Implementing artificial intelligence at scale remains a challenge for many companies. While AI pilot projects may show promise, translating that success into enterprise-wide transformation requires a comprehensive strategy.
In this detailed guide, we will explore the 6 essential components that set leading organizations apart in scaling AI‘s impact:
1. Start with Your Business Strategy
The companies that gain the most from AI share a common practice – aligning AI initiatives with overarching business strategy.
AI is not a solution looking for problems. It is a powerful means to achieve strategic business goals if applied thoughtfully.
Assess strategic readiness
Before embarking on enterprise AI, conduct an audit evaluating your organizational strategy. Key questions include:
- Is our strategy still relevant given market and technology changes?
- What are the biggest gaps between our strategic priorities and current capabilities?
- Which business processes can be improved the most with AI capabilities?
- Do we have the supporting data, infrastructure, and organizational alignment needed to succeed?
This assessment provides focus on high-impact AI use cases that map back to strategic priorities. It also identifies gaps that must be closed before scaling AI adoption.
Avoid misalignment risks
Many companies fail by rushing into AI without grounding projects in business strategy. According to Gartner, through 2022, 85% of AI projects will deliver erroneous outcomes due to bias in data, algorithms or human interpretation.
For example, leading retailers implemented personalized recommendation engines without updating website design or product taxonomy. As a result, abandonment rates increased even as on-site search improved.
Such failures demonstrate the risks of misaligned AI investments that don‘t consider the broader business context.
2. Adopt a Data Strategy
"Data is the new oil" has become a cliché. But behind this trite analogy lies an essential truth – data is the fuel for enterprise AI.
Without a sound data strategy, flawed data will impair even the most advanced algorithms. Poor data means poor results.
Focus on quality, not just quantity
Many companies focus excessively on accumulating more data. But above a certain point, marginal gains decrease as data quality issues compound.
Leading organizations take a data-centric approach focused on:
Accuracy – Does the data reflect ground truth? What are the error rates?
Completeness – Is the data comprehensive? Are there gaps?
Relevance – How useful is the data for the desired task?
Timeliness – Is the data current? How frequently is it updated?
Continuously monitoring and optimizing these aspects improves results faster than acquiring more low-quality data.
For example, Google improved the accuracy of its translation service using cleaner, higher-quality datasets rather than exponentially bigger ones. This data-centric approach reduced errors by 60% compared to prior versions.
Implement data pipelines
Manually managing enterprise data for AI is impossible. Automated data pipelines are essential to efficiently collect, clean, label, integrate, and prepare data at scale.
Leading organizations are investing in data workflow orchestration platforms to productionize these pipelines. This enables scaling while reducing costs and delays for data scientists.
According to a Fivetran survey, companies using automated analytics pipelines saw ROI increase 8X compared to manual approaches. Those investing in data pipelines will gain a competitive edge.
3. Invest in Technology Infrastructure
Developing and deploying enterprise-scale AI requires vast computational power. The computing needs for state-of-the-art models are rapidly increasing.
For example, OpenAI found that since 2012, the compute used in leading AI research has doubled every 3.5 months. This exponential growth shows no signs of slowing down.
Supporting intensive workloads across the enterprise demands strategic infrastructure investments.
Cloud vs on-premise considerations
Cloud services offer convenient access to flexible computing with lower up-front costs. But ongoing fees can become exorbitant for heavy production workloads.
On-premise infrastructure requires huge initial capital expenditure. However it provides more control, security, and lower total cost of ownership at scale.
For example, autonomous vehicle company Scale AI found cloud costs for their workload exceeded $6 million per month. By investing $15 million in their own hardware, they reduced this to $1 million – paying back the investment in just over 2 months.
MLOps boosts efficiency
MLOps platforms optimize model development, deployment, monitoring and management. This improves productivity, efficiency and reliability of machine learning engineering.
According to McKinsey, MLOps can reduce wasted cycles by 80% and cut development costs by 50%. Companies scaling AI should integrate MLOps into their DevOps programs.
4. Establish an AI Center of Excellence
Scaling AI across the enterprise is too complex for disjointed, siloed initiatives. A dedicated AI Center of Excellence (CoE) is key to streamlining a unified strategy.
The AI CoE comprises a cross-functional team including data scientists, IT experts, business leaders, strategists and others. Their mandate is governing and supporting all AI activities company-wide.
According to a Deloitte survey, 37% of large companies have established an AI CoE, recognizing their importance in effective scaling.
Structure and staffing
While structures vary, common roles in an AI CoE include:
- Leadership: Senior executives governing strategy, budgets and roadmaps
- Technical: Data engineers, ML engineers, architects
- Domain experts: Business analysts with operational insights
- Ethics: Responsible AI specialists to assess risk
- Program managers: Cross-functional coordination
Staffing should cover this diverse skill set while maintaining agility in a small core team. Top-down executive support is critical for alignment and access across business units.
Unified AI vision
With this structure, the CoE can promote a consistent AI vision enterprise-wide. Shared best practices reduce redundancy and fragmentation. The CoE provides training, change management, feedback channels and other support for smoother adoption.
According to PwC, 89% of companies with an AI CoE believe it accelerates adoption by orchestrating strategy.
5. Develop AI Responsibly
AI brings tremendous opportunities alongside new risks. Companies must commit to ethical development and deployment.
Failure to address responsible AI risks brands facing backlash, legal issues and loss of trust. Incorporating ethics helps avoid these pitfalls.
Key principles
Leading authorities including the IEEE have identified core principles for responsible AI:
Fairness – Mitigating bias and discrimination in data and algorithms
Transparency – Enabling visibility into how systems operate
Security – Safeguarding confidentiality, integrity and availability
Reliability – Ensuring consistency, accuracy and safety
Privacy – Protecting personal data and adhering to regulations
Inclusiveness – Promoting accessibility to disadvantaged groups
Practices for implementation
Translating principles into practice requires concrete changes by technical and non-technical teams alike:
- Conduct AI impact assessments identifying risks
- Adopt techniques like differential privacy to preserve anonymity
- Monitor systems for bias or other harms using external audits
- Clearly communicate AI usage and get consent where applicable
- Provide explainability into model features and decisions
- Enable human oversight of consequential predictions
Responsible AI reflects shared values. With ethical implementation, companies can advance AI for social good.
6. Increase Employee Engagement
The transformative impact of AI relies on people. To drive change, companies must engage workforces through upskilling, role redesign and change management.
Build new capabilities
AI is not eliminating jobs, but transforming them. McKinsey estimates less than 5% of occupations can be fully automated. However, most will require new skills as AI enters workflows.
Companies must invest in capability building through training programs. Data literacy, data engineering and machine learning engineering are among the most in-demand skills.
Upskilling shows employees their value in the face of disruption. It also unlocks hidden productivity as teams augment their strengths with AI tools.
Reconfigure roles
AI‘s strength is narrow, repetitive tasks. This creates an opportunity to elevate human work.
Thoughtful job redesign focuses humans on creative, strategic priorities while AI handles routine activities. For example, chatbots handling common customer queries frees staff for complex issues.
According to Capgemini, 78% of organizations say AI increases time focused on core job functions rather than admin tasks.
Cultivate understanding
Transparent communication and education help employees understand AI-driven changes. Leaders must provide the context and vision for how AI supports individual and organizational success.
With empathy and inclusion, companies can overcome fear of the unknown. AI transformation relies on people. That starts with building their trust.
Enterprises no longer ask if they should adopt AI, but how to adopt AI successfully. This requires evolving strategy, data, technology, people and ethics in concert through an integrated framework.
Companies that navigate these 6 components effectively will transform their organizations and industries. Those that don‘t risk squandering resources on fragmented, siloed AI initiatives.
Scaling AI is complex but necessary in a digital world. We hope this guide provides a strategic blueprint to overcome challenges and capture opportunities. The future will reward companies that start building their enterprise AI foundations today.