5 key steps to building your AI practice

Artificial Intelligence (AI) is everywhere. TV. Movies. Wall Street. The World Economic Forum in Davos, Switzerland. And it should be top of mind for all Microsoft partners. Customers across the US are accelerating their pace of AI adoption, as referenced by Microsoft AI leader Eric Boyd in his recent AI blog. This is a great opportunity for partners as AI solutions will often require significant investments in infrastructure, data consolidation, and applications – and become a digital transformation driver for customers.

Microsoft partners have access to numerous resources to help navigate this fast-moving market. For those interested in a broad perspective on AI from the front lines, check out The Future Computed: Artificial Intelligence and its role in society. Additionally, a must-read for partners investing in an AI practice is the Artificial Intelligence Practice Development Playbook, which provides a detailed, 5-step guide to accelerate and optimize your AI practice. I’ve summarized and packaged up these steps below to help you get started.

Define strategy

For partners planning an AI practice, defining a strategy is the first step. Building a successful AI practice requires significant investment and commitment, but there are simple steps partners can take to begin offering AI solutions to customers by leveraging pre-built AI APIs, as mentioned in the guide.

Understanding and interacting with your existing customer base may be the best opportunity to lay the groundwork for this strategy. Any initial investments you make should be consistent with your current business strategy, capabilities, and target customer profile. This will help define your direction and differentiation. Will you focus on building intelligent agents (bots), modernizing applications with AI, or using AI to transform business processes? It depends on what you do well today and what your existing customers are looking for.

Using Microsoft resources will jumpstart your practice and maximize profitability to help you begin planning with your leadership team now. The practice development playbook provides extensive guidance on how to get started with AI and advancing along the AI practice maturity model.

Hire & train

Going deep on AI will require people investments in a highly competitive field where talent is scarce. Building an AI practice entails investments in data scientists, data architects, data engineers, and developers.

Filling these roles can be challenging, time consuming, and expensive, for two main reasons. First, data scientists require significant training: according to Gartner TalentNeuron, 34% of relevant job postings require a doctorate level of education. Second, there is an extreme shortage of this type of talent: the McKinsey Global Institute has estimated that the U.S. economy could have a shortage of 250,000 data scientists by 2024. The tight labor market can also lead to turnover as partners, technology companies, and end customers all compete for limited talent.

Partners without the ability and appetite (or expected customer demand) to make these investments should not fear. There are ways to monetize AI without building out a team of PhDs, and many partners are closer than they think to solving AI market needs profitably. Train your existing resources to leverage pre-built APIs to add intelligence to your offering. For more demanding customer needs, consider partnerships with specialist service providers or ISVs.

Operationalize

Compared to recruiting data scientists, operationalizing AI in your practice is a walk in the park! Most partners have well-defined processes and infrastructure in place to manage their business. For new businesses or partners new to the Microsoft ecosystem, this section of the practice development playbook will inform your plan.

Existing partners should consider how to operationalize AI internally. Building intelligence into internal apps, creating customer-facing intelligent agents, or otherwise leveraging AI to run your business can provide your team hands-on training, not to mention the value AI can bring to your business.

Go to market & close deals

If you properly defined your AI strategy, this step should be a natural outgrowth. The practice development playbook provides AI marketing guidelines:

Partners should be mindful of applying their current marketing tactics to AI, as there are important differences from a traditional technology sale. For one, the buyer is likely different. While CIOs will undoubtedly invest in AI, partners must become adept at using AI to address business problems and selling directly to a line of business—or in some cases, boards of directors. The high cost of qualified talent and need to educate buyers will also require partners to be disciplined marketers and have a well-defined sales process. Sharpening your focus to an industry or specific solution will be critical to driving a high win rate necessary to support these costs.

Optimize & grow

If it is not yet clear, you will not build an AI practice overnight. The good news is that you don’t need to be or employ a data scientist to offer AI to your customers. The Microsoft approach to AI enables you to leverage pre-built APIs and accessible, pre-trained models to get started today. Additionally, there are several resources to help you along your journey, such as the Cloud Enablement Desk to onboard partners into Microsoft and ramp AI resources, Learn AI for hands-on training and AI Inner Circle for a deeper connection with Microsoft’s AI team. There are also hundreds of sellers within Microsoft who can and want to help partners co-sell across the enterprise to drive the shared success of customers.

Leverage Microsoft resources to jumpstart your AI practice today!

Justin Ross is a Sr. Product Marketing Manager on Microsoft’s US Intelligent Cloud team. In this role, he is responsible for the US System Integrator strategy and programs for the Azure business across both Applications & Infrastructure and Data & AI. Justin has a deep background in the Microsoft partner ecosystem, with previous roles in partner program management, channel development and partner marketing, primarily in the Dynamics business. Prior to Microsoft, Justin worked in M&A as both and investment banking advisor and in a corporate development capacity, and has additional experience in technology-focused equity research. Justin holds an MBA from the MIT Sloan School of Management.

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