Artificial intelligence promises immense opportunities for businesses – from driving efficiency to enabling new products and revenue streams. However, realizing AI’s full potential requires comprehensive planning and execution. Based on insights from AI leaders, this 2600+ word guide outlines the key components of an enterprise AI strategy for maximum business impact.
Table of Contents
- Align AI with Business Goals
- Implement Robust Data Governance
- Architect Future-proof Infrastructure
- Organize for Effective AI Adoption
- Industrialize AI Development
- Incorporate Responsible AI
- Foster an AI-ready Culture
1. Align AI with Business Goals
The first step is to clearly identify how AI can support your organization’s strategic objectives. Instead of implementing AI just for technology’s sake, focus on high-impact business use cases.
For example, AI-powered customer service chatbots from vendors like ADA Support and MobileMonkey help e-commerce businesses boost sales and loyalty. British Airways uses AI to personalize promotions and recommendations. This doubled response rates for some campaigns. AI inventory optimization solutions from vendors like Blue Yonder and AnyMind minimize waste and stockouts.
AI leaders suggest assessing processes tied to business KPIs – customer conversion, product quality, cycle times etc. Look for processes that are constrained despite automation, needing complex expert decisions or dependent on large, fast-changing data. These indicate high AI readiness for measurable ROI.
An example is Starbuck’s using AI to optimize staff scheduling, improving customer wait times and store efficiency. As Kevin Johnson, Starbucks CEO notes, “AI and ML give us the capability to take all of the data in our stores and make sure that we are staffed appropriately at the right times”.
So take a strategic view of your operations to pinpoint use cases for efficiency, automation and enhanced decision making via AI. Partner with business teams to identify processes for AI pilots with clear ROI metrics. This aligns AI programs with financial outcomes versus vague modernization goals.
2. Implement Robust Data Governance
High-quality datasets are the lifeblood of AI systems. That‘s why an enterprise AI strategy must incorporate strong data governance protocols and infrastructure.
Matt Sanchez, Vice President of Enterprise Data and Analytics at financial services giant TIAA, stresses the need for a holistic view. "Things like data lineage, data cataloging, data transparency and model risk management are crucial. You want visibility into the history of your data as it flows through the organization.”
It’s also vital to ensure consistency in how data is handled across regions, business units and applications. Data ethics and privacy should be top priorities too. Options like BigID, Collibra and Informatica provide data governance capabilities tailored for AI initiatives.
With growing model complexity, concept drift monitoring is equally important – tracking statistical shifts that degrade model performance over time. This requires robust data ops pipeline data with mechanisms to raise alerts on drift.
Crowdsourced labeling and synthetic data generation also bolster datasets for better model accuracy. Talking about leveraging human intelligence for data science, Matt Turck of FirstMark Capital notes, “We’re seeing a Cambrian explosion of startups using crowdsourcing to generate labeled datasets”.
Ultimately, well-governed, high-quality data is indispensable for impactful enterprise AI. Treat it as a core business asset with appropriate investments in people, processes and technology.
Below is a sample data governance policy outline covering key aspects like security, privacy, sustained access and risk management. Enterprises should use this as a template while formulating formal data standards.
Data Governance Policy Template – Source: Cloud Data Governance
3. Architect Future-proof Infrastructure
With AI model complexity rising exponentially – OpenAI notes 300,000x growth from 2012 to 2022 – so are compute requirements. We’re firmly in the era of hyperscale AI needing thousands of PCs worth of horsepower.
Cloud platforms like AWS, GCP and Azure offer on-demand access to AI hardware including GPUs and TPUs. For most enterprises though, the cloud can get prohibitively expensive at scale. Dedicating in-house infrastructure is preferable despite higher upfront costs.
Source: Determined AI
For training complex Deep Learning models, GPUs are essential given their parallel processing capabilities. NVIDIA T4 GPUs provide strong price-performance for mainstream workloads. For intensive workloads, the latest A100 GPUs are ideal.
TPUs are purpose-built for inference – Google’s TPU v4 pods offer up to 1 exaflops of AI performance. This powers scalable inference services handling billions of production requests.
On-premise AI infrastructure should factor in present and future needs around model development, deployment latency, data pipelines and more. It‘s wise to overprovision – AI’s exponential trajectory won’t slow down.
Also leverage orchestration tools like DeterminedAI and Kubernetes to simplify infrastructure management.
The bottomline – architect infrastructure for fast, efficient hyperscale AI with room to grow.
4. Organize for Effective AI Adoption
To steer AI adoption across the enterprise, a dedicated AI Center of Excellence (CoE) is indispensable. The AI CoE charter spans:
- Identifying high-value AI applications
- Piloting solutions
- Coordinating technical and business teams
- Defining standards and best practices
- Monitoring progress
Staffing is critical – you need subject matter experts from IT, engineering and business units plus project managers and even ethicists.
Rachael Rekart, AI Lead at real estate giant Jones Lang LaSalle explains, "Our AI CoE includes data scientists, data engineers, and business solution engineers. We also leverage external partnerships with academics and AI experts."
Centralized governance is prudent too – platform standards, model validation procedures, and stage-gates for new initiatives.
With a collaborative, skilled AI CoE driving progress across functions, you get coordination, speed and consistency.
Many leading enterprises now use a Hub-and-Spoke model for their AI Centers of Excellence:
AI CoE Hub-and-Spoke Model – Source: Intel
Here, domain-focused “Spoke” teams coordinate with a central “Hub” AI CoE housing shared data, tools and platforms. The Hub provides oversight and best practices. Spokes operate agilely while benefiting from economies of scale.
Whether Hub-Spoke or centralized, the AI CoE is key for enterprise success. Staff smartly, invest in technology, and align to business priorities.
5. Industrialize AI Development
The pathway to large-scale AI success isn‘t ad-hoc experiments but industrialized processes for building, deploying and monitoring intelligent systems. Mike Gualtieri of Forrester Research characterizes this as "AI software engineering".
It entails developer-friendly interfaces, modular components, CI/CD pipelines, regression testing and controlled rollouts. Platforms like Algorithmia and ParallelM enable assembly-line creation of enterprise AI apps.
Equally crucial is model ops tooling – scalable deployment, explainability, concept drift detection and lifecycle governance. Vendors such as Arize, TruEra and Valohai provide these capabilities.
For instance, Valohai’s MLOps platform handles the entire machine learning lifecycle – from experiment tracking to model monitoring and version control:
End-to-End MLOps Platform – Source: Valohai
Together, these techniques support rapid experimentation along with safe, explainable and maintainable AI apps at enterprise scale.
6. Incorporate Responsible AI
For Enterprise AI to fulfill its promise, ethics and responsibility cannot be an afterthought.
Businesses must evaluate AI systems for potential issues around bias, fairness and explainability. Techniques like differential privacy help tackle bias while open standards such as DARPA‘s XAI boost model transparency.
Here is a code snippet demonstrating a bias testing approach using an open-source library:
from aequitas.bias import Bias
# Sample demographic datasets
ds_1 = pd.DataFrame(data={‘group‘: [‘A‘,‘B‘], ‘decisions‘: [100, 50], ‘outcomes‘: [50, 10]})
ds_2 = pd.DataFrame(data={‘group‘: [‘A‘,‘B‘], ‘decisions‘: [50, 100], ‘outcomes‘: [40, 25]})
bias_checker = Bias()
bias_checker.load_data(ds_1, ds_2)
# Test Dataset 1
bias_report = bias_checker.get_report()
print(bias_report)Python Bias Checking – Source: Aequitas Library
Many organizations now have Executives focused on Responsible AI – like Kathy Baxter at Salesforce and Timnit Gebru previously with Google. Checklist-driven frameworks such as the ACM Code of Ethics codify best practices that enterprises should adopt.
Ultimately, Responsible AI minimizes risks and liabilities for organizations while increasing public trust – key for the technology’s broad adoption. Make it a cornerstone of your enterprise AI strategy.
7. Foster an AI-ready Culture
An enterprise-wide AI transformation goes beyond just technology to also encompass people, culture and processes.
Invest in continuous learning – AI Academies and certification programs to reskill employees. Restructure workflows to integrate predictive models. Communicate proactively to allay anxieties about AI taking over jobs.
AI Business Strategist Esteban Kolsky stresses, "AI is a culture play – unless you transform how business is done, ROI will be limited."
Leading enterprises now run AI awareness drives along with technical and non-technical AI skill-building programs. Starbucks holds AI Forums for employees globally and has AI training content integrated into its mobile app.
With clear understanding of AI’s promise, and support for adopting new capabilities, employees will pivot from resistance to becoming enthusiastic ambassadors.
The Road Ahead
In summary, an effective Enterprise AI Strategy must straddle business objectives, data, infrastructure, development rigor and cultural readiness for maximum transformational impact.
Done right, it propels significant top and bottom line gains. The pillars above serve as your blueprint.
For guidance on formulating and executing your AI strategy, feel free to connect with leading AI consultants. The road ahead promises intriguing opportunities at the intersection of business priorities and artificial intelligence!