Large and midsize enterprises successful at competitive transformation have one characteristic in common: careful team-building around both soft and technical skills. Let’s examine how you should think about your digital transformation team (even though it may not be called that). Since there are many books on building agile teams, squads and dojos, this post will focus on the soft skill mix that a majority of IT executives say is the roadblock to successful competitive digital transformation.
Application creation is facing accelerating waves of change. The World Economic Forum asserts we are entering the fourth industrial revolution, even as the third chugs along. Surviving concurrent revolutions requires our digital transformation approach to be as agile as our development methodology. Your transformation must result in a digitally competitive enterprise. The skills needed can be broken into three categories, each with three sub-categories.
Skills to Survive
Consider the bare minimum set of skills required for DevOps projects to avoid failure. These fall into three general subcategories: organizational, business and technology.
Organizational people line up the dominos for other participants to knock over. They ensure decisions are made and the work gets done as expected. These are skills or titles that DevOps practitioners will be well familiar with, including Scrum Master, Project Manager, Squad leader, and Technical Architect. Without these skills, effort tends to run overtime and wanders away from original goals.
Business people bring the reality check from the real world. They ensure that technical success will have business relevance, and that the business is ready for transformed business models and processes. Look for titles like Product Owner, Business Systems Expert, and Business Line Owner. As more digital natives enter your enterprise, expect a higher level of digital awareness and creativity from those bringing your business skills into the team.
These three groups are the essentials—the survival skills—for digital processes to exist and thus are the minimum set needed for digital transformation. Any enterprise going through this transformation has these skillsets—in some shape or form with engineering and organizational skills—in its transformation teams. However, once your business transformation introduces artificial intelligence as part of the architecture, you will need to think differently about the skills needed for success.
Skills for Machine Learning
The machine learning (ML) statistical revolution is changing the world. To embrace this change, enterprises must engage ML in two main ways: as a black box encapsulated within a vendor’s product; or custom-built for competitive advantage.
Application Performance Management (APM) is a good example
of the black box approach where AIOps or Cognitive Services
are delivered by your vendor, and the skills listed under machine
learning are not required.
When encapsulated, the needed skills are housed within the software vendor rather than in your organization, and the vendor will select the optimal algorithms and training frameworks for each type of data and specific use case. For targeted solutions like DevOps, the encapsulated approach is best.
However, you may be surprised by some of the skills required for your business to build out a data science team and gain competitive advantage from machine learning. Research from Accenture and MIT broke the skills surrounding artificial intelligence into three categories: trainers, explainers and sustainers. (The Jobs That Artificial Intelligence Will Create)
Trainers are what we see commonly in AI today. They match models and frameworks to specific tasks, and identify and label training data. Trainers help models look beyond the literal into areas such as how to mimic human behavior, whether in speech or driving reactions. In London, a team is trying to teach chatbots about irony and sarcasm so they can interact with humans more effectively.
As AI gets more advanced, the layers of neural networks creating answers will exceed simple explanations. Explainers will provide non-technical explanations of how the AI algorithms interpret inputs and how conclusions are reached. This will be essential to attain compliance, or to address legal concerns about bias in the machine. If you create AI to approve mortgages, for instance, how will you establish the AI is not inflicting bias based on gender or creed? The explainer will play a necessary role.
Someone needs to ensure the AI systems are operating as designed ethically, financially and effectively. The sustainers will monitor for and react to unintended outcomes from the “black box.” If the AI is selecting inventory and setting prices, a sustainer will ensure there is no resulting price-gouging on consumer necessities—thus avoiding customer revolt.
The machine learning marketplace is the opposite of the gig economy. In the gig economy, skills are a commodity, like driving a vehicle. You can swap cars and still be a skilled driver. In contrast, the needed skills for ML may change with every new type of data. When your competitive digital transformation seeks customer facial recognition as shoppers walk in the store, you will likely apply Tensorflow and hire for those skills. Next, the business may want to recommend adjacent products to a customer. The optimal algorithm will be a decision tree, and now you’ll need to hire for that skill. Later you may need email text inference, which requires skills in text tokenizing and stemming before the email data can be fed into Tensorflow. You end up using different languages and frameworks for each new use case. Even within a single use case, the optimal algorithm may change over time as particular frameworks improve for specific tasks.
For the technical hire, you should qualify on aptitude rather than skills. Find the right person, then train them. The apprenticeship approach of giving workers time to learn shows you value your people, which enhances loyalty. You either accept apprenticeship as a cost, or you will need to hire an army of individuals. With AI/ML, you will initially hire the trainers that select and code models. As you do, consider who will grow into the explainers and sustainers.
Regardless of whether your transformation includes machine learning, there are additional skills you’ll need to attain competitive business transformation.
Skills to Compete
Now we are getting into a different mind space altogether. Inclusiveness and variety is now a stated goal for leading competitive companies. News headlines have multiple examples where applications failed embarrassingly due to the lack of variety, digital awareness and experience in the transformation team. Even an automatic soap dispenser can have bias if it delivers foam to light-skinned hands but not into the hands of people of color. In this real-world example, the dispenser registered light reflected off caucasian skin, but the Fitzpatrick scale tells us you need a stronger light to trigger the sensor for people of color. A broader team or testing regimen would have identified the problem before release. Similarly, Amazon immediately cancelled a machine learning project once aware of the inherent bias of the trained model. Amazon, hoping to better prioritise future applicants, trained a ML model with resumes from previously successful candidates. Unfortunately, the trained model kept selecting males because most of the successful resumes in the past decade had been predominantly male.
For competitive digital transformation, add three new groups of skills to your requirements:
Firstly, look at your overall culture and diversity. Without considering culture, you may easily leave your reputation in tatters as in the examples above. Seek out variety in gender. Combine millenials with baby boomers and mix digital natives with digital immigrants. Even variation in birthplace and societal culture creates the variety of viewpoints needed to ward off potential bias. Hearing different voices will help identify gaps in testing criteria and in training data sets.
The second set of skills leads to “digital dexterity.” Remember, you want the benefits of digital transformation to be experienced by the largest number of people across your organization. This effort involves evangelizing the changes to the entire organization through training and communication. Ensuring that all those using technology feel completely comfortable and skilled with the technology. Identify an ambassador to the executive team outside of regular reporting structure. Look for someone on the fast path to leadership, maybe recently out of college, and assigned a mentor from the executive level. This ambassador gives a rapid and clear path to communicate important achievements and crucial requirements when needed. Also, look for an internal VC. Sometimes the executive sponsor of the transformation is not the same person as the budgetary sponsor. Ensure someone has the skills to build the VC-like pitch for continued funding.
Today’s app-driven world makes User Experience (UX) and Customer Experience (CX) critical. These are not equivalent, as UX is an app category focusing on human interaction with technology, while CX goes beyond the application to the full interaction a human will have with your organization. Are they walking in a door, or onto a factory floor, or calling via phone to reach your digitally transformed technology? What happens after they exit the website or application. Owning these experiences is as critical to successful competitive digital transformation as understanding the experiences offered by your competitors. Ensure that you are correlating user and customer experience to application performance and business impact.
The best way to understand the strengths of your team for competitive digital transformation is to create a simple table of the skills mentioned above as rows, and team candidates as the columns. Check off skills as you build out the team. In essence, any skill not provided by the team will need to be provided by you as the Agent of Transformation.
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