A new and profound technology race is upon us. While some organizations may be reluctant or slow to enter the race, one thing is becoming abundantly clear — early adopters are gaining a powerful competitive advantage by implementing artificial intelligence (AI) at scale. Whether producing new scientific breakthrough materials, identifying superior manufacturing methods, gaining deeper insights from purchasing behaviors, or creating unique leading-edge products and services, these 21st-century trailblazers are securing strategic advantage for decades to come.
Although AI has gained momentum in recent years, generating considerable interest across industries worldwide to advance and modernize processes and products, full-scale enterprise adoption has not yet been embraced as might be expected. According to a recent McKinsey Global State of AI report, only 50% of organizations have adopted AI in at least one function in 2020. As some organizations remain hesitant, others forge forward materially impacting our daily lives with AI-driven systems from voice recognition apps to route finding apps to recommendation platforms to autonomous devices and robotic solutions.
These systems use AI that processes vast amounts of information, solving complex problems with sophisticated computer algorithms. Rather than tackling large difficult problems right out of the gate, many organizations dip their foot in the water by implementing their first AI pilot project. Given a successful conclusion, initial pilot projects are often followed by a series of additional pilot projects. While this approach lends value for proof-of-concept purposes, the material gains for an organization are typically realized when scaling AI across the enterprise.
According to Boston Consulting Group (BCG), scaling AI isn’t just about investing more in new technologies alone but also integrating human capabilities and operations to extract value. Scaling, therefore, means deploying AI across the organization and realizing its full potential. This requires transforming a business’s operating model, including a series of top-down and bottom-up actions, including a committed budget.
Implementing the necessary change management strategies within the operation with the right approach to scale AI across the organization is much more complex than launching a series of AI pilot projects. Companies that successfully scale AI within their organizations have invested in people and processes as well as technology. And, once AI is deployed at scale, corporations have enjoyed considerable market advantage when such strategies are executed successfully.
What are some of the challenges to scale AI?
Building AI solutions and scaling them while they go hand-in-hand are two significant challenges organizations face. About half of all AI projects fail, and around 90% of companies face difficulties scaling AI across their organizations.
Challenges while building AI solutions in the early stages of adoption might include a lack of technical talent and AI expertise that can be both scarce and expensive. Further, AI implementations can be time-consuming, labor-intensive, and costly, with a less obvious probability for success.
Accenture carried out a study to analyze how crucial it is for businesses to implement AI at scale and discovered that around 84% of executives believe that they cannot achieve their growth objectives if they fail to scale their AI. Approximately 76% of these executives acknowledged that they know how to pilot but find it more challenging to scale AI across the business. Challenges for deploying AI at scale include
- IDC’s predictions in 2014 suggested that the world would generate about 44 ZB of data in 2020. However, by 2020, the world generated 64.2 ZB of data (verified by IDC in May 2021). The processing of such ever-expanding and vast amounts of data presents difficult challenges to organizations requiring complex AI models.
- AI applications and solutions can generate insights from every engagement with customers. The inferences generated from the large volume of customer interactions require machine learning and deep learning system training that may require expensive and heterogenous computation power.
- An era of smart and connected devices, the Internet of things (IoT), has been unlocked with the advent of AI. These devices take intuitive actions based on the awareness of the situation. Organizations are required to maintain AI models that comply with each device connected. These models and algorithms are built by developers who may have limited knowledge and expertise in understanding the end devices, leading to suboptimal performance trade-off decisions.
Thus, challenges for building AI and deploying at scale include ever-increasing volumes of data and diverse areas of implementation that increase technical complexity.
Best practices to implement AI at scale
While getting AI into full production is far from a trivial undertaking, the following approaches can be implemented by organizations to speed up their path to scaling AI.
Adopt a “fruit-basket” approach
AI is a ‘portfolio’ of technologies, and organizations may benefit by adopting a portfolio approach for AI implementation. In this approach, the organizations can create a “project portfolio,” similar to the fruit basket approach in investment management, where a few stocks might be quick wins, and few others might be big wins. Adopting this approach will help businesses realize some early benefits and understand some essential lessons on the applicability of these AI technologies in the specific context of their organization.
Build a powerful data strategy
AI programs thrive on data. With the right type, quality, and volume of data, these programs contribute to success. Having a robust data strategy that involves effective and efficient data generation (right data at the right time) and data governance (data ownership and data usage) strategies help the businesses accelerate their AI implementation and realize better outcomes.
Robust AI governance and policies
AI keeps learning over time, requiring organizations to implement an enhanced learning and refinement process after deploying AI programs to optimize their success. Organizations also need to create a robust governance model and risk framework to allocate the roles that have to be played by AI and humans for implementing AI at scale.
General practices
AI adopters that are in understanding, evaluating, and piloting phases of AI
– Get started with what you have, i.e., the data you have, where it resides, and who manages it.
– Start small. Create and follow a roadmap based on impact and feasibility and then scale.
– Adopt software engineering principles and transfer those skills and processes to AI projects by adjusting and customizing these policies and procedures for an AI environment.
– Open and transparent metrics should be mapped against all the KPIs and significant risk factors.
– AI projects should be regularly tested for bias and transparency to help ensure the AI is ethical and fair.
AI adopters that are in implementing, operating, and optimizing phases of AI
– Maintain a checklist of principles, KPIs, successes, failures, and create an architecture and team structure that operates at the intersection of design and data centers.
– Continuously evaluate and improve the AI models while in production as AI learns over time.
– Monitor the models for explainability, fairness, robustness, and unintended bias.
– Integrate the existing AI systems with robust NLP capabilities and other cutting-edge technologies to add value to the business and innovate at scale.
Realizing the full potential of AI means deploying across the organization. Armed with the best practices for scaling AI within your operation, you too may realize a competitive advantage within your industry and assure a strategic recipe for success.
If you would like to learn more about scaling AI within your enterprise, send us your query to intellect2@intellect2.ai. Intellect Data, Inc. is a software solutions company incorporating data science and artificial intelligence into modern digital products with Intellect2TM. IntellectDataTM develops and implements software, software components, and software as a service (SaaS) for enterprise, desktop, web, mobile, cloud, IoT, wearables, and AR/VR environments. Locate us on the web at www.intellect2.ai.