Fierce competition means every business must adapt to succeed, and artificial intelligence (AI) and machine learning (ML) initiatives have emerged as vital ways to get ahead.
Now a staple of modern business, the adoption of cloud-based solutions has accelerated over the past year to meet the demands brought about by the pandemic, and in particular to support remote and hybrid working policies. Those businesses that have successfully implemented cloud-based solutions using AI and ML programs have report better streamlined processes, faster time to profit, and improved customer satisfaction as the top benefits.
This value has seen businesses spend on average $1.06M per year on AI and ML initiatives. That spend is spread across the organisation on current and planned projects, to grow revenue, drive innovation, increase productivity and enhance user experience. However, the pace of adoption has been rapid and success isn’t guaranteed – what’s more, analyst figures on project implementation make for sobering reading. Gartner predicts that under half of modern data analytics and ML initiatives will be successfully deployed in production by 2022. So, it is no surprise that more than half of Aussie IT professionals are still exploring how to implement and operationalise AI and ML models, when more than one-third of them also report R&D has been tested and abandoned or failed. For businesses investing millions, failing to grasp the complexities of building and running a productive AI and ML program can be costly.
A common pain point for IT teams, is balancing the potential benefits of AI and ML with the challenges of getting these programs off the ground. Whilst some early adopters are already seeing the benefits of these technologies, others are still trying to navigate a lack of internal knowledge, legacy technology stacks, poor data quality, or the inability to measure ROI. With an array of challenges in implementing AI and ML to optimise cloud technologies, many might wonder how to move forward. This combination of pressure and challenges can be overwhelming, especially if you’re at the start of your AI and ML journey.
Here are three actionable steps business and IT decision makers can make today to get on the right track.
1. Fill all skills gaps
Whether modernising tech legacy infrastructures, unlocking transformative new capabilities by developing custom AI and ML algorithms for your data, or creating enterprise-grade cloud deployment pipelines for AI and ML operations – all business needs are different.
However, not all businesses have developed the required technical skills and business processes to implement AI and ML solutions. They may not have expertise in mathematics, algorithm design or data science and engineering. Or the data may not be in a unified data lake infrastructure for ready access. These conditions create challenges for any business looking to advance in the market and derive value from AI and ML.
Before starting your program, assess your in-house skills and determine whether you can fill the necessary roles, are able to re-skill your team or need to enlist an experienced provider.
2. Address data quality
At the heart of any AI and ML program is the desire to generate and act on insights from data. However, data quality and data management challenges have historically plagued businesses, and these same challenges often stand in the way of AI and ML adoption.
These barriers fall mainly into the categories of data hygiene, governance and processes. Businesses going into AI and ML initiatives without plans to complete the requisite work around cleaning up data and streamlining data governance and data management are often destined to fail. AI and ML can help companies leverage data for innovative new use cases, but they can’t inherently clean up dirty data or realign data collection and governance policies.
An AI and ML program requires clean, integrated data. The first step in a successful AI and ML program is cleaning up your data and data processes – which includes setting definitions, eliminating data silos, establishing governance and aligning business processes.
3. Strategy first
As businesses look to the future, IT and operations are the leading the areas where they plan on adding AI and ML capabilities. However, AI and ML has potential in a variety of business units.
Organisational challenges in implementing AI and ML span beyond the IT department, with other obstacles such as executive buy-in also impacting the journey. Strategic concerns, like identifying use cases, and defining a business case, reiterate the importance of starting with a strong plan when embarking on an AI and ML program.
To define the right AI strategy, prepare the data, incorporate AI and ML frameworks into application and data platform development, and maintain and optimise the environment, requires dedicated planning, AI and ML engineering expertise and automated operations. However most importantly, strategy can sidestep the areas where AI and ML efforts may lose momentum or get lost in complexity.
Without a solid destination and organisational buy-in, an AI and ML journey could waste a lot of money and resources and never become production-ready. Start by gathering the major stakeholders, presenting a strong business case and gaining consensus on deliverables, milestones and timelines to keep your project on track.
Across industries, businesses are looking for ways to enhance their product offerings, improve business efficiency and anticipate customer behaviour. More than just implementing an application, successful AI and ML initiatives tie together a complex ecosystem of data, business processes and new skill sets – to ultimately, drive business value.