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How to set up an artificial intelligence center of excellence in your organization

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Yogesh Malik
Yogesh Malik

Setting up a COE ( Center of Excellence ) for AI ( Artificial Intelligence ) could be a daunting task. Lack of skills and quality data sets could hold you back. But still you should not wait any longer and start with what you have, build skills by training people, and move ahead in gettering executive approval for building an artificial intelligence center of excellence

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Yogesh Malik Exponential Thinker | Technology Evangelist | Digital Futurist @ https://FutureMonger.com/ & https://welcome2050.com/ May 1 · 8 min read How to Set-up an Arti cial Intelligence Center of Excellence inYour Organization? Harnessing the potential of arti cial intelligence as part of a digital transformation strategy will be the key component in creating the intelligent enterprise of tomorrow. Arti cial intelligence is already rising to play a signi cant role in helping enterprises re-imagine their products and services, drive revenue, realize business e ciencies, and enrich the customer experience. Photo by Venveo on Unsplash
Andrew Ng is one of the most recognizable names in the Arti cial Intelligence community. He is Chief Scientist of Baidu. Andrew formed the Google Brain deep learning AI system while at Mountain View. Once he expressed: - In the past, a lot of S&P 500 CEOs wished they had started thinking sooner than they did about their Internet strategy. I think ve years from now there will be a number of S&P 500 CEOs that will wish they’d started thinking earlier about their AI strategy. But how do you start arti cial intelligence related initiatives in your organization? What problems will it solve? Stick with me here, because in this article we will be talking about those things. . . . Why DoYou Need an Arti cial Intelligence COE inYour Organization? It may seem that there are no immediate advantages of using Arti cial Intelligence in your organization and you may hesitate and ask, “Is there an AI solution to every business problem?”. Like the Internet revolution a decade ago AI is not optional anymore. Any delay is dangerous and if you want your organization to survive for another decade, you need to start embracing AI today. We already know that algorithms are ruling the world—from sorting cucumbers to curing cancers arti cial intelligence based algorithms are doing everything With this in mind let’s turn our attention to how today’s organizations can start AI Initiative within a minuscule group that can expand throughout the organization. But, is there a size that ts all solution? Small and medium size companies—Can they ignore AI? If not, then how do they start Center of Excellence for Arti cial Intelligence? No matter, what your think of AI but recent technological development associated with new patterns of globalization is threatening to create a new tomorrow—forcing businesses to re-imagine their products and
services, realize business e ciencies and drive customer experience to an altogether di erent level. You need to develop a team of inspired and talented, take initiatives for developing AI systems in your organization. This would take executive sponsorship, capturing benchmark that can demonstrate values—all focused on organization needs, making you propel AI initiatives from conceptualization to implementation . . . Strategy for Arti cial Intelligence Center of Excellence You need to bring in research orientated tech expertise and establish a platform that can catalyze the growth of the deep technology ecosystem and present the stimulus for innovation in the elds of data science and arti cial intelligence. This new team should function like an extended team to the external units to deliver excellence by scaling up existing/new product/project activities build around arti cial intelligence technology. From value creation to value realization, this new AI COE should deliver minimum viable product (MVP) with original ideas by experimenting with emerging technologies. A key step towards preparing COE for its success in the organization is to build prototypes with a long-term view and enhance ecosystems and partnerships to promote purposeful arti cial intelligence The people, process and technology must be in alignment for any new venture to be competent and holistic. These three components are the key steps towards a successful organizational transformation Executive sponsorship: Senior management people play a signi cant role in the organization and an e ective sponsorship from them is required so that you can improve collaboration across the organization. This will bene t you stay aligned on the strategic priorities. Once you secure their commitment sponsors can function as a point of escalation —but make clear you don’t engage them for day-to-day tactical issues
Finding the right team: This could be your biggest trouble. Getting people on-boarded for the right skills could make or break your center of excellence strategic plan. How do you source these people? Internal hire or external, local to your COE region or a mobile/remote worker from some another country’s o ce Capture benchmark that can demonstrate value: If you can’t measure it you can’t manage it. Your leadership want to see measured progress and you need to have a dashboard to represent internal performance. Make it transparent and comprehensive. Finding the right problem: Be it your organization internal problem or customer use case, you need to research multiple launches. You need a design thinking before you act on building an AI platform. Talk to sales, interview clients, know their business pain points that can be solved by the adoption of arti cial intelligence. Problem need to be thoroughly investigated so that you can apply the right AI tool to iron out, and present COE value proposition even for the early pilot launch. Leveraging the data to solve it: The importance of data availability and data quality are of pivotal value. Arti cial Intelligence provides a whole di erent meaning to the importance of data. The collection, distribution, and validation of data are thus important issues in the formulation of solutions involving AI Build a repeatable AI solution: A repeatable solution that can be o ered to others in the company or marketed to the customer, drives value to the business. A template approach that can be applied to the di erent situations is invariably favorable. Center of excellence leaders need to hook up with numerous product owners, designers, business analysts in building horizontal or industry speci c o ering solutions
. . . Challenge No #1: BuildingTheTeam Getting the right people with the right skills could be your single most prominent and serious challenge. Your current technology leaders and team members may be nding themselves in the corners. Where do you start? How do you source AI skills? Arti cial Intelligence, machine learning, data science and programming, people with these skills need to work together along with domain/industry expert because arti cial intelligence approach is not just about technical function alone. Recruiting and retaining AI talent could be a nightmare and this can continue for months. You need to get HR involved in nding individuals with arti cial intelligence skills and convince executive sponsors on this or your business take the risk of being left behind. You may uncover that the re-skilling of your current open-minded tech team could be a valuable option, so encourage them to take various
courses on arti cial intelligence and machine learning. Learning and research platform for your internal COE members, provisioning of new AI tools and test beds for existing and new projects, all this takes rather a bit serious thinking in terms or research work and operational/organizational skills. Go for few people who are cross-skilled, multi-skilled and harmonized with non-technical skills like communication, creativity and versatility. If you want to extend your AI portfolio beyond proof-of-concept and sandbox implementation, you will require real AI experts and those skills come at a price. Not just new AI skills, organizational level behavioral changes are needed, not just business knowledge and technology innovation but human insight need to bring in the products and features for developing highly successful long-term strategies . . . Challenge No #2: Making Data Actionable Many companies do not realize that they are sitting on the pile of data that could be a virtual goldmine if managed properly. On the other hand, lack of quality data could cost you a huge money if you want to get it xed. But the chances are that most of the data your organization has might not be all bad, but it won’t be able to solve the problem that you have. Having quality data requires building e cient data strategies and robust data management infrastructure. Your customers, partners, employees and brand ambassadors—data is everywhere. It is up to you how you to make data actionable by fetching insights and intelligence out of it. Lack of quality data might hamper your AI project as machine learning algorithms require a massive amount of data. So if you want to write a brand new data strategy for your organization, do not hesitate to put required e orts in doing so.
Identify various types of data available; its meaning, location, origin & structure. How data will be stored, analyzed, processed and protected. How various data will be packaged for reuse? What policies to implement for e ective data governance? Once done, do not consider it done. Make data review and measurement an ongoing process. Your data strategy is a roadmap and a key component for your arti cial intelligence projects. More than ever, your ability to manage data could be the single more critical component to your company’s success. • • • . . . OverToYou Now Start with what you have. Create building best practices, get your team trained on AI skills and collaborate with others to build minimum viable product for a pilot problem given the right dataset. Use cloud- based AI stack from AWS, Google or Azure if you don’t want to invest in hardware and licenses. You need to invest time and money in hiring AI-COE Project Lifecycle
skilled resources and keep your current team engaged in continuing learning new skills. So, tried old management advice on starting a COE won’t cut it, your approach must be unique and strategic for AI initiatives. Building an “AI rst” culture or “AI rst” policies could be very challenging, but without any delay you need to start looking at your organization’s existing products and services through the lens of arti cial intelligence. You need to start building good data strategies and create unique data sets so that you are ready with a failure-proof arti cial intelligence center of excellence. . . . This story is published in The Startup, Medium’s largest entrepreneurship publication followed by 320,131+ people. Subscribe to receive our top stories here.

More Related Content

How to set up an artificial intelligence center of excellence in your organization

  • 1. Yogesh Malik Exponential Thinker | Technology Evangelist | Digital Futurist @ https://FutureMonger.com/ & https://welcome2050.com/ May 1 · 8 min read How to Set-up an Arti cial Intelligence Center of Excellence inYour Organization? Harnessing the potential of arti cial intelligence as part of a digital transformation strategy will be the key component in creating the intelligent enterprise of tomorrow. Arti cial intelligence is already rising to play a signi cant role in helping enterprises re-imagine their products and services, drive revenue, realize business e ciencies, and enrich the customer experience. Photo by Venveo on Unsplash
  • 2. Andrew Ng is one of the most recognizable names in the Arti cial Intelligence community. He is Chief Scientist of Baidu. Andrew formed the Google Brain deep learning AI system while at Mountain View. Once he expressed: - In the past, a lot of S&P 500 CEOs wished they had started thinking sooner than they did about their Internet strategy. I think ve years from now there will be a number of S&P 500 CEOs that will wish they’d started thinking earlier about their AI strategy. But how do you start arti cial intelligence related initiatives in your organization? What problems will it solve? Stick with me here, because in this article we will be talking about those things. . . . Why DoYou Need an Arti cial Intelligence COE inYour Organization? It may seem that there are no immediate advantages of using Arti cial Intelligence in your organization and you may hesitate and ask, “Is there an AI solution to every business problem?”. Like the Internet revolution a decade ago AI is not optional anymore. Any delay is dangerous and if you want your organization to survive for another decade, you need to start embracing AI today. We already know that algorithms are ruling the world—from sorting cucumbers to curing cancers arti cial intelligence based algorithms are doing everything With this in mind let’s turn our attention to how today’s organizations can start AI Initiative within a minuscule group that can expand throughout the organization. But, is there a size that ts all solution? Small and medium size companies—Can they ignore AI? If not, then how do they start Center of Excellence for Arti cial Intelligence? No matter, what your think of AI but recent technological development associated with new patterns of globalization is threatening to create a new tomorrow—forcing businesses to re-imagine their products and
  • 3. services, realize business e ciencies and drive customer experience to an altogether di erent level. You need to develop a team of inspired and talented, take initiatives for developing AI systems in your organization. This would take executive sponsorship, capturing benchmark that can demonstrate values—all focused on organization needs, making you propel AI initiatives from conceptualization to implementation . . . Strategy for Arti cial Intelligence Center of Excellence You need to bring in research orientated tech expertise and establish a platform that can catalyze the growth of the deep technology ecosystem and present the stimulus for innovation in the elds of data science and arti cial intelligence. This new team should function like an extended team to the external units to deliver excellence by scaling up existing/new product/project activities build around arti cial intelligence technology. From value creation to value realization, this new AI COE should deliver minimum viable product (MVP) with original ideas by experimenting with emerging technologies. A key step towards preparing COE for its success in the organization is to build prototypes with a long-term view and enhance ecosystems and partnerships to promote purposeful arti cial intelligence The people, process and technology must be in alignment for any new venture to be competent and holistic. These three components are the key steps towards a successful organizational transformation Executive sponsorship: Senior management people play a signi cant role in the organization and an e ective sponsorship from them is required so that you can improve collaboration across the organization. This will bene t you stay aligned on the strategic priorities. Once you secure their commitment sponsors can function as a point of escalation —but make clear you don’t engage them for day-to-day tactical issues
  • 4. Finding the right team: This could be your biggest trouble. Getting people on-boarded for the right skills could make or break your center of excellence strategic plan. How do you source these people? Internal hire or external, local to your COE region or a mobile/remote worker from some another country’s o ce Capture benchmark that can demonstrate value: If you can’t measure it you can’t manage it. Your leadership want to see measured progress and you need to have a dashboard to represent internal performance. Make it transparent and comprehensive. Finding the right problem: Be it your organization internal problem or customer use case, you need to research multiple launches. You need a design thinking before you act on building an AI platform. Talk to sales, interview clients, know their business pain points that can be solved by the adoption of arti cial intelligence. Problem need to be thoroughly investigated so that you can apply the right AI tool to iron out, and present COE value proposition even for the early pilot launch. Leveraging the data to solve it: The importance of data availability and data quality are of pivotal value. Arti cial Intelligence provides a whole di erent meaning to the importance of data. The collection, distribution, and validation of data are thus important issues in the formulation of solutions involving AI Build a repeatable AI solution: A repeatable solution that can be o ered to others in the company or marketed to the customer, drives value to the business. A template approach that can be applied to the di erent situations is invariably favorable. Center of excellence leaders need to hook up with numerous product owners, designers, business analysts in building horizontal or industry speci c o ering solutions
  • 5. . . . Challenge No #1: BuildingTheTeam Getting the right people with the right skills could be your single most prominent and serious challenge. Your current technology leaders and team members may be nding themselves in the corners. Where do you start? How do you source AI skills? Arti cial Intelligence, machine learning, data science and programming, people with these skills need to work together along with domain/industry expert because arti cial intelligence approach is not just about technical function alone. Recruiting and retaining AI talent could be a nightmare and this can continue for months. You need to get HR involved in nding individuals with arti cial intelligence skills and convince executive sponsors on this or your business take the risk of being left behind. You may uncover that the re-skilling of your current open-minded tech team could be a valuable option, so encourage them to take various
  • 6. courses on arti cial intelligence and machine learning. Learning and research platform for your internal COE members, provisioning of new AI tools and test beds for existing and new projects, all this takes rather a bit serious thinking in terms or research work and operational/organizational skills. Go for few people who are cross-skilled, multi-skilled and harmonized with non-technical skills like communication, creativity and versatility. If you want to extend your AI portfolio beyond proof-of-concept and sandbox implementation, you will require real AI experts and those skills come at a price. Not just new AI skills, organizational level behavioral changes are needed, not just business knowledge and technology innovation but human insight need to bring in the products and features for developing highly successful long-term strategies . . . Challenge No #2: Making Data Actionable Many companies do not realize that they are sitting on the pile of data that could be a virtual goldmine if managed properly. On the other hand, lack of quality data could cost you a huge money if you want to get it xed. But the chances are that most of the data your organization has might not be all bad, but it won’t be able to solve the problem that you have. Having quality data requires building e cient data strategies and robust data management infrastructure. Your customers, partners, employees and brand ambassadors—data is everywhere. It is up to you how you to make data actionable by fetching insights and intelligence out of it. Lack of quality data might hamper your AI project as machine learning algorithms require a massive amount of data. So if you want to write a brand new data strategy for your organization, do not hesitate to put required e orts in doing so.
  • 7. Identify various types of data available; its meaning, location, origin & structure. How data will be stored, analyzed, processed and protected. How various data will be packaged for reuse? What policies to implement for e ective data governance? Once done, do not consider it done. Make data review and measurement an ongoing process. Your data strategy is a roadmap and a key component for your arti cial intelligence projects. More than ever, your ability to manage data could be the single more critical component to your company’s success. • • • . . . OverToYou Now Start with what you have. Create building best practices, get your team trained on AI skills and collaborate with others to build minimum viable product for a pilot problem given the right dataset. Use cloud- based AI stack from AWS, Google or Azure if you don’t want to invest in hardware and licenses. You need to invest time and money in hiring AI-COE Project Lifecycle
  • 8. skilled resources and keep your current team engaged in continuing learning new skills. So, tried old management advice on starting a COE won’t cut it, your approach must be unique and strategic for AI initiatives. Building an “AI rst” culture or “AI rst” policies could be very challenging, but without any delay you need to start looking at your organization’s existing products and services through the lens of arti cial intelligence. You need to start building good data strategies and create unique data sets so that you are ready with a failure-proof arti cial intelligence center of excellence. . . . This story is published in The Startup, Medium’s largest entrepreneurship publication followed by 320,131+ people. Subscribe to receive our top stories here.