According to Accenture, companies that deploy AI at scale achieve ~3x higher return on AI investments than companies that are in the AI proof-of-concept phase. However, deploying AI at scale can be challenging for companies as it involves much more than implementing pre-built AI models for arbitrary business processes.
Companies need to formulate an enterprise AI strategy to accelerate and successfully scale AI adoption. In this research, we will explore 6 key components of a successful AI strategy.
1. Start with your business strategy
AI is a powerful technology but implementing AI is not a goal in itself. An AI strategy must be developed to support a business strategy. Starting with defining the business strategy would help companies:
- Review their business strategy to see whether it is still relevant,
- Align their business strategy with the opportunities offered by AI,
- Decide the processes where AI can add value and where alternative technologies can be used,
- Ensure that the processes where AI can add value are ready for it,
- Avoid unnecessary costs from failed AI projects that had been initiated without a concrete strategic roadmap
2. Adopt a data strategy
Data quality can make or break AI systems. Therefore, if you want to scale AI in your organization, your AI strategy must be supported by a solid data strategy. This includes:
- Managing all components of the data lifecycle from collection and storage to integration and cleaning.
- Ensuring that you feed your AI systems with high-quality data with accurate labels.
Automation is key to a scalable data strategy. You can manage data manually for a small AI model, but an enterprise AI strategy requires automated data pipelines. Check our article on DataOps to learn more about automated data orchestration.
If you don’t have large amounts of data, you need to focus on the quality of your datasets. Not having large datasets shouldn’t stop you from implementing AI in your business. Industry leaders argue that small but high-quality datasets can produce better performing models than large but low-quality datasets with a data-centric approach to AI development.
3. Ensure you have a reliable technology infrastructure
AI systems can be hungry for computing power. According to OpenAI, the computational power used to train popular AI models has doubled every ~3.5 months since 2012. Therefore, it is important to have the infrastructure to develop and deploy AI models.
Cloud services can be a cheaper way to start with AI initiatives. However, an on-premise infrastructure with specialized hardware can be a more cost-saving option in the long run. For instance, Figure 1 provides a cost comparison between different cloud computing services vs. building on-premise infrastructure for deep learning.
Figure 1. Cost comparison of cloud services and on-premise DL infrastructure
Source: Determined AI
4. Establish a cross-functional center of excellence
A dedicated business unit that oversees and coordinates all AI initiatives in your organization is an important component of a successful enterprise AI strategy. This unit would identify AI use cases and set a roadmap for them. 37% of the large companies in the US have already established such business units, called AI Centers of Excellence (AI CoE).
Companies should build AI CoEs with a broad range of skills, including AI and IT professionals as well as business executives and domain experts for specific use cases. This would help companies:
- Bridge the gap between executive decision making and AI implementation,
- Create a unified vision for AI across the enterprise,
- Standardize common practices and facilitate communication.
For more, feel free to check our article on AI Centers of Excellence and how you can build them.
5. Develop AI responsibly
As AI systems become more powerful and widespread, people are increasingly concerned about the ethical implications of their use. Without taking a responsible approach to real-world AI development, you can make unfair and biased decisions that impact the public. You can also put your company at risk with lawsuits or reputational damage.
Business leaders and AI professionals must familiarize themselves with AI ethics and the principles of responsible AI development such as fairness, transparency, privacy, and security. Feel free to check our article on responsible AI to learn about how you can implement these principles in your AI systems.
You can also check our article on AI bias and how to reduce it.
6. Increase employee engagement
An organization-wide AI initiative cannot be limited to technology investments. You should also invest in the people aspect of your initiative and align the company culture and ways of working with your AI vision. This is because you will need new skills for a large-scale AI transformation. Moreover, employees may fear being replaced by AI which can slow down the transformation.
To overcome these challenges, companies should:
- Create opportunities for reskilling and upskilling for workers,
- Restructure business processes, workflows, and policies,
- Improve communication so everyone understands what is changing, why it is changing, and what the expectations are.
These can help companies prepare for the changes that large-scale adoption of AI will bring and increase employee engagement.
If you have other questions, we can help:
Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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