Hugh Simpson, Global Lead – Data&Analytics, A.I.&Industry 4.0 at Ciklum. Hugh is assisting the clients to become data-driven organisations. Previously he worked for EY consulting clients on IoT and Data Analytics.
The capabilities afforded by artificial intelligence (AI) are reshaping industries and the ways in which companies employ enterprise technologies. But with all its opportunities, AI remains an operational challenge for most companies today. Their inability to implement and scale AI tools effectively poses serious problems for the longevity of their companies:
In fact, most companies are only piloting AI or using it in a single business process, providing only incremental benefits, McKinsey reports. They must do more to scale AI’s impact. But a recent analysis from Ciklum shows many companies reach a ‘state of paralysis’ beyond the proof of concept (PoC) stage of AI development, in part because they are equipped for only traditional software development.
What’s more, companies are not investing in the organisational changes required to scale AI for operational and business success. They are wanting for a practical means to transition from PoC to widespread end-user adoption in a timely way.
Overcoming a ‘State of Paralysis’ When Scaling AI
Companies that take the right approach to organisational changes can scale AI and its adoption, delivering meaningful business value, repeatability, and longevity. They use an implementation strategy that is planned and developed with a product-based—and not a software development-based—approach. That is, they develop AI as if developing a product.
As we will find, companies can break down barriers to scaling and reduce time to value using an AI ‘Route to Live’ approach. This roadmap to live implementation defines the steps needed to take AI projects from PoC to production, successfully and with expedience.
Treating AI Like a Product Positions It as a Critical Source of Business Value
But organisations continue to find it difficult to transition from positioning AI as a source of innovation to one of business value. That’s because AI is different from traditional software implementation projects, which companies are typically better equipped to deliver.
The data infrastructure required for AI includes environments that can manage multiple large data sets and scalable neural network algorithms. Developers must prepare and transform data into the right form to be effective for AI as well. AI must become infused with both new and existing software, where developers must apply new practices to both.
Preparing Your Organisation for AI
A pilot AI implementation may only demonstrate the value of AI in a limited capacity. A product-based approach to AI development can make the difference in realising a truly business-driven solution. Bridging this gap is where the “fail fast, fail often, fail forward” product-driven approach makes a difference.
But making a difference requires companies to transform their organisations as well, with the same level of commitment they might assume during a new product launch. That includes realigning corporate strategy, introducing employees to new skills, and scaling capabilities.
“These AI high performers are more likely to apply core practices for using AI to drive value across the organisation, mitigate risks associated with the technology, and retrain workers to prepare them for AI adoption,” McKinsey reports.
But AI requires specialist skills and experience not readily available in most in-house data and analytics teams. Top AI engineers are a mix of data engineer, data scientist, researcher, and business analyst, all with a product-centric mindset. Ciklum’s AI ‘Route to Live’ approach helps companies position themselves with the tools and expertise they need to carry out their product-driven approach to AI development in this way.
Creating a ‘Route to Live’ for Your AI Solution
There are three clear stages to a successful ‘Route to Live’ AI implementation approach. The approach is characterised by accelerating AI development from pilot to production, and aligning AI with business value in strategic ways.
1. Set Your Vision with AI Strategy and Discovery
AI implementation success means aligning your business, technology, and AI strategy. Start by identifying your current state of drawing on pre-defined use cases or sector experience. Then, use a structured AI “discovery” approach that defines and prioritises only the right use cases—ones that augment humans to create business value.
2. Start Small with Proofs of Concept
As you prepare your data and infrastructure for AI, don’t get bogged down by technology at the start. You can reduce your time to value by establishing the right governance, data preparation techniques, and both flexible and scalable architecture blueprint first.
Your next step is to get the right people and capabilities. Leverage experienced AI engineers and data scientists who have successfully scaled AI before, then integrate them seamlessly into your existing team to share knowledge.
3. Launch Fast and Scale in Months, not Years
Finally, move quickly beyond proof of concept by aligning success to business value. Taking a product-led approach to scaling AI effectively enables you to fail often, fail fast yet fail forward enabling you to reduce time to value and increase ROI.
As you move forward, ensure you scale with teams tailored to your needs. With the right resources, you can build a fit-for-purpose team around your solution—including QA, DevOps, data engineering, and core development right through to project and product management experts.
Fail Often, Fail Fast and Fail Forward, with Ciklum
A product-driven approach allows you to accelerate AI implementation and scale as you support the role and processes required to keep it running successfully.
The vast majority of companies plan to either increase their AI investments in the coming years or adopt AI for the very first time. Ensure you are putting your AI to work not as an experiment, but as a true source of ongoing business value. Contact Ciklum to learn more about a successful approach to implementing and scaling AI as a product.