video-casts to others without being exposed!

5.   ERP delivers value from ensuring that process level integration points, which flow across functional boundaries are well managed and aligned with the help of an IT-system. ERP users need to be sensitive of the process interdependencies to do justice to their role.  On the other hand, Digital transformation programs are essentially focused on driving value thorough employees voluntarily and creatively collaborating across boundaries, in an open transparent and relatively tolerant environment, supported by additional data analytical skills.  Understandably, cultural permission plays much greater influence in driving outcome of the digital transformation program.

6.   Employee generational split may also become a relevant segmentation strategy during digital transformation exercise, given different level of natural adoption to digital technologies among different generations.

7.  While significant effort is required in training the users in using ERP systems, the training effort associated with use of digital technologies may not be much and may be in the form of familiarisation modules; as the social technologies are quite intuitive, and users are significantly mature in the use of these tools in their personal life. The barrier to adoption of digital initiative, in that respect is seldom lack of skills on the part of employees.  There would off-course be need for specialised skill pool, say that of data scientists, digital strategists, digital technologists, which in any case would be part of overall capability building program.

What does these differences mean for CM approach and intervention design:

1.      Communicating the case for digital transformation has to be lot more leadership- driven, continuous, and conversational, leveraging all possible channels. Stories, describing creative usage of new capabilities to drive value-adds, emanating from different sources, play a pivotal role in driving adoption.

2.      Leadership Buy-in: Leaders need to convince believe within themselves that its worth it, and what is expected of them is do-able and non-conflicting to their self- image. Leaders have to hear first-hand stories and alternative experiences to appreciate the potential of digital transformation and their own role in supporting this change. Peer level conversation and experience sharing at leadership level is a must and has to be facilitated as part of CM intervention.  Leadership enablement is easier by associating some digital enthusiast to work along for some protracted period.

3.      Policies and practices: Digital enterprises thrive on a certain level of responsible information sharing, open communication, and collaborative learning that need support from enabling policies and practices.  As a change facilitator, it is important to identify and bring forth the policy or practices conflict with digital transformation objectives and help address them.  Often it is more to do with interpretation of the policies than policy itself that is in conflict.

4.      Training and capability building:  Digital transformation linked capability development effort will involve more of familiarising users with the features of digital technologies and varied ways it has been used to create value.  To that extent the learning will be more byte sized, social learning and experience sharing based continual learning, than structured class-room trainings and practice sessions predominantly used during ERP implementation program. Games as learning tool seem to be quite relevant.  Instead of user-manuals or reference sheets, guidelines, best practices and provocative use cases and stories may be more relevant.     

5.      Adoption tracking and support: There is clear method and science behind measuring the adoption levels of ERP system usage, and segments /pockets that reflects low adoption levels can be analysed and system, training or management intervention can be made to address the cause. Concerted efforts made thus, shall help achieved fairly stable usage of the system across the enterprise, which signals reduced need for CM intervention.  In case of digital transformation, the adoption is linked to employee voluntary engagement with the new capabilities and their extracting value out of it.  The usage pattern may vary (tank after peak!) and could be due to variety of reasons.  CM needs to do much more diligence to find real reasons behind these variations and also experiment ways to spur adoption again. Digital transformation in that respect is a journey and CM has to be co-traveller on this route for a much longer distance. 

Success of digital transformation program hinges on employee engagement, supporting culture and leadership participation, and not on management dictates (with  structured capability building interventions), and that is what makes CM work challenging and interesting!

Share your experience and view-points!

 

 

TRAP-PROOF your Technology Enablement Program

IT enablement program, although well intended and management supported, fails to deliver as it falls prey to traps during the journey.  Here are a few “Traps” that spread across six dimensions; System, People, Infrastructure, Data, Execution and Resources (S.P.I.D.E.R) that can play havoc—being cognizant of these upfront help.

SYSTEM:

Trap 1:  New system will automate everything

System in its original or morphed form should cater to all users’ wishes, including complete elimination of paper documents; excel sheets and other manual ways of working. 

Trap 2:  All systems should be able to talk to each other…… seamlessly. 

In the professed new “open, networked, connected world”, there should be no barriers with regard to data flow, access rights and application usage between new and existing legacy systems.

PEOPLE:

Trap 3:  People care for new IT system, as much as IT team does. 
IT enablement once completed will help business gain better control on planning, operations and customer acquisition levers, resulting in real business benefits.  So business should be readily sparing team members for system testing, getting trained and conducting trainings as asked by IT team. 

Trap 4:  People adopt well designed IT systems on their own.

The new system offers multiple benefits and all it expects is people to enter data into system instead of on paper or Excel! On top of that, we have offered training programs, designated super users to help and made management reinforce the benefits of new system.  Human beings are rational beings, at-least in office, all of them, all the time……. fair assumption? 

INFRASTRUCTURE:

Trap 5:  Infrastructure availability is only a cost issue

You can get any infrastructure you need, provided you are willing to pay for.  Issues like existing infrastructure status, network security and risk vulnerabilities, buy vs rent decisions, vendor delivery timelines are too trivial aspects to bother about.   

Trap 6:  New system is modern only if it works on my tablet. 

My personal applications work across channels, so should my office applications.

DATA:

Trap 7:  All type of recordable data should be available in the system. Lets’ have it, just in case!    
It takes hardly any effort to configure another template/KPI in the system.  And business will religiously maintain correct and up-to-date data in the system. 

Trap 8:  Data preparedness can be managed easily

The challenges associated with data preparedness, i.e. collection, validation or migration from existing formats, need no special attention.  

EXECUTION:

Trap 9:  Assigning Accountabilities and Responsibilities will ensure delivery
RACI is more used when things go wrong for finding scapegoats, than for driving effective collaboration.

Trap 10:  Getting stakeholders meetings on calendar is Governance

All stakeholders when invited to the weekly meetings and risks and issues openly sought, should leave no scope for surprises at the last moment.

RESOURCES:

Trap 11:  Two partially competent resources can substitute for one competent one
Is It?

Trap 12:  Resource replacement is BaU (business as usual)

We have robust documentation of changes and requirements.  Moreover everyone is expected to collaborate, jell well with team members and adapt to the operating context, fast.  Isn’t it?

The above list is by all measures illustrative. 

Have you also encountered traps that have potential to derail the program?  Help validate and add to the above list.