Scaling Artificial Intelligence (AI) – 4 Reasons for People and Enablement

The guide to closing the skills gap in companies

Data has been called the most valuable resource of the new world. Ultimately, however, people are essential to derive value from data and apply it in innovative ways.
Leaders recognize that advances in intelligent automation and artificial intelligence will have a significant impact on the workforce. And that they need to invest in talent that understands the intersection of data and algorithms and their impact on workflows. This article highlights strategies that will have the greatest impact on closing organizational skills gaps.

I think we’ve all heard the expression by now that data is the most valuable resource in the new world, and organizations have tons of it.

Most organizations are already capable of basic business intelligence (BI) processes. They use a few analytics dashboards or at least Excel sheets. I’d say about half of them are moving up the maturity curve, and the majority are capable of ad-hoc data analytics.

Enterprises are maturing in the area of data use, but they are still struggling to integrate and scale Data Science, Advanced Analytics (AA) and Artificial Intelligence (AI) into everyday decision making in real time. Proof-of-concept after proof-of-concept is being conducted, but realizing the value of their investments in AI and Machine Learning (ML) is failing to materialize. And with that, unfortunately, the majority of organizations are unable to extract the insights they are really looking for from their data usage.

Four reasons organizations struggle with data use:

  1. You can’t get access to their data.
  2. They don’t have the right tools or the right infrastructure.
  3. They don’t have the right talent.
  4. They lack the right methodology, common framework, and common ways of working.

Availability and quality of qualified workforce are at risk

Data has been called the new natural resource. An article in The Economist even goes so far as to say that it has replaced oil as the world’s most valuable resource.

Ultimately, however, people are at the heart of business – and without talented and innovative people, the power of data goes unused. In other words, people are essential to deriving value from data and applying it in innovative ways.

That’s why, in this article, I’d like to start by taking a closer look at “the right talent” to scale data use.

Already today, companies are struggling to find skilled workers. This shortage is only expected to increase. By 2030, the global talent shortage could reach more than 85 million people. This is not a shortage of workers – but a shortage of workers with the right skills.

As technology advances, 100 percent of jobs, 100 percent of occupations and 100 percent of industries will change. As a result, some 120 million people in ten of the world’s largest economies will need to reskill over the next few years.

Organizations know they need to act, but do they know where to start?

Many companies tell me they need more data science experts. But I believe that we need to address things. Have you actually defined in your company what skills you’re going to need for the future and how many people you’re going to need with those skills? Once we have that question answered for us we can figure out what to do about it and how to either build or buy those skills.

We know that there are not enough skills in the market to buy them on an ongoing basis. So, for the most part, we have to take the approach of retraining for the future.

AI-assisted automation: An opportunity and a challenge

There’s no denying that smart automation will have a huge impact on workers. Based on a global country study conducted in 2018, it is estimated that up to 60 million workers in the world’s 12 largest economies could be reduced or reassigned to other roles by employers in the next three years alone.

At the same time, companies can support their employees with the use of intelligent automation solutions to reduce their skill gap somewhat.

AI systems applied to HR, for example, can also help reduce employee churn, make hiring processes more efficient and attractive, and recommend and plan retraining – personalized to the employee.

Closing the skills gap: strategies and recommendations

Solving the skills problem is no easy task. At the enterprise level, organizations must take a leadership role that goes beyond recruiting and traditional training initiatives and commit to continuous, strategic exploration of new pathways.

Identify the key competencies required for success and align your future skills strategy across the entire employee lifecycle – from recruitment to team building, learning, career coaching, compensation and retention.

AI can help enable visibility and skill personalization across the extended learning ecosystem. Analytics can reveal visibility into skill distributions, trends, and future skill gaps.

Within the organization, create agile teams with heterogeneous skill sets to enable experience-based peer-to-peer innovation and create a culture where learning becomes an essential part of the job. Create opportunities for job sharing and internal mobility that focus on skill development.

Leverage initiatives such as massive open online courses, code schools, and industry knowledge networks. Apply AI to source and harmonize the most relevant educational assets for your learners.

And last but not least: Make it personal

Personalization has become part of everyday life in the consumer world. Employees expect the same personalized experience in their work environment. Employees want careers, skills and learning that are tailored to their experiences, goals and interests. By enabling them to do just that, organizations also manage to place their employees exactly where they can add the most value to the organization.

Invest in talent that understands the intersection of data and algorithms and their impact on workflows. People are essential to creating value from data and applying it in innovative ways.

Britta Daffner ist seit über einem Jahrzehnt in der Technologie- und Daten-Industrie zu Hause. Ihr Credo: Innovation und Digitalisierung von Unternehmen vorantreiben – durch Technologie und Führung. Dafür befähigt sie als Abteilungsleiterin im Bereich „Artificial Intelligence & Data Science“ in der Beratungssparte von IBM Unternehmen dabei, das volle Potential aus Daten zu nutzen. Daneben ist sie Autorin des Buches "Die Disruptions-DNA, sowie Coach und Mentor von Leadern, die in der Konzern- und Wirtschaftswelt etwas verändern wollen.

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