Enterprise AI Strategy: Creating a successful AI strategy for your forward-looking business

Excitement for enterprise AI runs high, but converting these innovations into tangible business outcomes is not without its challenges. One recent report found over 30% of generative AI projects are abandoned after the proof-of-concept, while another RAND report found that AI projects can have double the failure rate relative to non-AI information technology projects.

A part of the reason for these failures could be that organizations are jumping headfirst into AI investments without a clear strategy. This lack of an AI strategy can cost your company resources, undermine productivity, erode employee confidence, and fail to deliver meaningful business value. 

Successfully developing an enterprise AI strategy is critical in delivering efficiency gains and enabling informed decisions that drive real business impact. So how can you integrate an enterprise AI strategy into your business before you’re left behind? 

In this article, we’ll answer the question “What is an AI strategy?”, before talking about why enterprises need an AI strategy in the first place, and how to seamlessly create and launch an AI strategy for your enterprise. 

What goes into an AI strategy?

An AI strategy is a structured plan that outlines how an organization will leverage artificial intelligence to achieve specific goals or gain a competitive advantage. An enterprise AI strategy involves identifying priorities, allocating resources, and establishing frameworks for the development, deployment, and governance of AI technologies. A well-defined AI strategy aligns AI initiatives with broader organizational objectives while addressing ethical, technical, and operational considerations. 

Your AI strategy should establish distinct guidelines for:

• Technology adoption and integration
• Ethical considerations and governance
• Skill development
• Organizational readiness
• Specific use cases and implementation approaches

Keep in mind that creating an enterprise AI strategy involves multiple stakeholders, including:

• Executive leadership
• IT departments
• Data scientists
• Human resources
• Department heads from key business units

Remember that the goal of an AI strategy is to ensure that AI investments are aligned with your business goals and are capable of delivering measurable value. By gathering input from across the organization, you can better ensure that your AI solutions support the needs of every department.

Why do enterprises need an AI strategy?

A well-defined AI strategy helps you:

• Align AI initiatives with business goals
• Prioritize AI projects for maximum impact
• Allocate resources effectively
• Manage risks and ethical considerations

Despite these benefits, Asana reports that only 31% of enterprises have an AI strategy in place. To justify the significant investment, you need a coherent strategy so AI adoption drives real value for your organization. 

So let’s talk about the “why,” and then we’ll outline the “how.”  

Ensure AI initiatives support the overarching business strategy

To drive real value, your AI initiatives must be tightly integrated with your core business processes.

So, where do you start?

First, identify key business objectives. Clarify your goals, the areas in which AI can create the most impact, and how AI will support growth and give you a competitive advantage.

Second, develop a process for piloting AI use cases using the following four steps:

1. Select potential projects

2. Run small-scale tests

3. Measure results against KPIs

4. Refine and scale successful initiatives

This approach helps you gauge impact before full implementation.

Also, be wary of AI projects that don't align with your core strategy. These often fail to scale and provide limited value. Instead, focus on AI initiatives that directly contribute to business objectives and consider how AI can enhance areas like product development, customer experience, operational efficiency, and decision-making processes.

Bottom line: Effective enterprise AI strategies don't just implement technology—they transform how your business operates. Continuously evaluate and adjust your approach to ensure AI remains aligned with your business needs.

Establish an AI governance framework for a responsible AI strategy

To responsibly and ethically leverage AI, businesses must pay close attention to their data sources, make sure their AI models are accurate and unbiased, and be clear about where their data comes from.

However, PwC’s 2024 US Responsible AI Survey found that only 11% of executives had completely applied responsible AI policies. The problem here is that failing to adopt an ethical and responsible approach can not only increase organizational risk, but in some cases could even potentially lead to lawsuits. 

Therefore, adopting a solid AI governance framework or selecting an AI provider with strong security and privacy measures should be a top priority for your organization. This framework will guide your AI development, deployment, and usage while ensuring alignment with your company's values.

Here’s where to start:

1. Define clear principles for responsible AI. Address key ethical concerns such as data privacy, security, and potential bias in AI systems. Consider how your AI initiatives impact employees, customers, and society at large.

2. Develop comprehensive policies. Outline data governance standards, security protocols, transparency requirements, and bias mitigation strategies.

3. Establish a dedicated ethics committee to oversee AI projects. This group should include diverse perspectives to address various ethical considerations.

4. Implement rigorous testing processes for your AI systems. Regular assessments help identify potential issues early and ensure ongoing compliance with ethical AI guidelines.

5. Train your teams on AI ethics and your governance framework. Encourage a culture of responsibility and open dialogue about ethical concerns.

6. Consider legal requirements, especially if operating internationally. Data protection laws and regulatory standards vary across regions, so make sure your framework accounts for these differences.

By prioritizing ethical considerations in your AI governance, you'll build trust with stakeholders, mitigate risks associated with AI deployment, and position your enterprise as a responsible leader in the AI space.

Build a culture that embraces AI adoption

According to Forbes, “There is a direct correlation between using artificial intelligence and concerns about job security.” In fact, CFO Dive reports that 28% of workers fear AI will replace them. 

You can mitigate these concerns in your business by building a culture that welcomes the use of AI. We recommend five key steps to help you create an environment where your employees thrive alongside AI.

1. Educate your workforce on AI capabilities and benefits. Highlight how AI can enhance productivity and improve customer experiences. This knowledge helps alleviate fears about job displacement.

2. Encourage experimentation with AI tools across departments. Set up pilot projects to demonstrate tangible benefits in areas like marketing, customer service, and operational efficiency.

3. Invest in training programs to upskill employees. Focus on developing AI-complementary skills that boost human-AI collaboration. This approach helps staff feel valued and secure in their roles. You could also create a conversational AI strategy to help solve employee issues at work. 

4. Promote open communication about AI initiatives. Address concerns transparently and showcase how AI augments rather than replaces human work. Emphasize opportunities for career growth alongside, and complementary to, AI adoption.
   

5. Recognize and reward AI-driven achievements. Celebrate successes to reinforce the positive impact of AI adoption on individual and company performance.

Successful implementation of an AI strategy at scale starts with laying the right foundation. A supportive environment encourages employees to view AI as a powerful tool for growth rather than a threat.

How to create an enterprise AI strategy

Now, let’s move onto the “how.”

While every enterprise has unique challenges, goals, and capabilities, this framework can help you develop an effective enterprise AI strategy that minimizes risks and ensures a positive ROI.

Your existing business strategy should serve as your “north star” when creating an enterprise AI strategy. Before bringing AI into your organization, ensure that you have a firm grasp of your in your department and organization's core objectives and key performance indicators (KPIs),  and consider:

• What are our main business goals?
• Which specific areas need improvement?
• Where do our competitive advantages lie – and how might AI further enhance these?

By aligning your AI strategy with these important business elements and objectives, how you choose to apply your AI will be better positioned to achieve meaningful and impactful results

Top 6 tips for implementing an enterprise AI strategy

Once you’ve answered these questions, use your current business strategy to inform alignment, implementation and AI adoption:

1. Identify processes that could benefit from AI automation. Look for opportunities to leverage AI in decision-making, customer service, or product development. Also, consider your industry and market trends. How might AI disrupt your sector? What AI capabilities do your competitors possess?

2. Assess your current tech infrastructure and data assets. AI thrives on quality data, so evaluate your data collection and management practices.

3. Align AI initiatives with your company's mission and values. Ensure AI supports enterprise ambitions and drives stronger results. Think about where a new AI strategy might fit in with your overarching business goals.

4. Review your talent pool. Do you have the necessary skills in-house, or will you need to hire AI experts?

5. Examine your budget and resource allocation. AI projects often require significant resources. Prioritize initiatives that offer the highest potential return on investment.

Identify strategic AI use cases for your enterprise

With countless possibilities, pinpointing the right AI use cases for your business helps you avoid getting swept up in the hype of new technology

Start by examining your current processes. Where are the bottlenecks? Which tasks consume excessive time or resources? These pain points often signal prime opportunities for AI intervention.

Next, you’ll need to evaluate use cases based on business value, ease of use, data availability, and alignment with your goals.

Prioritize quick wins to build momentum. Focusing on 3-6 initial use cases that offer clear value and manageable implementation should allow you to demonstrate AI's potential without overwhelming your organization.

Consider these common high-impact enterprise AI applications:

• Automated customer support
• Predictive maintenance
• Fraud detection
• Supply chain optimization
• Personalized marketing

Remember, the most exciting AI solution isn't always the most impactful. So align your AI strategy with business objectives to ensure meaningful results.

Define how you'll measure the success of your AI strategy

You probably know the saying, “What gets measured gets managed.” This applies to investing in AI initiatives too. We’d argue it’s even more important here since, if implemented poorly, your AI strategy could end up decreasing business value rather than delivering it. Setting Key Performance Indicators (KPIs) for AI should be both direct and indirect.

For example, you may not feel the cost when using poor AI for a specific task. But poorly implemented enterprise AI can have exponentially bigger consequences across your organization. Therefore, establishing clear business value, and metrics that act as guardrails,is essential for evaluating your AI strategy. 

Start by defining and prioritizing business needs that AI can address. This will guide your measurement strategy. Consider using KPIs that revolve around these areas:

• Cost reduction
• Revenue growth
• Operational efficiency
• Customer satisfaction
• Employee productivity

You’ll also want to customize metrics to align with your specific AI use cases and organizational goals. What works for one company may not be relevant for another. Acacia divides AI success metrics into four categories:

1. Efficiency metrics

2. Performance metrics

3. Financial metrics

4. Accuracy metrics

Your next step is to set up real-time monitoring frameworks to track progress continuously. Forbes recommends asking the following questions as your AI initiatives take shape:

• Is AI helping us to make better decisions?
• What is the level of buy-in?
• Are we using AI to improve our customer experience and value?
• Is AI helping us make measurable improvements and progress toward our goals?
• Are we implementing AI projects on time and within budget?

Successful AI implementations often show improved decision-making speed, enhanced product or service quality, increased automation of routine tasks, better resource allocation, and more accurate predictions and forecasts.

Finally, regularly review and adjust success metrics—as your AI matures and business needs change, your measurement approach should adapt too.

Evaluate your data, tech stack and human resource readiness for a new AI initiative

Start by assessing your current IT infrastructure and team capabilities. Conduct surveys and interviews to uncover departmental bottlenecks and identify areas where a new AI strategy can improve your processes. This evaluation will reveal strengths and weaknesses in your existing tech stack and guide decision-making.

Consider these key factors in your assessment:

1. Data quality and accessibility

2. Computing power and storage capacity

3. Network bandwidth

4. Integration capabilities

5. Scalability potential

Ensure your data is clean, organized, and accessible. And remember that better input leads to more accurate AI outputs.

Next, evaluate your tech stack's readiness for AI integration. Outdated systems and insufficient computing power can hinder AI adoption. If your infrastructure lacks the necessary resources, consider cloud-based solutions for scalability and performance.

And third, assess your team's AI readiness and identify skill gaps through surveys and interviews. You may need to invest in training or hiring to build a well-rounded AI team. Look for expertise in:

• Data science
• Machine learning
• AI ethics
• Project management
• Domain-specific knowledge

Consider out-of-the-box enterprise tools, such as Moveworks, if you lack internal resources. These solutions often come pre-trained and can be quickly deployed, reducing strain on your IT team.

Assess and prepare for potential risks in your AI strategy

AI can help your business grow, but at the same time, it may leave you open to potential risks. A survey by KPMG found that 18% of Canadian AI users had entered sensitive company data into a prompt. So how do you better safeguard against potential risks?

Identify Potential AI Risks: Start by identifying risks across various domains, including: data privacy, algorithmic bias, model inaccuracy or instability, regulatory non-compliance, and cybersecurity vulnerabilities.

Data Privacy Breaches: This includes not only accidentally sharing sensitive data in prompts, as highlighted by the KPMG survey, but also risks related to data collection, storage, and usage by AI systems. 

• Algorithmic Bias: AI models are only as good as their data and where we are with artificial intelligence, data bias will perpetuate and amplify existing biases in the data they are trained on. 
• Model Inaccuracy and Instability: AI models can sometimes produce incorrect or unpredictable results, especially when faced with new or unexpected data. 
• Regulatory Non-Compliance: The use of AI is increasingly subject to regulations. Businesses must stay informed about relevant laws and ensure their AI systems or providers are compliant. 
• Cybersecurity Vulnerabilities: Since AI systems are relatively new, vulnerabilities are being targeted by cyberattacks, leading to data breaches, model manipulation, or denial-of-service attacks. 

Assess Risk Likelihood and Impact: You’ll want to assess each risk's likelihood and potential impact. This AI risk assessment helps create a strategic roadmap for safe AI use.

Develop a Mitigation Plan: And finally, develop an action plan to mitigate any high-priority risks.  For example, to mitigate data privacy risks, you might implement strict data governance policies, employee training on responsible AI use, and data encryption measures. To address algorithmic bias, you could use techniques like data augmentation or fairness-aware algorithms.

 These may include:

1. Implementing data governance practices

2. Regular model auditing and retraining

3. Establishing clear accountability frameworks

4. Investing in cybersecurity measures

5. Ongoing monitoring of regulatory changes

Remember to consider risks at every stage of your AI strategy. Doing so will better enable you to avoid costly mistakes and reputational damage, build trust with stakeholders and customers, enable compliance with changing regulations, and better maximize the value of AI investments.

Outline an AI strategy adoption and implementation plan

We know that any organizational transformation is difficult, so in order to set up your AI implementation and adoption for success, we recommend the following eight steps for establishing a comprehensive AI implementation plan:

1. Start by defining clear goals and metrics to measure progress. This ensures everyone understands the purpose and expected outcomes.

2. Assign roles and responsibilities to key team members. Designate project leads, technical experts, and business stakeholders. Clear ownership helps drive the initiative forward smoothly.

3. Develop a communication strategy to keep all parties informed. Regular updates maintain alignment and enthusiasm across the organization.

4. Assess your current AI skills and identify gaps. Plan for necessary training and resource development. This may include online courses, workshops, mentorship programs, technical documentation.

5. Create a phased rollout plan with measurable milestones. Start small with pilot projects before scaling. This approach allows for learning and adjustment along the way.

6. Allocate resources appropriately. Include budget, personnel, and technology infrastructure. Ensure you have the necessary tools and platforms in place to support AI initiatives.

7. Establish a timeline for implementation, but remain flexible. AI adoption often involves unexpected challenges and opportunities.

8. Plan for ongoing evaluation and optimization. Regular check-ins help you stay on track and make data-driven decisions to improve outcomes.

Choose an enterprise AI solution that empowers everyone 

Choosing the right AI solution and then empowering widespread AI adoption is critical for a well-planned AI strategy to succeed. When evaluating AI solutions, prioritize platforms that make your implementation and adoption simple, and enable:

1. An easy to use, intuitive user experience

2. Accurate, contextual responses

3. Seamless integrations with your current applications and systems

4. Scale across departments and use cases

Moveworks is a leading enterprise AI solution with an agentic Copilot that empowers employees across all departments with a single place to search, act, and get work done across all enterprise systems.  Our  platform delivers:

• Help employees find the right information faster: Take an intelligent, reasoning-powered approach to search and information discovery. Deliver trustworthy insights to employees, no matter your file location or format.
• Automate everyday tasks across enterprise systems: Accelerate your workflows with a Copilot that can take action in business systems – and complete tasks end-to-end.
• Increase productivity with secure access to powerful generative AI: Provide employees with the most useful generative AI capabilities in one AI assistant, reducing tool travel and tech stack bloat.
• Build and deploy AI agents at scale: Empower developers to go from idea to working AI agent in minutes. Extend the power of Moveworks across all systems and domains with our robust developer platform.

Moveworks gives employees instant access to knowledge, accelerates your workflows, all contributing to enhanced overall business and employee productivity.  

See how Nutanix lowered its IT wait times with AI in less than seven weeks.