When our teams face new challenges and are encouraged to try new things, they’re more likely to find new and creative solutions. It all depends on workplace culture. Over the years, we’ve helped thousands of organizations adopt new technology, and time after time we discover the same truth: culture is the engine of innovation.

So, what happens when an industry built on tradition and precedent finds itself at the crossroads of new challenges and established ways of working?

Recent surveys show 62% of legal professionals now report spending up to seven hours a week tracking and analyzing regulatory developments, and the overwhelming majority (73%) anticipate this surge in regulatory activity to continue.¹ 

Like many companies, Microsoft is exploring how AI can help our legal teams more efficiently handle these new workloads at scale and deliver impact. 

To recap our journey so far, Chief Legal Officer and Corporate Vice President, Hossein Nowbar and Corporate Vice President Jared Spataro recently discussed how our Corporate, External, and Legal Affairs (CELA) organization is integrating AI into their workloads to stay ahead of the curve. They emphasized the importance of strategy, data, and culture. By reimagining the way the team works, they are harnessing the power of AI to bring significant enhancements to the legal field, including better services, smoother operations, and more time for essential tasks.

Wherever you are on your AI journey, we’re excited to partner with you—whether you’re just dipping your toes into AI exploration or looking to build on your current achievements. If you’re facing the same regulatory challenges as many of our customers and are curious how AI can help, I encourage you to watch this interview.

Strategy in the era of AI

Guided by data and collective contributions, CELA defined a strategy and launched a departmental initiative to harness the power of AI focusing on three key areas of broad impact: enhance advisory services, streamline transactional processes, and strengthen compliance and risk management. A multifunctional team led by Hossein and his co-executive sponsor, Chief Data Scientist, Juan Lavista Ferres, was assembled to execute on a strategy focused on experimentation, technical development, cultural change initiatives, adoption, and beyond. 

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Better data means better AI

When asked about the biggest learnings from his AI journey, Hossein emphasized the importance of data and sharing it more broadly across the organization. To harness its full potential, AI needs access to vast amounts of high-quality data. However, gathering this data can be challenging, especially if it’s siloed across the organization with disparate storage structures and governance models. Navigating a legal technology ecosystem with various domain-specific applications requires consolidation and aggregation of data from those systems. 

To address this challenge, the team built a data factory. CELA’s data factory is the foundation for their AI infrastructure to centrally and securely manage their diverse data sets and ultimately power AI solutions. AI and Microsoft Copilot, coupled with data infrastructure investments, are helping CELA to effectively transform vast amounts of data into valuable insights to accelerate AI transformation while also managing data privacy, security, and governance. 

It’s not just about tech—it’s about people

The success of an AI initiative depends not just on the technology but also on how people within the company adopt and use it.

When AI and Copilot was brought to our legal teams, some team members were uncertain about how this new technology would affect the way they worked.

One of the first tasks was to help teams understand the tangible benefits of AI and Copilot through proactive experimentation and invite them to imagine what they could use it to do. CELA embarked on a range of experiments to inform opportunity analysis and bring about measurable improvements in efficiency, quality, and scale. 

As the customer zero for all our products, we understand how it’s one thing for people to learn how to use a new tool, but it’s another to get them genuinely excited enough to truly adopt it. 

As Hossein explains in the interview, to navigate this cultural shift, the team implemented a multifaceted approach to model, recognize, and incentivize innovation and experimentation.

To support this, the team established a communication and change management plan. A community of AI catalysts representing all practice groups was formed to help drive AI transformation. They also introduced recognition opportunities to celebrate team members who contributed innovative ideas.

You have to celebrate people who are adopting AI and showcase their efforts

Hossein Nowbar, Corporate Vice President and Chief Legal Officer, Microsoft

By encouraging teams to join in the innovation and showing them real examples of what AI can do, it helped ease uncertainties and fostered a culture that embraces AI integration in their work. 

Upskilling empowers employees to adapt to AI transformation

Closing the gap in skills and knowledge is crucial for making the most of advanced AI tools. When companies invest in solid training programs and online resources, they’re not just teaching their employees how to use new tools—they’re showing they care about their team’s growth and future.

In our own journey, CELA established training programs to help teams adapt to new tools. For example, with support from Microsoft, the CELA Academy hosted the CELA Copilot skilling series, resulting in a 50% increase in employees using Copilot experiences in Microsoft Teams and Microsoft 365. 

Whether you’re getting started with Copilot or building custom AI solutions, implementing effective training isn’t something you have to do alone, either. The Microsoft AI learning hub helps organizations prepare for AI transformation with the Microsoft Cloud and offers tools for building AI-powered apps, generative AI solutions, bots, and other AI models. 

You can also discover more insights from our AI journey, along with advice from experts in the field in Building a Foundation for AI Success: A Leader’s Guide. This guide can help your organization start using AI, inspiring your team to innovate and get excited.

AI tools like Copilot will help you save time on legal tasks

Using AI tools like Microsoft Copilot for certain legal tasks not only speeds things up but also enhances work product quality, increases agility to support business velocity, and facilitates decision making. Employees get more time to focus on the work that matters. The team is leveraging Copilot and AI to deliver broad impact across CELA by prioritizing use cases, such as:

• Create efficiencies for regulatory work: Quickly summarize regulations, streamline analysis, gather research to stay up to date on industry news and legal developments, draft guidance, and provide actionable insights, empowering legal professionals to stay ahead of the curve. 
• Strengthen compliance and risk management: Analyze large data sets, help proactively spot possible compliance issues, respond to requests for information, and enable agile and efficient action. 
• Improve client Interactions: Redirect high volume low-risk inquiries to client self-service capabilities to deliver faster responses. 
• Enhance advisory services: Quickly find relevant information across sources, including outside counsel work product, to facilitate rapid decision making, and draft communications tailored for different audiences verifying key advisory points are clear and relevant. 
• Simplify transactions: Condense intricate agreements, pinpoint essential clauses, flag potential risks, compare contracts, compile insights, draft clauses, and research legal structures for increased velocity and better decision-making. 
• Support pro bono: Impact the world around us by creating efficiencies to enable pro bono volunteers to help more clients in need in less time by using AI to empower volunteers with information they need and automate tasks such as form completion.

Join Microsoft on our AI journey

AI transformation continues at a fast pace. Every day, we are leveraging Copilot and AI, discovering the benefits and new use case scenarios. 

Making legal departments more efficient with AI

Learn from our journey

In the end, the right culture is key. By fostering a culture that values new ways of working, companies can set the stage for a successful AI transformation. Forward-thinking leaders, like Hossein Nowbar, play a pivotal role in this success by actively supporting AI initiatives, boosting their teams’ and partners’ confidence, and modeling the change mindset needed to unlock AI’s true transformative value. 

Adoption of AI is not a luxury for legal departments; it’s a necessity. It can never replace human judgment, but it can help us do our work better and faster.

Hossein Nowbar, Corporate Vice President and Chief Legal Officer, Microsoft

This interview is the first part in our All in on AI series that explores how Microsoft is adopting AI across our business. Next, Kathleen Hogan, Microsoft Chief People Officer, sits down with Jared Spataro to see how AI is helping human resources (HR) teams do more with less and to share the transformative best practices she used to drive some of the highest, fastest AI adoption rates Microsoft has ever seen.

¹Cost of compliance 2023 report examines the top three operational insights, Thomson Reuters.

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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.

The post The AI Strategy Roadmap: Navigating the stages of value creation appeared first on The Microsoft Cloud Blog.

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.

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

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.

The post Building a foundation for AI success: Organization and culture appeared first on The Microsoft Cloud Blog.

Advances in AI, cloud, and other emerging technologies are empowering organizations to pursue new opportunities for growth and innovation. Read on to learn how skill building with Microsoft Learn is a critical element of your AI transformation strategy.

AI’s ubiquity across organizations means that business and technology leaders like you need to approach AI skill building in a different way. We know that you’re not only facing the challenge of keeping up with the rapid pace of technology, but you’re also grappling with a shortage of skilled workers.

It’s clear there is a pressing need for AI skilling. To help address the skills gap, this past year, Microsoft engaged more than 6 million people globally in learning activities with ambitions to skill everyone to use our AI technology.

It’s your AI learning journey

Today, it’s more important than ever to have the right skills so you can accelerate AI implementation at scale. While Microsoft’s AI apps and services empower you to innovate and accomplish more than you thought possible, we recognize it can be challenging to know where to begin.

We’re on this journey at Microsoft as well. We’re customer zero of our own products, exploring how to use AI to drive growth, unlock efficiency, and reduce operating costs. As we upskill ourselves in AI and understand how we can best use it in our day-to-day, we’re also exploring how to leverage AI to improve the learning experiences we offer.

Together with our customers and partners developing new AI skills, we’ve gathered key insights about what is necessary to nurture AI competency. We’ve designed a simple framework to help our customers chart their own course for building the necessary AI skills to realize the value of the Microsoft platform. Learn more with the Accelerate AI transformation with skill building position paper.

Accelerate AI transformation with skill building

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Each organization has its own unique AI learning journey. Your goals and needs, and those of your team members, may vary depending on how much you know about AI and how you want to use it. It’s your AI learning journey.

• Understanding AI means getting to know the basics of AI, such as definitions and key terms, responsible AI, and how it benefits individuals and organizations.
• Preparing for AI is about acquiring the skills to successfully get your organization ready to later use Microsoft Copilot and build AI-powered apps.
• Using AI means understanding Copilot and getting the skills to write effective prompts and get the best out of generative AI, so you can use Copilot successfully.
• Building AI solutions is about learning skills to embed Al in apps using Microsoft Azure Al Services and skills to build your own copilots experiences with Microsoft Copilot Studio.

Microsoft Learn is your AI skill-building partner

No matter where you are on your AI learning journey, Microsoft Learn meets you there. Whether you’re just beginning to understand what AI is and how it might be beneficial to your organization, or you’re ready to use Copilot and productivity-enhancing AI, or you’re looking to build bespoke AI-powered solutions, we can help you reach your goals.

By offering comprehensive curated resources, tools, and guidance, Microsoft Learn supports you and your team as you build the skills necessary to execute new AI innovation projects and achieve your business objectives.

Here are some learning tools and resources to explore.

The AI learning hub

Begin with the AI learning hub, the go-to resource. In this hub, business leaders, business users, and technology professionals can find everything they need to gain AI skills, in a single place. They can explore training by role or by technology, options to learn with the support of the community or Microsoft Training Services Partners, and recommendations from our team on new topics to deep dive into.

AI learning hub

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Verifiable AI skills, with Microsoft Credentials

Take your team’s AI skills to the next level with Microsoft Credentials, which include Microsoft Certifications and Microsoft Applied Skills. Certifications offer the flexibility to grow the skills needed for critical roles, and Applied Skills offer the agility to expand the skills needed for key business scenarios. Together they bring verifiable skillsets aligned to AI job roles and AI projects, ensuring you’re building resilient and adaptable teams ready to take on new opportunities.

Explore Microsoft Credentials for AI and find role-based certifications like Azure AI Engineer or Azure Data Scientist, or scenario-based Applied Skills including developing generative AI solutions, training and deploying machine learning models, or creating analytics solutions with Microsoft Fabric.

Start your AI learning journey now

However you’re looking to advance your organization, Microsoft Learn is the trusted source to help you get skilled up and ready to power AI transformation with the Microsoft Cloud.

Explore our Microsoft Learn Blog, follow us on X and LinkedIn, and get subscribed to “The Spark,” our Microsoft Learn LinkedIn newsletter, to stay updated on what’s new in AI and cloud skills.

The post Transform your business with AI skill building on Microsoft Learn appeared first on The Microsoft Cloud Blog.

We’re already seeing how Microsoft Copilot can help turn “I can’t” into “watch me” for people around the world in their everyday lives. But for business leaders, Microsoft Copilot is just the start—there are so many additional ways you can bring this transformative technology into your organization. Understanding your options—and the business scenarios best suited for each—is critical. Read on to learn how you can approach making these decisions for your organization.

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Establish your business objectives

Before you think about how you’ll deploy AI solutions, you have to start with why. Aligning your AI investments with a clear business strategy is imperative, as my colleague Susan Etlinger explores in this blog post.

With specific goals in mind, you can prioritize use cases based on their potential for impact and scope your solution based on data and infrastructure requirements. This will help you make decisions aligned with your business objectives and drive your company forward.

Once you’ve identified your why, there are a few common pathways we’re seeing for how to apply AI to reach those goals—from buying off-the-shelf software as a service (SaaS) solutions to building a custom solution that meets your specific needs. Let’s explore the options.

Use Microsoft Copilot to boost employee productivity

If your priority is employee productivity across your business, Microsoft Copilot should be one of the first solutions you consider. If you’re already using Microsoft Entra for identity management, your employees can sign in using their work or school account and get commercial data protection for free—which means chat data isn’t saved, Microsoft has no eyes-on access, and your data isn’t used to train the models.

Copilot helps your people to get the answers and time-saving assistance they need from powerful AI models without putting your company data at risk. But that’s just the start.

Copilot for Microsoft 365 is our best Copilot experience for organizations. It gives you priority access to the very latest models—starting with OpenAI’s GPT-4 Turbo. You get Copilot in Word, Excel, PowerPoint, Outlook, OneNote, and Microsoft Teams—combined with your universe of data in the Microsoft Graph, including data you bring in from external sources through Microsoft Graph connectors. Copilot for Microsoft 365 has enterprise-grade data protection, which means it inherits your existing Microsoft 365 security, privacy, identity, and compliance policies. It also includes Copilot Studio to customize Copilot for Microsoft 365 and build standalone copilots—more on this in a minute.

Built on Microsoft’s comprehensive approach to security, compliance, privacy, and responsible AI, Copilot for Microsoft 365 is designed to be enterprise-ready, helping employees in every part of your business unlock productivity and unleash creativity.

And with Copilot for Sales, Copilot for Service, and Copilot for Finance, we’ve added role-specific workflow automation, guided actions, and content generation to the applications professionals use the most. With built-in integration across business systems—whether Dynamics 365 or a third-party customer relationship management (CRM), enterprise resource planning (ERP), or contact center applications—sellers, service agents, and financial professionals can be more effective and efficient as they work, guided by Copilot. 

When you’re thinking about boosting employee productivity in common business scenarios, an out-of-the-box solution like these can deliver incredible impact. But a solution like this may not be the right fit—so let’s look at the next pathway.

Customize Microsoft Copilot to serve the unique needs of your business

No two organizations are alike, and neither are the apps, data, and workflows that drive your business. Microsoft Copilot can help boost employee productivity in the usual functions of every business, but you may also need solutions customized for your own business processes. With Microsoft Copilot Studio, a low-code tool, you can tailor and extend Copilot for Microsoft 365 or build standalone copilots specific to your needs.

For example, within Copilot for Microsoft 365, you may be looking to add specific plugins to address nuanced topics like legal, finance, or human resources (HR). You may want to create and call new workflows within Copilot or connect Copilot with data that lives outside the Microsoft Graph. These customization capabilities are included with Copilot for Microsoft 365.

Create your own copilots to deliver transformational experiences

Now let’s take this a step further. Instead of building on the foundation of Microsoft Copilot, you may be looking to bring copilot experiences into your own applications with other data sources. The new copilot category is not limited to internal productivity and employee-facing applications, but can extend into external, customer-facing experiences that differentiate your business and drive growth.

With your customized copilot solution, you can bring generative AI to your own unique business processes like supply chain management, manufacturing line operations, or quality control. You can engage customers and users with more personalized experiences and recommendations. The potential for new innovation is limited only by your imagination—and with Microsoft’s range of development tools, you can build generative AI-powered experiences exactly where you want them.

To quickly and securely build your own copilots, you can start in a low-code environment with Copilot Studio, going beyond the extensibility capabilities we already covered. With Azure OpenAI Service behind the scenes, Copilot Studio is a fully managed, hosted SaaS service, with built-in analytics as well as security and governance controls. You also maintain control over dialog management and conversational orchestration, and you can deploy custom copilots built with Copilot Studio to many channels across web, apps, social channels, and Microsoft Teams.

You can also take your custom copilot a step further with a pro-code approach giving developers full control in an end-to-end application development platform—Azure AI Studio.

Azure AI Studio is a generative AI environment for developing intelligent applications from end-to-end, including custom copilots. For skilled development teams that need to benchmark models, mix and match models, fine tune, evaluate, and continuously monitor their solutions, this is the place. Azure AI Studio enables developers to build new AI applications or augment existing apps with AI capabilities. Developers can identify the best models for a custom copilot, create multimodal capabilities beyond text alone, design and evaluate prompts, build extensions with custom AI search, mitigate risk with robust content safety tools, deploy at scale, and continue to monitor applications in production.

Use the right tool—or tools—for the job

Though we’ve outlined a few different options for how you can use AI to help reach your business goals, these pathways are by no means mutually exclusive—you can and should use more than one approach to meet your needs. And with the Microsoft Cloud, you can easily work across approaches, and shift gears as your needs change.

You can start with Copilot for Microsoft 365 and use Copilot Studio to quickly build a workflow that serves a specific business function, then as you uncover more advanced applications for that workflow, Copilot Studio also works with Azure AI Studio and additional Azure services. This connected, end-to-end AI toolchain allows your developers to use the right tool for the job at hand, spanning low-code and pro-code capabilities as their needs change.

And keep in mind that AI safety and responsibility should be top of mind from the very beginning. Make sure you’re considering the principles, corporate standards, tools, and governance that you’ll need to ensure your AI experiences are built on trust. The Responsible AI Standard is a great place to start.

Next steps

I hope you take the time to explore the breadth of Microsoft’s AI innovation, and dig deeper into the various AI approaches I’ve outlined here. Remember—no matter what your business priorities, or where you’re starting from, Microsoft is ready to help empower your AI transformation.

The post Build, buy, or both? How to choose the right approach for your AI transformation appeared first on The Microsoft Cloud Blog.

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.

Building a foundation for AI success

Identify how to accelerate your company’s success with AI

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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.

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

In every industry and around the world, over the past year we have seen organizations embrace generative AI to transform their businesses. From Canada to Finland to Hong Kong—and everywhere in between—leaders are seeing the potential and possibility made possible by this transformative technology, and they’re beginning to realize the benefits. According to IDC, for every $1 an organization invests in AI technology, they’re realizing an average return of $3.50 on that investment.¹

Looking ahead to 2024, the question facing business leaders is this: How will you seize the opportunity to lead in this new era of AI? Microsoft is continuing to innovate with AI so that we can empower every person and every organization on the planet to achieve more—and it starts when “I can’t” becomes “Watch me.” Take a look.

As your organization charts its journey into this era of AI, we are excited to be your partner—whether you’re just starting to explore AI or looking to build on what you’ve already achieved. To learn more about how the Microsoft Cloud can help power your transformation journey, I’d like to invite you to watch our 20-minute webinar.

AI transformation

Learn how your organization can unlock productivity, build your AI capability, and innovate with trust at the core

Watch the webinar

Unlock productivity across your business with Microsoft Copilot

Becoming an AI-powered organization starts with bringing AI right into the flow of work for any employee. And that’s what we’ve enabled with Microsoft Copilot.

Imagine a tool that makes your work so much more productive you don’t want to give it up. A tool that helps you sell more effectively, code more efficiently, respond to customers more accurately—and so much more.

“Copilot use cases are growing. In Microsoft 365, easily getting data from Excel files, creating a first draft in Word, or creating a PowerPoint presentation are some of the top examples,” said Sarah Lewandowski, Global Technology & Innovation Lead at Bayer.

We are seeing incredible results from early adopters of Microsoft Copilot. Our recent Work Trends Index study found that 70% of Copilot users said they were more productive, and 68% said it improved the quality of their work. And in fact, 77% of people using Microsoft Copilot said once they used it, they didn’t want to give it up.

Identifying how Microsoft Copilot can unlock productivity across business roles and functions should be a key element of your AI transformation.

Build unique, transformational experiences to suit your needs

Deploying generative AI solutions like Microsoft Copilot is one part of your AI transformation journey. But you should also be thinking about the ways generative AI can support your business in a more tailored way.

This may mean extending Microsoft Copilot with Copilot Studio, as we are seeing at organizations like dentsu. The creative agency’s IT team are working on how to create customized prompts for key business functions or roles, as well as how they can connect Copilot with the in-house or third-party apps that already support their business.

“I have a lot of ideas, including using it for our onboarding employee experience with prompts that could help new employees find content. HR or Finance’s systems could have custom prompts in those applications,” says Julie Duvillier, Head of Unified Communications Platforms for Global Technology, at dentsu.

But you can also explore creating your own copilot solution that leverages the full power of Azure AI, the same platform that powers Microsoft Copilot. KPMG Australia, for example, created its own conversational AI assistant called KymChat, which leverages not just the generative AI capabilities of Microsoft Azure OpenAI Service but also the vector search capabilities of Azure Cosmos DB to help the solution deliver faster results at scale.

“Let’s say an employee is about to interview a candidate for an open senior tax advisor role, and they want to know what questions to ask,” says Robert Finlayson: Senior Product Manager at KPMG Australia. “In the past, this employee might have had to liaise with HR or conduct a time-consuming web search of multiple sources to obtain viable questions. Now they simply ask KymChat to create a list of 10 role-specific interview questions, and they have their interview prep finished in seconds.”

Scenarios like this, which are highly unique to your own organization, are crying out for a custom copilot that takes advantage of the full Azure AI portfolio. You can develop these solutions in-house, or work with a Microsoft partner bringing their own expertise to the table.

Rely on a trusted, responsible AI partner

AI innovation is nothing without trust. Trust must be built into the AI apps you deploy and the AI solutions you create from the very beginning—and that’s why Microsoft commitment to Responsible AI is so important.

Our approach to Responsible AI is grounded in a set of principles that guide everything we do. These principles are brought to life through our corporate standards, the implementation and tooling we create, and the oversight and governance we have in place.

We’ve also seen the new importance of safeguarding your business and your data in this era of AI—especially as security becomes a defining challenge of our time. Microsoft’s approach to security can level the playing field for defenders, helping you protect your organization with AI-powered, end-to-end security.  

“One of the benefits of being able to use Copilot through Microsoft is that security’s already built in. And so the logged in user only has access to things they have access to, through the bigger security enterprise that you have set up, and it follows all your policies in your Microsoft landscape,” said Coşkun Çavuşoğlu, EY Americas Financial Services Tax AI Go-to-Market Leader.

Shape the next step of your AI journey today

Wherever you are in your AI journey, whether you’re just getting started or pushing the edge, you can take that next step today. Your AI journey should be multifaceted, encompassing the AI-powered apps you can use to unlock productivity across your business, the AI solutions you’ll build to serve your unique business needs, and a trusted and responsible foundation for AI innovation.

To dig into some real-world examples that can help you visualize your own AI success, check out the 20-minute webinar.

If you’re interested in learning more about Microsoft Copilot or building your own AI capability with Microsoft Azure AI, check out Microsoft AI for the latest news and information.

¹SOURCE: IDC InfoBrief, sponsored by Microsoft, The Business Opportunity of AI, IDC #US51364223, November 2023.   

The post The AI capabilities you need to power your AI transformation appeared first on The Microsoft Cloud Blog.

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

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

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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.

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