Blueprint for Developing a Successful Enterprise AI Strategy

As artificial intelligence continues transforming businesses, developing a comprehensive AI strategy is crucial for any organization that wants to gain competitive advantages and maximize value from these emerging technologies. However, crafting an effective strategy can seem daunting, given the complexities involved. Based on a proven framework, we will break down the critical elements of a holistic AI strategy. By following the approach outlined here, you can develop a strategy that will help your organization reap the benefits of AI in a managed way.

Everyday AI vs. Transformational AI

At the core of any effective AI strategy are the concepts of “everyday AI” and “transformational AI.” Everyday AI focuses on incremental efficiency gains through tools that augment human workers, such as chatbots, digital assistants, and other productivity aids. The goal is to boost individual productivity by 5-10% through skills development and automation of routine tasks. More specifically, everyday AI solutions aim to assist employees as they go about their daily work. Chatbots are a prime example, providing answers to frequently asked questions, helping with basic administrative tasks, and routing users to suitable human agents for more complex issues.

Other everyday AI tools focus on automating discrete steps in operational processes. For instance, data entry tasks that require transferring information from paper forms to digital systems are well-suited for automation. Computer vision and natural language processing can be applied to extract structured data from unstructured documents with minimal human involvement. Similarly, approval workflows can be streamlined by using AI to route documents to the appropriate signatories based on business rules. The goal of everyday AI is not to replace humans but to enhance their productivity and focus their efforts on higher-value work. By automating repetitive chores and augmenting employees with intelligent aids, organizations can achieve efficiency gains of 5-10% or more, according to various studies. This provides a foundation for further AI expansion.

Transformational AI, on the other hand, aims to fundamentally change business processes and drive substantial top- and bottom-line impacts through intelligent automation. This could include automating entire functions to reduce overhead by 75-90% or implementing autonomous agents to achieve best-in-class quality levels. For example, intelligent process automation can handle an organization’s order management procedures independently from end to end with minimal human intervention.

In another instance, AI and machine learning could be applied to a company’s customer service operations. By analyzing massive troves of past service interactions, an autonomous agent may be developed to resolve the majority of common issues through natural language conversations. This could dramatically cut costs while improving customer satisfaction metrics.

The possibilities for transformational AI are vast. By intelligently automating core business processes that directly impact key performance indicators, organizations have the potential to gain significant competitive advantages in both efficiency and quality that would have been unthinkable just a few years ago. Of course, such initiatives also require sophisticated technologies and specialized expertise to develop and implement successfully.

A strong AI strategy must pursue both everyday and transformational AI in parallel to maximize organizational benefits. Everyday solutions provide near-term wins to build momentum and skills, while transformational projects target high-impact opportunities that can truly move the needle. This balanced, two-pronged approach is critical. Governance, Risk, and Compliance

Establishing robust governance, risk, and compliance is crucial for managing AI initiatives safely and effectively. Left unaddressed, the complex issues surrounding AI development and deployment could seriously undermine an organization’s strategy and objectives.

The strategy should call for a multi-disciplinary AI council overseen by C-level executives to provide strategic guidance and oversight. This council is responsible for developing policies on matters such as data governance, algorithmic transparency, and responsible innovation. Technical standards like the OECD AI Principles and ISO’s upcoming AI safety guidelines can help inform the policy framework.

However, policies are only as good as their enforcement. The strategy must ensure technical controls are in place and mapped to appropriate frameworks such as NIST, ISO, and industry-specific regulations where applicable. For instance, controls around data access, model monitoring, and incident response need to be automated and continuously enforced by the AI platforms and systems. This moves beyond just administrative policy and instills responsible practices directly into operations.

Proactively identifying and mitigating risks is also a core function of the AI council. A structured risk assessment methodology grounded in standards such as the NIST AI Risk Management Framework will help surface issues early. This includes risks related to data, algorithms, and infrastructure dependencies, legal/regulatory concerns, and potential harms. Mitigation strategies can then be developed and operationalized.

Regular audits help validate that controls and risk management processes work as intended over time. Outcomes should be reported to executives and boards to ensure accountability. Organizations can responsibly drive AI initiatives forward with suitable governance structures while maintaining stakeholder trust.

Strategic Partnerships and Platforms

Most companies will not have the in-house expertise or resources to develop advanced AI capabilities alone, especially in the early stages of their strategies. The AI council must, therefore, identify where strategic partnerships are needed to access specialized skills, tools, and technologies.

There are a few factors to consider when selecting partners. First is the breadth and depth of their AI capabilities. Transformational projects, in particular, may require best-in-class capabilities across multiple domains, such as computer vision, natural language processing, optimization, and robotic process automation.

Partners should also have proven experience delivering complex AI solutions for other enterprises. References and case studies provide confidence they can navigate organizational and technical challenges. Complimentary services around implementation, change management, and ongoing support are also essential to consider.

Ideally, strategic partners would provide turnkey, outcome-based solutions rather than just point products or components. This allows organizations to realize value more quickly while avoiding the risks of piecemealing disparate parts together. Vendors that can tailor pre-built solutions to a company’s strategic goals and operating environment tend to yield the best results.

In addition to partnerships, core technical platforms are required to power transformational AI initiatives. This includes a cognitive storage layer to curate and manage vast volumes of structured and unstructured data. An AI operating system then sits atop this data foundation to develop, deploy, and manage intelligent automation models at scale using machine learning and other techniques. Additional specialized platforms may be needed for certain domains like computer vision or natural language.

These foundational platforms can be built internally over the long run or obtained from strategic partners initially when expertise is lacking. The right balance depends on an organization’s specific AI maturity and ambitions. Either way, platform selection and integration are significant undertakings that require careful planning within the overall strategy.

Communication and Education

Without a plan to drive organizational adoption and change management, no strategy is complete. Employees will play a key role in identifying new AI opportunities, providing data and feedback to fuel initiatives, and ultimately adopting new tools and working methods.

The strategy proposes immersive boot camp-style training sessions, strategic accelerators, and continuous awareness campaigns to educate and engage employees at all levels. Through interactive projects and exercises, boot camps provide hands-on learning of AI concepts and tools. Strategic accelerators then apply this knowledge by mapping AI applications to specific strategic goals and processes.

Campaigns like newsletter articles, online learning modules, and seminars help socialize initiatives continuously. Leaders must also be equipped to oversee AI implementation within their teams, which may require new skills in change management, retraining workers, and optimizing team structures. Continuous feedback loops allow for refining the strategy based on real-world experiences.

Execution Approach

With the foundational elements of governance, partnerships, platforms, and communication established, the strategy can outline how initiatives will be executed. This includes the following high-level phases:

Analyze Opportunities - Leverage boot camps, accelerators, and ongoing idea collection to map out where AI can create value across the organization comprehensively. Prioritize opportunities based on strategic alignment and impact.

Establish Governance - Form the multi-disciplinary AI council and standing committees. Develop the initial policy framework and controls architecture with partners.

Select Partners - Evaluate options and onboard strategic partners best equipped to help with the most critical capabilities.

Deploy Platforms - Roll out the cognitive data layer and AI operating system through partners or internal teams.

Host Initial Sessions - Launch the first boot camps and strategic accelerators to start building skills and mapping AI to key processes. Pilot Projects - Stand up initial everyday and transformational AI initiatives as proofs of concept in controlled environments.

Continuous Improvement - Pursue both types of AI in parallel through iterative development cycles, continuously optimizing and expanding capabilities. Monitor KPIs and benefits realization.

Reinvest Savings - Reinvest a portion of efficiency gains into further automating strategic priorities and fueling innovative new opportunities.

Organizations can systematically build AI maturity and capture value over time by taking a phased and agile approach guided by the framework. Continuous oversight from the AI council ensures the strategy remains aligned with business objectives as technologies, markets, and other factors inevitably change. With the right blueprint, AI becomes an engine of sustainable competitive advantage.

Conclusion

Developing a comprehensive yet balanced AI strategy is key to extracting value from these technologies in a responsible manner. By addressing the pillars of governance, partnerships, platforms, communication and execution laid out here, companies can craft the right multi-pronged approach.

Following this proven framework will help organizations methodically pursue both incremental everyday benefits and transformational impacts. With the proper strategy, AI can be leveraged to gain competitive advantages, improve outcomes, and prepare workforces for the future while proactively managing risks. Now is the time to start planning how your enterprise can benefit from this new wave of innovation.

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