By Ryan Rascop / 4 Aug 2022 / Topics: Data and AI
Benefits of AI include:
But despite the consensus around AI’s importance and potential, organizations are often on slightly different paths when it comes to AI adoption. With the market flooded with AI solutions, best practices and strategies, it can be difficult to determine what actions to take to build and sustain momentum.
At Insight, we see five stages of AI adoption:
Here’s a breakdown of the five maturity levels of AI adoption, common challenges associated with each and how to keep making progress — whether you’re still exploring AI or winning at it.
In this “early interest” stage, there is awareness that AI could be valuable in your business, but you don't know what your first move should be. Don’t fret. This is a favorable place to be — it gives you the ability to slow down, assess options and avoid brute-forcing change that could cause more inefficiencies to the business.
Being in this phase is all about exploring your challenges, potential use cases and the overall art of the possible.
Many organizations in this stage find great success with brainstorming workshops designed to answer questions such as:
A strategic envisioning exercise will use a best-practices approach for this particular maturity level — there’s no jumping the gun and no hasty landing on a silver-bullet tool or solution. Done right, discovery workshops will thoroughly explore ways to integrate AI into your business, triage AI ideas based on ROI and estimated time to value and outline a roadmap for delivering the most compelling AI solution.
If you’re in what’s known as the “active” stage of AI adoption, you’ve already begun to experiment with AI (mostly in a data science context). The prototypes exist and you might have a couple of data scientists, but nothing is crossing the threshold into production.
Why is this challenge so common? The skillset needed to put an AI model into production isn’t one that’s found across data science, IT or DevOps alone. It’s a new skill set — one that requires a unique mix of expertise across all areas.
At this stage, pulling in multidisciplinary resources to build an actionable framework around model building is key. Your framework should adhere strictly to secure, repeatable best practices and may focus on CI/CD and DevOps enhancements, deployment to edge environments and more — all depending on your needs. Investing in these consultative solutions can help move your ready AI and analytics models into the hands of business decision-makers, propelling your initiative forward and getting your models into production.
If you’re in this stage, you’re considered “operational,” but teams may be experiencing challenges with:
Similar to stage two, it can be extremely valuable to bring in an interdisciplinary, consulting-driven team to guide further progress. As a solutions integrator, our teams at Insight span multiple areas: OCM, Agile, applications, cloud, security, DevOps, machine learning and data science. Bringing this “brain trust” of diverse specialists into one room has been a game-changer for us. For our clients, it gives them an objective look at specific roadblocks. Often, struggling to hit your stride with AI is caused by gaps in strategy. Uncovering ways to fill those gaps — in a truly holistic way — can be transformative.
In this stage, you are considered “systemic” in your AI adoption. At this level, organizations are increasing their deployment of AI solutions, and new opportunities are surfacing — not to solve for a lack of buy-in or production problems — but to improve efficiency and effectiveness as teams continue to deliver AI projects. A proven way to sustain momentum in this stage is to invest in solutions that streamline the way you develop and manage AI systems across the enterprise.
In the systemic stage, solutions around new infrastructure and modern platforms for data integration can be of significant value. It’s also critical to develop strategies for measuring the performance of AI models and to implement new programs to train and upskill users. New programs will often require different resources and outside guidance from teams that specialize in AI training and upskilling.
This is the apex of AI adoption, and few organizations operate at this level, known as the “transformational” stage. Here, AI drives strategy and decision-making, and is defined by executives as a key budget priority. If you’re in this stage, AI is embedded across the entire organization, touching operations, product offerings and services.
Sustaining momentum in this stage will be about true innovation — building and executing on strategies around the ethical use of AI, as well as empowering AI teams to focus on next-generation business challenges and unlocking new AI solutions to solve them.
Whether you’re just starting out or have realized the full potential of AI, success is possible at every stage of the AI journey. The rate of innovation we’re seeing today means that the possibilities are endless. And while an AI initiative can seem intimidating because of its vast potential, focusing your efforts on the quickest, highest-ROI use cases means you’re on the right track.
Insight AI Practice
Ryan leans on more than 15 years of industry experience to shape Insight’s go-to-market strategy around AI, empowering teams as they help clients adopt AI strategically. His expertise spans technical and solutions engineering across critical areas, such as storage, networking, cloud and more.
