2021: The year of enterprise AI implementation
At 2021.AI, we believe that the year 2021 will be the year of enterprise AI implementation. Over the past several years, we’ve seen AI adoption speed increase as the implementation barriers have lowered. The climate continues to be healthy for adopting AI with support from boards and top management who want to invest more in AI in 2021.
Companies are increasing their AI budgets, and most are now seeing their initial investments starting to pay off. However, the practical implementation of AI continues to be a hurdle that burdens most organizations. To successfully execute on their enterprise AI ambitions, most will need to educate their organizations, implement MLOps platforms, and be alert to new laws, regulations, and policies for data and AI.
Your organization is as important as ever before
Many companies have yet to prepare for the organizational changes required to establish the foundation and structures to ensure a successful enterprise-wide AI implementation. The commitment to AI needs to be pervasive throughout an organization with support and ownership from the board and top management. It should also include strong guidance from the leadership team throughout the organization’s different functions. Without full commitment, enterprise-wide AI implementations are unlikely to succeed. Organization-wide guidelines and structure must ensure that those directly and indirectly involved understand the roles and responsibilities that come with implementing AI.
Algorithmia’s 2020 State of Enterprise Machine Learning survey found that the largest hurdle that enterprises face is scaling AI across their organizations. If your organization does not have the foundation built for AI, there will inevitably be problems with scaling any success. We believe that an important, and often neglected step, in the process is to ensure that the maturity of an organization’s operations matches the complexity that comes with AI adoption.
Our experiences show that a comprehensive end-to-end AI platform must have smooth implementation and deliver the best possible results and most value. Such a platform will also help alleviate the issues that come with Shadow AI, where different AI projects and tools are scattered across an organization in an unorganized and unhelpful manner.
Responsible AI on the rise
Another hurdle, which has surfaced during the last 18 months, is the fundamental concept of the responsible use of AI. A very concrete way for organizations to address the responsible use of AI is to work with the concepts of Governance, Risk & Compliance (GRC) for data and AI, which offers a robust framework for enterprise-wide AI implementation. In 2021, we believe that it will be imperative to have a GRC solution for an enterprise’s use of data and AI. Organizations must prepare to deliver consolidated insight around their enterprise-wide use of data and AI. It will need to include substantial internal and external reporting to monitor and track all their data and AI activities in real-time. The ability to deliver such all-encompassing reporting while at the same time scaling up the number of AI projects in a consolidated enterprise-grade implementation is a steep challenge.
Across AI projects, organizations must also automatically produce and share easy to understand documentation with internal and external stakeholders. Furthermore, this must apply to all AI projects to instill trust for users and others potentially impacted. To get here, organizations need to move beyond focusing primarily on an AI project’s model development and start gravitating towards a more collaborative process across data science, IT, and business operations to support the entire AI-lifecycle. As companies begin to involve more stakeholders in their AI projects, a prerequisite for making it efficient becomes having all activities consolidated in one enterprise-wide platform, offering transparent processes combined with a robust GRC solution that monitors, reports, and audits all activities. Only with such support will companies be able to comply with the growing number of new laws, regulations, and policies for AI and data across all sectors. This compliance burden for data and AI we foresee will continue in 2021 and the coming years.
2021.AI’s Grace Enterprise AI Platform is an end-to-end AI platform with full support for Governance, Risk & Compliance. Grace makes it easy to implement and scale AI projects alongside the necessary GRC for data and AI to comply with the growing number of data and AI laws, regulations, and guidelines.
About the authors
Founder and CEO, 2021.AI
Mikael is the founder and CEO of 2021.AI. He has 25+ years of experience in Technology and Financial sectors. Mikael was the Global Head of Technology and Operations at Saxo Bank. He is the Chairman of Copenhagen FinTech, investor and board member of several other technology companies.
PRODUCT MANAGER, 2021.AI
Yina is a Product Manager at 2021.AI working to bring Responsible AI to every enterprise. She has experience working with AI platforms and investing in early-stage startups. Yina is also the author of the newsletter, The Big Y, where she focuses on interesting and relevant AI topics.
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