While multiple elements play a part in AI success, the most powerful factor—by far—is that leadership consistently communicates a clear vision and commitment to AI. In fact, according to The AI Strategy Roadmap, 100% of organizations at the most advanced stage of AI readiness report strong vision and commitment from senior leaders, compared to 1% of organizations at the earliest stage.

To learn more about his role and the emerging best practices for AI leadership, I sat down with Florin Rotar, Chief AI Officer (CAIO) at Avanade. Rotar is the company’s first-ever CAIO and has been tasked with leading the company to deliver sustainable AI value both for clients and for Avanade itself. We discussed a range of topics, including:

• The milestones that led Avanade to appoint a CAIO
• What distinguishes a dedicated CAIO from other roles in the C-suite
• The critical role that a leader-driven AI strategy plays in AI value creation
• “Guardrails” versus “speed bumps”: Avanade’s approach to responsible AI
• Rotar’s advice for aspiring CAIOs

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Susan: We’ve seen a strong pattern of organizations appointing a CAIO to the C-suite as they progress in their use of AI. What were the milestones that led up to Avanade appointing a CAIO?

Florin: From our perspective, several pivotal milestones led us to prioritize AI as a central strategic focus for Avanade. First, we recognized AI as a potent catalyst for growth and a means to reinvent ourselves, aligning with our strategic priorities and our purpose to make a genuine human impact.

Second, we understood that AI transcends organizational boundaries, so we would have to streamline our approach. In our highly matrixed structure we knew we needed executive-level leadership and focus. One of my first priorities stepping into the role was to launch Avanade’s Center for AI: a hub that pulls together different parts of our business behind a clear AI strategy and vision.

Third, this journey underscored the need for a leadership approach that prioritizes people alongside business, technology, and data considerations. The establishment of the CAIO role reflects a holistic approach that integrates diverse expertise to drive AI innovation.

Susan: Many technology trends have led to the creation of new C-suite roles over the years. What’s different, if anything, about the CAIO?

Florin: When you look at the role of CAIO, you get an appreciation for what’s top of mind for the board and CEO when it comes to AI. It is a uniquely ubiquitous topic that is relevant to everyone from the general counsel to the chief executive officer, chief growth officer, chief people officer, chief information officer, and beyond. The CAIO role encapsulates the breadth of strategic considerations at board level that demands specific executive attention.

My appointment to CAIO was largely based on my background in modern workplace technologies and experience bridging people and technology. This role is much more about people than technology, so as a CTO-turned-CAIO, I spend most of my days talking about the human impact of AI.

Another important distinction of the CAIO role involves overseeing responsible AI practices. While innovation thrives in experimental settings, we must uphold ethical and regulatory standards. At Avanade, we’ve engaged in lively C-suite debates about balancing risk and reward while advancing AI at speed. As CAIO, I’m ultimately responsible for navigating these considerations to ensure ethical decision-making.

Susan: Our research underscores the vital role of a leader-driven AI vision and strategy for value creation. How does that resonate for you and the role of CAIO?

Florin: In my experience, AI leadership truly begins with the company board of directors setting strategic guidance and priorities. Value from AI is inextricably linked to strategic alignment. I see many leaders overestimating short-term gains while underestimating the long-term potential for AI to re-write the rule book.

Therefore, I’ve collaborated extensively with our executive team to thoroughly define our “why for AI”—keeping our organizational purpose as our guiding principle, or north star. Rather than fixating on specific use cases, we prioritize understanding the foundational reasons behind our adoption of AI. Our competitive edge comes from ensuring our AI strategy aligns with our purpose and the strategic outcomes we aim to achieve. Without a clear understanding of our “why,” we risk dispersing our efforts across too many initiatives simultaneously.

Clarifying the “why” behind AI initiatives ensures that you align with organizational goals and prioritizes how to engage your people. This is essential because people are arguably the most critical aspect of any organization’s AI strategy and should not be overlooked. Even as AI copilots, for example, begin to share the load, human expertise and accountability shouldn’t be relinquished. Employee training and support is key to not only educating employees in responsible AI use but showing them that AI is about helping them realize their full potential in role.

Without a clear understanding of our “why,” we risk dispersing our efforts across too many initiatives simultaneously.

Florin Rotar, Chief AI Officer (CAIO), Avanade

Avanade’s AI Readiness Report shows that 98% of business and IT executives agree that support will be required to onboard and train employees to use generative AI tools like Microsoft Copilot.¹ As users transition from adoption to advocacy with the support of a people-first approach and adequate training, the true value of AI emerges at scale.

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Susan: You describe responsible AI as a set of guardrails, rather than speed bumps. Can you elaborate on that?

Florin: First, I must emphasize the importance of adopting AI ethically and safely, balancing the need for speed with our responsibility to proceed in a responsible, human-centric manner. Following Avanade’s very early experiences integrating AI, we concluded that a responsible AI framework was the most effective way to put this into practice.

“Speed bumps” symbolize attempts to control AI usage to manage potential unknown outcomes. The consequence is predictable: that approach hinders AI adoption and skill development. At Avanade, we firmly believe that there’s as much risk in moving too slowly as there is in moving too quickly. Therefore, our framework focuses on establishing “guardrails”, which enable us to accelerate progress by providing clear guidelines for decision-making authority and accountability—simply put, what to do and what not to do. This flexible approach allows for failure, quick learning, and onward progress—a cycle of insights we’re now equipped to share with our clients.

At Avanade, we firmly believe that there’s as much risk in moving too slowly as there is in moving too quickly.

Florin Rotar, Chief AI Officer (CAIO), Avanade

This mindset also led to establishing our “Avanade School of AI,” which offers every single employee responsible AI training. The core of this initiative is to change the mindset around AI by mitigating fears and misconceptions about the technology and empowering our employees to embrace the potential of AI with understanding and trust.

Susan: Now that you’ve been in role for more than six months, what advice would you give to aspiring CAIOs?

Florin: Four main takeaways stand out that I urge leaders in the role of CAIO to diligently consider.

First, AI value doesn’t start with technology. It starts with what’s most important: people. We need to look beyond productivity gains and imagine how generative AI can help people become the best versions of themselves, replacing tasks and not jobs. This builds trust and promotes adoption—the business outcomes follow naturally.

Second, don’t forget your “why for AI.” The path to differentiation is to map AI value to the strategic objectives outlined by your CEO and board of directors. Once the “why” is in place, you can drill down on the “what” and the “how.” While most organizations begin by implementing use cases that focus on optimization, I would encourage leaders to be bolder. AI has the potential to disrupt processes, functions, and business models—these are the areas that will drive growth, innovation, and differentiation.

Third, responsible AI is non-negotiable. It must be anchored at the board level and be regarded as a potential for strategic advantage, not just compliance and risk mitigation. This cannot be stressed enough: a framework for governance, including a combination of process, compliance, technology, and training, enables you to move fast while upholding ethical standards.

Fourth, it’s crucial to be discerning about the technology ecosystem you commit to. Differentiation lies in adopting a strategic enterprise architecture mindset: will you be consuming existing solutions, customizing them, or creating entirely new ones? This translates into the three Cs of AI: consume, customize, or create. While we’re inclined towards Microsoft’s ecosystem, we recognize the frenetic market landscape, where choices can significantly impact costs, value, and futureproofing, so it’s critical to make informed decisions in this regard.

Next steps

For more information on how to accelerate your organization’s path to value with AI, please download The AI Strategy Roadmap: Navigating the stages of AI value creation.

Footnotes

¹Generative AI Organizational Readiness Report, Avanade.

To learn more about how Avanade helps organizations ready people, processes and platforms for AI, please visit AI | Avanade.

The post 4 strategies to accelerate AI value creation: Advice for chief AI officers appeared first on The Microsoft Cloud Blog.

AI has come a long way since John McCarthy coined the term at the Dartmouth conference in 1956.¹ Since then, we’ve seen multiple waves of innovation, from machine learning to neural networks and, of course, generative AI. Today we are in the midst of a platform shift that is changing the way we live and work. The challenge and the opportunity for leaders is to lay the groundwork today that will enable your organization to deliver value from AI in the months and years to come.

The AI Strategy Roadmap shares what Microsoft has learned about the emerging best practices that organizations are using to create sustainable value with AI, as well as actionable insights to help you focus on the steps that are most likely to drive results. Here are some of the questions we sought to answer with this research:

1. What are the characteristics of organizations that realize value from AI at scale?
2. What do leaders need to think about from a technology, business, and organizational perspective to enable them to meet their goals?
3. What is the roadmap to success with AI? (Spoiler alert; there is no single roadmap.)
4. What is the top priority of most AI initiatives, and how does that change as organizations realize value?
5. What is the single most predictive factor for success with AI?

We worked with Ipsos to survey more than 1,300 business and technology decision makers across multiple regions and industries. Ipsos then built a predictive model, using advanced analytics, to identify the most powerful factors that affect time to value.

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The result is a set of evidence-based best practices intended to help you build your AI roadmap or pressure-test your existing plan with confidence. Here are some of the highlights.

It’s not (just) about the technology

AI technology is more powerful than it’s ever been, and the pace of innovation is humbling. Yet the ability to realize value from AI depends as much on strategic, organizational, and cultural factors as it does on technology.

Part one of the e-book offers deep insights into the five drivers, introduced in Building a Foundation for AI Success, that contribute to an organization’s ability to deliver value with AI.

There is no single roadmap for success

Given the diversity among organizations in terms of size, age, region, industry, and other attributes, there is no “one size fits all” roadmap. Our research describes five stages of AI readiness, from organizations just getting started to those already realizing sustainable and measurable value from AI at scale. The chart below lays out the descriptions of each stage, along with corresponding profile data.

You’ll note that organizations at the exploring and planning stages tend to be older and less likely to be cloud-first, compared to those at the realizing stage. We also saw corresponding trends related to the organization’s success at realizing significant value from AI; 3% at the exploring stage, compared with 96% at the realizing stage. So it became clear that we needed to look not only at what leaders should be prioritizing to optimize for AI, but when.

We needed to look not only at what leaders should be prioritizing to optimize for AI, but when.

Part two of the e-book includes guidance to help you map your strategy and identify the highest-impact actions based on your organization’s stage of readiness and unique needs.

In the exploring stage, for example, the primary focus is on AI strategy and experience, as learning about and ideating on potential AI use cases is the best way to build momentum.

As organizations move to the planning stage, the focus shifts to business strategy (to prioritize use cases and ensure they map to business objectives) and technology and data readiness (to ensure the organization has access to the data and infrastructure needed to run large AI models at scale).

In the implementation stage, and throughout the scaling and realizing stages, the priority shifts to organization and culture. This reflects the fact that, by now, some of the critical groundwork needed to deploy AI projects is in place, and enablement is the next priority. This includes steps such as identifying AI experts, defining an operating model, and, most importantly, securing the leadership vision and support needed to deliver sustainable value.

As organizations realize greater value from AI, they tend to increase their focus on growth

While efficiency and productivity will always be paramount, organizations at the realizing stage focus on growth-oriented objectives—such as customer experience and product and service innovation—at almost twice the levels of those in the exploring stage.

Senior leadership’s vision and support are—by far—the strongest drivers of success

A leader-driven AI strategy is most strongly associated with AI value creation. One hundred percent of senior leaders of organizations at the realizing stage have clearly communicated their commitment to AI compared to 6% at the exploring stage. We also saw that, as organizations reach the more advanced stages, they become more likely to add a chief AI officer (CAIO) to their executive team. By the time they’ve reached the realizing stage, nearly two-thirds have appointed a CAIO.

Achieve more in the age of AI

The AI Strategy Roadmap lays out the most effective steps you can take to build momentum toward your goals, based on your organization’s stage of readiness. We hope the insights we’ve shared help you lay the foundation for sustainable AI innovation, accelerate your organization’s time to value, and help you achieve more in the age of AI.

For more information on how to accelerate your path to value with AI, please download The AI Strategy Roadmap: Navigating the stages of AI value creation.

¹The Meeting of the Minds That Launched AI, IEEE Spectrum.

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To date, this series has explored four of the five drivers of AI readiness: business strategy, technology and data strategy, AI strategy and experience, and organization and culture. Each is critical to an organization’s ability to use AI to deliver value to the business, whether it’s related to productivity enhancements, customer experience, revenue generation, or net-new innovation. But nothing is ultimately more important than AI governance, which includes the processes, controls, and accountability structures needed to govern data privacy, data governance, security, and responsible development and use of AI in an organization.   

“We recognize that trust is not a given but earned through action,” said Microsoft Vice Chair and President Brad Smith. “That’s precisely why we are so focused on implementing our Microsoft responsible AI principles and practices—not just for ourselves, but also to equip our customers and partners to do the same.” 

In that spirit, we have collected a set of resources that encompass best practices for AI governance, focusing on security, privacy and data governance, and responsible AI. 

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Security

Just as AI enables new opportunities, it also introduces new imperatives to manage risk, whether related specifically to AI usage, app and data protection, compliance with organizational and legal policies, or threat detection. The Microsoft Security Blog includes a set of resources to help you modernize security operations, empower security professionals, and learn best practices to mitigate and manage risk more effectively.  

One of the first steps you can take is to understand how AI is being used in the organization so you can make informed decisions and implement the appropriate controls. This post lays out the primary concerns leaders have about implementing AI, as well as a set of recommendations on how to discover, protect, and govern AI usage. 

For example, you may have heard of (or already be implementing) red teaming. Red teaming, according to this post by the Microsoft AI Red Team, “broadly refers to the practice of emulating real-world adversaries and their tools, tactics, and procedures to identify risks, uncover blind spots, validate assumptions, and improve the overall security posture of systems.” The post shares additional education, guidance, and resources to help your organization apply this best practice to your AI systems. 

Microsoft’s holistic approach to generative AI security considers the technology, its users, and society at large across four areas of protection: data privacy and ownership, transparency and accountability, user guidance and policy, and secure by design. For more on how Microsoft secures generative AI, download Securing AI guidance.  

Privacy and data governance

Building trust in AI requires a strong privacy and data governance foundation. As our Chief Privacy Officer Julie Brill has said, “At Microsoft we want to empower our customers to harness the full potential of new technologies like artificial intelligence, while meeting their privacy needs and expectations.” Enhancing trust and protecting privacy in the AI era, originally posted on the Microsoft on the Issues Blog, describes our approach to data privacy, focusing on topics such as data security, transparency, and data protection user controls. It also includes a set of resources to help you dig deeper into our approaches to privacy issues and share what we are learning. 
 

Data governance refers to the processes, policies, roles, metrics, and standards that enable secure, private, accurate, and usable data throughout its life cycle. It’s vital to your organization’s ability to manage risk, build trust, and promote successful business outcomes. It is also the foundation for data management practices that reduce the risk of data leakage or misuse of confidential or sensitive information such as business plans, financial records, trade secrets, and other business-critical assets. This post shares Microsoft’s approach to data security and compliance so you can learn more about how to safely and confidently adopt AI technologies and keep your most important asset—your data—safe. 

Responsible AI

“Don’t ask what computers can do, ask what they should do.” That is the title of the chapter on AI and ethics in a book Brad Smith coauthored in 2019, and they are also the first words in Governing AI: A Blueprint for the Future, which details Microsoft’s five-point approach to help governance advance more quickly, as well as our “Responsible by Design” approach to building AI systems that benefit society. 

The Microsoft on the Issues Blog includes a wealth of perspectives on responsible AI topics, including the Microsoft AI Access Principles, which detail our commitments to promote innovation and competition in the new AI economy and approaches to combating deepfakes in elections announced as part of the new Tech Accord announced in February in Munich. 

The Responsible AI Standard is the product of a multi-year effort to define product development requirements for responsible AI. It captures the essence of the work Microsoft has done to operationalize its responsible AI principles and offers valuable guidance to leaders and practitioners looking to apply similar approaches in their own organizations.

You may also have heard about our AI customer commitments, which include:  

• Sharing what we are learning about developing and deploying AI responsibly and assist you in learning how to do the same. 
• Creating an AI assurance program.
• Supporting you as you implement your own AI systems responsibly. 

The Empowering responsible AI practices website brings together a range of policy, research, and engineering resources relevant to a spectrum of roles within your organization. Here you can find out more about our commitments to advance safe, secure, and trustworthy AI, learn about the most recent research advancements and collaborations, and explore responsible AI tools to help your organization define and implement best practices for human-AI interaction, fairness, transparency and accountability, and other critical objectives. 

Next steps

As Brad Smith concluded in Governing AI: A Blueprint for the Future, “We’re on a collective journey to forge a responsible future for artificial intelligence. We can all learn from each other. And no matter how good we may think something is today, we will all need to keep getting better.” 

Download our e-book, “The AI Strategy Roadmap: Navigating the Stages of AI Value Creation,” in which we share the emerging best practices that global leaders are using to accelerate time to value with AI. It is based on a research study including more than 1,300 business and technology decision makers across multiple regions and industries.

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What does it mean to become an AI-powered organization?

It’s long been understood that technology isn’t an island; it requires people and processes to deliver results. But AI, and especially generative AI, is unlike any technology that has come before, which requires us to look at the equation a bit differently, taking an intentional approach to deploy and drive adoption and value.

In this post, we’ll explore a few of the emerging best practices that are helping leaders position their organizations for success in the age of AI:

• Start at the top: communicate your vision and priorities
• Empower diverse teams
• Foster a culture of agile experimentation
• Empower employees with learning and skilling resources
• Establish a clear operating model

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Start at the top: Communicate your vision and priorities

Becoming an AI-powered organization begins with clarity, and clarity begins at the top. It’s critical for leaders—starting with the CEO and throughout the entire C-suite—to communicate their organizational priorities and their vision for how AI will support the future of the business so teams know what they’re solving for and can propose and execute on the highest-impact use cases.

Empower diverse teams

Studies continue to demonstrate the relationship between diversity and business performance, and this is as or more valuable with AI, given its wide applicability to many different types of use cases and human impacts. Leaders should reinforce the importance of diverse teams that represent multiple areas of the organization, as innovation can come from anywhere, whether it is human resources (HR), marketing, advertising, finance, sales, product management, or another group.

Diverse teams also deliver significant value related to anticipating potential issues, as having broader representation on a team helps to ensure that AI systems meet the needs of the widest possible range of customers and consumers.

Foster a culture of agile experimentation

Successful AI projects involve trial and error, experimentation, and a willingness to learn from failures as well as successes. But this can only happen when leaders actively encourage and value a growth mindset and create the conditions for psychological safety. This does not mean that “anything goes,” however. It does mean shifting from a linear development approach to more of an iterative one.

This is where process comes in. An iterative approach—what developers know as agile development—is specific, rigorous, and proven, and well-suited to the nature of AI. Agile development values principles such as customer satisfaction, collaboration between business and technology experts, and short timescales, among other things. Fostering agile approaches across the organization will help to create the kind of alignment among business and technology stakeholders that is critical to the success of AI initiatives and will help increase the velocity at which your organization is able to innovate.

Empower employees with learning and skilling resources

Because AI represents a new way to work, and it’s evolving so quickly, it’s important to offer continuous learning resources to enable employees across the organization to acquire new AI skills and stay abreast of industry trends.

• Encourage employees in the business to build their understanding of how AI works and experiment with AI-powered tools. This will enable them to stay current and envision new ways to use AI in their organization.
• Provide technology teams with access to skilling content on critical topics such as model building and refinement, prompt engineering, and responsible AI development so they can keep current with the latest tools, approaches, and techniques.
• When possible, deploy AI to entire teams within a specific business function, like customer service or sales, to enable them to share insights, learn from one another, and multiply impact.

Establish a clear operating model

As the number of AI-related projects grows, it becomes increasingly important to establish a clear operating model so that you can build sustainable value across the organization. Whether it is a center of excellence, a distributed team, or a different structure, a clear operating model should enable all teams working with AI—irrespective of their geographical location or business unit—to share best practices and resources, training and skilling tips, measurement strategies and learnings, and provide leadership with visibility on AI projects at an aggregate level.

Next steps

Organizations around the world are just starting their journey to become AI-powered. Yet because AI is such a significant change, and that change is coming faster than ever, leaders are increasingly trying to anticipate what’s next. One thing is clear—leaders who lean in early to the opportunities that AI represents will be best positioned to drive value for their stakeholders.

Stay tuned for the final post in our series: “Building a foundation for AI success: AI Governance,” in which we will explore the security, data privacy, and responsible AI best practices that are critical to building trust in and success with AI.

Download a copy of the “Building a Foundation for AI Success: A Leader’s Guide” white paper.

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This is part four of a six-part blog series. See part one, part two, part three, and download the white paper.

Building your organization’s understanding and experience are fundamental to any successful AI strategy. In this post, we’ll focus on emerging best practices that can help you position your AI projects for success.

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Start with a diverse team

Assembling a team that brings a diverse set of roles and experiences is a crucial first step toward realizing value with AI. A combination of technical, business, finance, marketing, security, data privacy, responsible AI and other experts is key, because diverse viewpoints tend to surface potential issues early on, reducing the need for re-work later in the project. A diverse team also helps to build the institutional knowledge that is so critical to your organization’s ability to scale AI projects successfully over time.

As your organization deploys more use cases and learns from deployments, you will be better able to anticipate and address potential barriers to implementation and success. One of the most common examples is the “perpetual proof of concept” loop, which tends to point to issues related to data, infrastructure, or lack of alignment between projects and valued business outcomes.

Think—and act—like a scientist

AI relies on probabilities and statistical models to identify patterns and relationships, unlike computing systems of the past that used precise rules that generated predictable outputs. The probabilistic nature of AI requires a different approach to development—one that is more geared toward testing and learning—than some organizations may be accustomed to.

“The most successful organizations tend to have a mindset of experimentation and learning so they can see what’s working and systematically tackle any issues that arise,” says Eric Boyd, Corporate Vice President, Azure AI Platform, at Microsoft. “That said, you really have to have a clear vision of what you’re trying to achieve with your AI model to determine how well it is performing.”

Pairing experimentation with structured, repeatable processes is very much in line with scientific method; developers know it as agile development. Whatever you call it, a focus on iteration and continual learning, combined with incremental planning, team collaboration, repeatable processes, and measurement rigor are characteristics of organizations that tend to see the most benefit from AI.

Use the right tool for the right job

The AI landscape is evolving rapidly, and a critical driver of success is to apply the right model to your use case—in other words, use the right tool for the right job. There are many types of AI models, including models that can find patterns and generate recommendations, understand languages and handle complex queries, summarize and translate text, recognize visual objects and scenes, and produce natural language, images, and code, among others.

To best position your organization to realize value, it’s critical to establish clear communication between developers and subject matter experts in the business so that developers know exactly what they are solving for and can choose the model best suited to the data and the use case. This means clearly articulating the business challenge you’d like to address with AI, your desired outcomes, and how you will measure success.

Measure the impact of AI projects holistically

Measuring the impact of AI projects should encompass a range of stakeholders and objectives and include both quantitative and qualitative methods. Following are a few suggestions on potential metrics to help you get started.

Next steps for successful AI development

Successful AI development is a blend of diverse teams, continuous learning, and a healthy tolerance for ambiguity. But the most important step is the first one.

“You’ve got to get in the game,” says Eric Boyd. “Try something. Iterate and learn, try different things, and see what works for your application. Empower everyone in your organization to discover how AI can transform your business.”

Stay tuned for the next post in our series: “Building a foundation for AI success: Organization and culture,” in which we will explore additional best practices that are frequently cited as critical to AI success.

Download a copy of the “Building a Foundation for AI Success: A Leader’s Guide” whitepaper.

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AI is redefining the boundaries of what’s possible, but the unique demands of AI—from vast data volumes and high-speed processing requirements to complex security and compliance challenges—require a strategic approach to data and technology infrastructure.

“It’s been over a year now since generative AI became mainstream. We are through the science experiment phase and leading companies are now putting AI into action,” says Wangui McKelvey, General Manager, Microsoft Azure Data Analytics.

In this post, we’ll focus on the five data and technology fundamentals required to deliver meaningful, sustainable, and responsible value creation with AI:

1. Align technology strategy and business strategy
2. Assess infrastructure needs and goals
3. Prepare your data estate to smooth the path from proof-of-concept to production
4. Consider build-versus-buy decisions
5. Determine a strategy for regulatory compliance and safeguarding AI assets

Five pillars of AI success

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Align technology strategy and business strategy

Successful AI projects begin with a clear, prioritized, and valued set of business objectives, such as maximizing productivity and efficiency, improving customer experience, or growth-oriented objectives such as revenue acquisition or product innovation. These goals will help you prioritize use cases against likelihood of impact and realistically assess feasibility based on data and infrastructure requirements. It’s also useful to think of your technology investments in aggregate as an investment portfolio, which will enable you to set clear success criteria and reallocate resources across projects as needed.

Assess infrastructure needs and goals

Moving successfully from proof-of-concept (POC) to production with AI depends on a mix of technology and business factors that, ideally, must work together.

“Understanding how your business strategy maps to your product strategy, and then how your product strategy maps to your infrastructure, is key,” says Omar Khan, General Manager, Microsoft Azure Product Marketing. “AI-optimized infrastructure will help accelerate both building AI solutions and integrating AI into applications.” From a technology perspective, the most critical requirement is access to infrastructure that is built for AI—with the ability to run large AI workloads and perform securely and reliably at scale.

• Cloud-based AI deployments leverage third-party cloud service providers to access scalable and flexible computing power without the need for extensive on-premises infrastructure. This option allows for cost efficiency with pay-as-you-go models and access to a variety of AI services.
• Colocation, or the practice of renting space within a third-party data center to house privately-owned servers and networking equipment with data, applications, AI services, and infrastructure all in one place—minimizes transfer times and leads to lower latency, which is crucial for real-time and high-performance AI applications. Colocation also provides cost savings because centralized infrastructure is more efficient to manage, maintain, and scale, reducing overall operational costs.
• On-premises deployment refers to hosting and running applications and infrastructure within an organization’s physical premises or datacenters. While this deployment option gives organizations more control over their IT infrastructure, it can mean high upfront costs for hardware, ongoing maintenance responsibilities, and limited scalability.

Prepare your data estate to smooth the path from proof-of-concept (POC) to production

Data is the fuel that powers AI technology, so planning for any successful AI implementation requires that you identify the right data sources and ensure that the data is complete, of high quality, in the right format, and representative of your target customers and business objectives.

“Organizations continue to see their data as their competitive advantage,” says Wangui McKelvey. “Unlocking insights from their data, across their organization, in a single, integrated platform, empowers businesses to take advantage of AI. If you don’t understand the insights you can deliver from your data with AI, then you’re going to be left behind.”

Consider what to build vs. what to buy

The decisions you make today to buy, build, or modernize your technology infrastructure can significantly influence your ability to execute on future goals, so it’s essential to choose a path that is supportive of your organization’s vision. Here are some of the considerations and trade-offs:

• Build: A “build” strategy enables you to tailor solutions precisely to your needs and implement your strategic business vision. Custom-built AI applications give organizations greater flexibility and control over the development process, facilitating scalability to accommodate future growth and evolving business needs.
• Modernize: In some scenarios, existing AI applications may still deliver value, but need an update to meet current demands. This approach can be beneficial when you have substantial investments in legacy AI infrastructure, but technology has evolved and your applications need to catch up.
• Buy: Some businesses buy prebuilt AI solutions, which may be a viable option if your AI use cases align with existing products. However, prebuilt AI solutions offer limited customization, reducing the ability to support more specific business needs. Other important considerations include integration complexities with existing IT systems, scalability limitations, and cumulative long-term costs, including licensing fees and upgrades.

Determine a strategy for regulatory compliance and safeguarding your AI assets

Regulatory compliance, particularly with the General Data Protection Requirement (GDPR) and the coming EU AI Act, introduces a set of technical, legal, and operational considerations for infrastructure choices.¹ Factors such as data sensitivity, data residency, scalability, and governance all play a part in architecture decisions, whether organizations choose on-premises, cloud, or co-located deployments. One critical factor to consider is access to sophisticated security measures that utilize machine learning, AI, and global threat intelligence databases to contribute to a more robust defense against cyber threats.

“Contrary to common belief, on-premises environments are often less secure than the cloud,” says Omar Khan. “Despite the sense of control associated with in-house infrastructure, cloud-based security solutions have evolved significantly, offering more advanced tools and technologies for threat detection and mitigation.”

Next steps

Stay tuned for the next post in our series, “Building a Foundation for AI Success: AI strategy and experience,” in which we will explore the factors that contribute to a successful AI strategy and experience for customers. We will follow the next entry with dedicated posts focusing on organization and culture, and AI governance.

Download a copy of “Building a Foundation for AI Success: A Leader’s Guide.”

¹EU AI Act.

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Business strategy is the first step toward AI success

AI is applicable to so many different use cases, from content generation to code generation to prediction to summarizing vast amounts of data. But what makes AI valuable is the impact it can have on business goals.

In this post, we’ll focus on business strategy—the first of five categories that support the ability to deliver meaningful, sustainable, and responsible value creation with AI. Subsequent posts in this series will cover best practices for the remaining categories: data and technology strategy, AI strategy and experience, organization and culture, and AI governance.

Building a Foundation for AI Success

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Five steps to building a successful business strategy for AI

AI has tremendous potential to transform multiple business functions, from marketing to product development to customer service to operations. But, like any consequential technology, it needs to support business objectives to drive meaningful business value.

In our conversations with customers, partners, and external and internal experts, we identified five steps that can help you develop a strategy for AI that will help you meet your goals.

1. Define and prioritize business needs

Successful AI projects begin with a clear, prioritized—and most importantly, valued—set of business needs. “We’ve only just begun to understand the potential for AI business transformation across organizations,” said Alysa Taylor, Corporate Vice President of Azure & Industry Marketing at Microsoft. “While customer use case adoption of AI varies by industry, we are seeing clear momentum around core business opportunities like employee experience, customer engagement, and internal business processes, as well as a focus on areas where AI can help bend the curve on innovation.”

Starting with the business need is crucial because it helps pinpoint the use cases that are best equipped to drive meaningful impact and garner executive visibility, support, and, critically, resources. This can help you avoid “perpetual proof-of-concept” and scale the initiatives with the greatest potential to become a force multiplier for your organization.

2. Identify AI use cases that support business objectives

Once your business needs are clear, it’s time to identify the use cases best suited to meeting your needs. Some of the top use cases we’re seeing for generative AI include:

Of course, the best use cases are ones that deliver value in multiple ways. Conversational search, for example, is a great time-saver but also improves customer experience, while call summarization can help front-line employees and surface issues or opportunities that can lead to product or service improvements, or even new features and products.

3. Establish a set of criteria you’ll use to prioritize use cases

The next step is to establish a set of criteria that you will use to evaluate use cases with the highest likelihood of success. It’s critical to engage a diverse group of stakeholders and teams spanning multiple areas of expertise within your organization. These insights can help to identify use cases from different perspectives and inform the potential impact of each one, so you have the broadest possible view of success from stakeholders across the business.

Following are five criteria to consider. Implementing these can be as simple as a discussion or as rigorous as a scorecard that you use at the beginning of each project.

• Business impact. Is this project set up to deliver powerful, demonstrable results?
• Feasibility. Does your organization have the data, expertise, and budget needed to support success?
• Time to implement. Is this a quick win or a longer-term investment? Starting with quick wins can build organizational confidence, while investing in longer-term projects can yield compounding returns over time.
• Ability to measure. Do you have the tools necessary to measure the impact of this initiative? Does it support established and valued key performance indicators (KPIs)?
• Risk. What are the risks, if any, associated with this initiative? What is the risk if you don’t pursue it?

Whatever process you choose, establishing a set of prioritization criteria will help build organizational alignment and confidence over time.

4. Determine how you will measure the value of AI initiatives

The ability to measure outcomes is one of the key criteria to consider as you prioritize AI use cases, as clear KPIs are critical for driving momentum and success for any technology project—and AI is no exception. Try starting with the discrete impacts of specific AI initiatives. Here are just a few of the ways our customers have measured the value of their AI initiatives:

• Productivity. Faster and more accurate transcriptions, document processing, market summaries, and content generation, reducing manual workloads and repetitive tasks.
• Personalization. Personalizing the customer experience—higher average order value (AOV) and customer lifetime value (CLV), increased customer satisfaction (CSAT) and net promoter scores (NPS), improved customer retention and conversion rates.
• Efficiency. Forecasting demand more precisely, automating inventory management and predictions, pre-empting production line disruptions, identifying potential safety vulnerabilities, optimizing resource allocation, and synchronizing supply chains.

Ultimately, by quantifying the value of AI initiatives tied to business goals, you can build a culture of data-informed decision-making and ensure that AI becomes a strategic asset rather than a disconnected technology experiment.

5. Build a portfolio management plan to help guide your investments

Finally, one of the biggest drivers of success is a portfolio management plan that helps to guide investments in AI.

In “Quick Answer: What Is the True Return on AI Investment?”, Gartner stated: “Enterprises do not achieve maximum leverage from artificial intelligence investments, despite increased spending. Executive leaders must become keen and discerning creators of AI investment strategies in order to obtain optimum value from AI initiatives,” and that “the best return yield from AI investment will come from an extensive portfolio of AI, guided by an expansive and evolving investment thesis that is aligned to strategic priorities and helps to allocate resources based on business impact. Organizations that follow a portfolio management plan to determine most AI use cases are 2.4 times more likely to reach ‘mature’ levels of AI implementation.”¹

Like a personal investment plan, an effective portfolio management plan sets up clear criteria for evaluating the success of individual projects, enabling you to identify which projects are delivering the expected value and which require adjustment or reallocation of resources. It also supports effective risk management. By carefully planning and prioritizing your AI use cases within a portfolio, you can diversify your AI initiatives, mitigate the risk associated with any single project’s failure or underperformance, and optimize resource allocation over time.

Next steps

Stay tuned for the next post in our series: “Building a foundation for AI success: Technology and data strategy,” in which we will explore the factors that contribute to a successful AI technology strategy and infrastructure. We will follow the next entry with dedicated posts focusing on AI strategy and experience, organization and culture, and AI governance.

Download a copy of “Building a Foundation for AI Success: A Leader’s Guide.”

To read more from Alysa Taylor about how AI is transforming work for every individual, every business and every industry, read Shaping the future with the cloud built for the era of AI.

¹Gartner. Quick Answer: What is the True Return on AI Investment? By Ethan Cohen, Afraz Jaffri, Published April 26, 2023. (gartner.com). GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.

The post Building a foundation for AI success: Business strategy appeared first on The Microsoft Cloud Blog.

In our conversations with customers, the questions we most often hear are: What should an AI strategy look like? What are the best practices? How do we create the most impact?

To begin to answer these questions, we developed “Building a Foundation for AI Success: A Leader’s Guide” as a framework to share what we are learning and hearing about the emerging best practices for driving business value with AI. It is drawn from conversations with customers, partners, analysts, AI leaders inside and outside of the company, and published research, as well as from our own experience.

We are sharing it as a resource to help inform your own AI strategy, whether you are just beginning to consider AI, are testing and deploying, or are well along the path.

The pillars of AI success

Building a Foundation for AI Success: A Leader’s Guide

Download a copy

How to accelerate your company’s success with AI

“The very first step of the journey is not even technical. It’s to establish a great partnership with the business. The number one goal is to deliver value to the company and to our customers.”

–Andy Markus, AT&T Chief Data Officer

“Building a Foundation for AI Success: A Leader’s Guide” lays out the five categories that, collectively, support the ability to deliver meaningful, sustainable, and responsible value creation with AI. While there is no one answer for all organizations, we’re starting to see best practices emerge across five discrete categories. They are:

1. Business strategy
2. Technology strategy
3. AI strategy and experience
4. Organization and culture
5. AI governance

This series will explore each of these topics in depth. In the meantime, here are a few highlights.

Business strategy

The consensus—from conversations with customers as well as other external experts—is that a clear set of business objectives for AI, with prioritized use cases and key performance indicators (KPIs), is closely correlated with success. An investment strategy for AI is also key, as it as it entails a structured process by which the organization assesses the business impact of AI against strategic priorities, uses those findings to inform investment decisions, and enables the organization to build a common understanding of how AI accrues to business value. According to Gartner, “Organizations that follow a portfolio management plan to determine most AI use cases are 2.4 times more likely to reach ‘mature’ levels of AI implementation.”¹

Technology strategy

From a technology perspective, the top priority is an AI-ready application and data platform architecture that will meet your organization’s requirements. It’s also crucial to align parameters for build versus buy decisions, as well as plans for where to host data and applications, to optimize outcomes.

AI strategy and experience

While previous experience in building, testing, and realizing AI value across multiple business units, use cases, and dimensions is extremely valuable, other elements of AI strategy are important to consider as well. Customer-centricity, and taking a systematic approach to AI, are both emerging as key contributors to AI success. The 2023 Gartner® report Survey Analysis: AI-First Strategy Leads to Increasing Returns found that “41 percent of mature AI organizations use customer success-related business metrics.”¹

Organization and culture

Organization and culture are also widely agreed to be significant success factors for AI. From an organizational perspective, having a clear operating model can make the difference between AI initiatives that are viewed as science experiments and those that are understood to be value drivers. From a culture perspective, we have also observed—and been told by customers and other experts—that embracing a culture of change management is key to building organizational capacity for AI.

AI governance

As with any consequential new technology, AI must be built on a foundation of security, risk management, and trust. As a result, organizations seeking to reap the greatest benefit from AI must develop their understanding of the data governance, security, and responsible AI implications of their decisions and implement the processes, controls, and accountability structures needed to govern it.

The paper also includes a set of stages—from exploring potential to realizing value—that you can use to map your own progress against these pillars, as well as a “Getting Started” guide that includes suggested next steps.

Next steps

Stay tuned for the next post in our series: “Building a business strategy for AI,” in which we will explore the factors that contribute to a successful AI strategy and transformation plan. We will follow it with dedicated posts focusing on technology and infrastructure, AI strategy and experience, organization and culture, and AI governance.

Download a copy of “Building a Foundation for AI Success: A Leader’s Guide.”

¹Gartner, Quick Answer: What is the True Return on AI Investment? By Ethan Cohen, Afraz Jaffri, Published April 26, 2023. (gartner.com). Survey_Analysis_ An__795644_ndx.pdf.
GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.

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