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Whenstarting outto deploy artificial intelligence (AI) and machine learning (ML), executives of legacy companiesoftenview thechallengesmainly astechnical problemsparticularly findingsources ofinternal datato analyze and choosingthe righttools. What they may not appreciate is just how data-rich their legacy companies already are.

From utilities and mining, transportation and shipping, to financial services and more, legacy company operationsand customer interactionsgenerate a wealth of data. Such data can be harnessed to tacklea very wide rangeof issues: optimizing supply chains,predictingmaintenance, reducing accidents, increasing production output, improving operational efficiency, raising revenue productivity, and growing customer value.

To realize these opportunities using AI, however, legacy companies worldwidetypically soondiscover that their biggest problem isnottechnology its talent.Demandfor data scientists and analysts is intense and continues to exceed supply. Amazon, Facebook, Google, and other tech leaders hire massive numbers of data scientists, offeringthemfascinating challengesand compelling opportunities. Bycomparison,from the viewpoint of a sharp data scientist with leading-edge AI proficiency,a 100-year-old company that makes tractors, manufactures appliances, operates power plants, or ships containers may seem boring.

In addition, legacy companies are often located outside of major tech hubs such as Silicon Valley, Seattle, Austin, New York,or Los Angeles all of which can make it even more difficult for legacy companies to find the data scientists they need. There is a solution: a two-pronged talent strategy of hiring externally and building internally.

Recruiting TalentUsing Interesting Problems

To attract data scientists, legacy companies can and shouldfocus onthe compelling, unique, and real-world business problems that theyoffer. As Grant Case, director of sales engineering for Dataiku, a leader in applying AI and ML forenterprises, who works with legacy companies in Australia and New Zealand, told me recently, We need to give data scientists interesting problems to work on and turn into value. Thats where the magic happens.

Virtually every legacy company across all industries has very complex and thus very interesting questions and problems that offer robust opportunities for intellectually curious data scientists to dig into, such as:

Unsnarling extraordinarily complex airline systems when weather closes multiple hubs

Optimizing electricity grids and storage in a world of distributed, multi-directional, production, transmission, and storage

Predicting accidents to reduce on-the-job injuries

Optimizing global shipping networks and supply chains in real time for millions of containers every day

Maximizing crop production from each square foot/meter of earth

BerianJames, head of data science and AI at Maersk, the global shipping giant, described optimizing their shipping network as a really interesting data science problem.Maersk uses AI and ML to address a wide range of problems and opportunities, from providing its customers with arrival intelligence for their shipments to advancing the companys decarbonization efforts.

Virtually every legacy enterprise, if executives stop and think about it, offers fascinating business questions, problems, and challenges that can stimulate the intellectual curiosity and challenge the technical proficiency of data scientists and AI talent.Thus, an emerging best practice for legacy companiesto recruit the talent they needis touse these interesting questions tooffer data scientistsfresh opportunitiesto personally addressand have an impact in solvingengaging, unique business problems. Suchscenariosmay be more appealing than becoming the latest addition to the multitude at Facebook, Apple, Netflix, Alphabet, and similar firms.

DevelopingHomegrown TalentCombining the Right Aptitudewith Business Understanding

Hiring data scientistsexternallyisnt the only solution.While its not the answer in every case,developingdata science and AI proficiencywithinternaltalentis often faster, easierand more productive,and can be more than sufficient for a wide range of business purposes.Internal subject-matter experts, who have the right aptitudes and interests, already understand the business. Thiscan bemore desirableand impactfulthan going outside the company to hire a data scientist who although technically advanced is unfamiliar with the industry and business-specificor company-specificproblemsand challenges. Ive heard many stories from executives at legacy companies that hired data scientists and embedded them into the business with great hopes only to be disappointed when it proved difficult to integrate those data scientists with the ongoing business management and processes.

While internallydeveloped talentmay notreplace the most advanced data scientists for the knottiest problems, they can often significantly advance the companys AI and ML useand produce material business value. Certain disciplines found within legacy companies are particularly well-suited to developing AI and ML expertise. Engineers of all types, operationsresearchers, physical scientists, revenue managers, and others typically have the technical foundation, quantitative aptitude, proficiency with data, and intellectual curiosity tolearn how to apply AI and MLand develop the capabilities to do so.

Casegave the example of a steel company where chemists and metallurgists deal with production challenges that could be addressed with data and AI. You can find talented individuals who want to progress in their careers and enable them with the right training, he told me.Plus, they typically have the important advantage of understanding the business and, thus, credibility with business leaders.

Solving the People Problem

It is increasingly evident, in talking with executives in a wide range of legacy companies who are working to apply AI and ML,that the biggest challenges are culture, connecting data science and AI to business management and processes and, particularly, finding the talent needed.Its not primarily a technical problem.As executives of these companies tell me, theongoing challenges arefinding the right people and incorporating them, along with AI applications, into theactual working ofanenterprise.

Theseobservationsdemonstrate that now, more than ever, using data scienceand AIto realize practical gains requiresadeptbusiness leadership. Senior leaders must understand what reallydrives and enables data scientists so that their companies can attract, grow, and integrate this talent in a legacy businessto create business value.

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Legacy Companies Biggest AI Challenge Often Isnt What You Might Think - Forbes