The Zen of AI Innovation: How to bring order to your AI portfolio and build a solid AI roadmap in 3 easy steps

Innovate faster, more productively, and with greater confidence

AI is moving from experimentation to industrialization at a staggering speed. Many organizations are struggling with ‘cat herding’ disparate AI innovation initiatives into a cohesive whole and balancing short-term payoffs with long-term transformation.

Here are some useful tips & tricks to help you generate high-quality ideas, logically organize them into a well-balanced AI portfolio, and ultimately generate a coherent enterprise roadmap.

Step 1: Generate high-quality candidate ideas

Filling your AI innovation funnel with high-quality ideas is easier said than done. What’s a science experiment and what’s potentially the ‘next big thing?’ To get a holistic picture early on in the innovation lifecycle, I recommend the 4D Framework for vetting candidate ideas. The 4 Ds are Data, Dollars, Deployability, and Differentiation

Data — Lifeblood of all AI — Do you have data sets available, either as raw or already labeled or feature-engineered sets that can be used to train machine learning models? Is the data either readily understandable by data scientists or do you have subject matter experts that can provide this clarity for — often cryptically named — tables and columns? What are the regulatory constraints on data usage and will physical data gravity potentially become an issue for very large data sets?

Dollars — Business Benefits Matter —How will your AI solution produce benefits? Is it focused on operational expenses and bottom-line savings or are you targeting top-line growth? Can you tell a simple value chain story — for instance, an AI-powered screen pop system in the contact center will reduce average handle time by 2 minutes on 10,000 calls per day for a savings of $20,000 (assuming a $1/minute cost)? How will you instrument, measure, and dashboard the system to prove AI impact? If your value case depends on generating additional share of wallet or market, how will you design your A/B experiment to isolate AI impact and prove business benefits independently of many other confounding factors?

Deployability — Avoiding ‘stranded models’ — Think of your fantastic ML model like a high-performance race car engine. Certainly a feat of modern engineering, but utterly useless without the other bits that are required to win a race — including wheels, gas, a windshield, struts, brakes, a pit crew, etc. Early on, consider the downstream dependencies and other enterprise systems that have to be in place and ready for model outputs to produce benefits. Beyond technical integration, also ensure schedule alignment as upgrade or maintenance cycles for large enterprise systems are not always flexible enough to incorporate AI-driven changes.

Differentiation — To AI or not to AI— Avoid blind spots and AI-biased thinking by asking yourself early on if AI is indeed the right tool for the job. Maybe a different approach produces value more quickly? Consider alternatives like Robotic Process Automation (RPA), or even spreadsheet-based or rules-based solutions. Maybe there is an existing third-party vendor or legacy model that can get you 80% there. Sometimes it’s not a technology question at all but a matter of team building and organizational change to address the root cause of an issue rather than fixing it with high-tech after it’s already occurred.

Step 2: Get clarity about your innovation type

Great! Your brainstorming sessions have generated a large number of high-quality innovation candidates using the 4D approach. Now what? To further prioritize your ideas and shape them into a well-structured portfolio, I recommend the “innovation 2x2” approach below.

The dimensions are simple — capability and demand. Capability expresses your organization’s ability to execute and demand expresses the demand signal from internal and external customers. On both axes, values can range from ‘certain’ to ‘uncertain.’

We can now map innovative AI ideas into quadrants…from incremental to disruptive.

Let’s start with certain demand and certain capability. We know we can do it and we know our customers want it. This is where incremental innovation lives.

In AI terms, it’s typically a point solution that makes existing processes slightly better, like a more accurate OCR system based on neural networks, a better voice assistant, etc.

In the next quadrant, we look at certain demand encountering uncertain ability to deliver. It’s possible that AI capabilities required to meet customer demands are still emerging, or maybe they are and will remain outside of the strategic focus area for the enterprise or department. This type of innovation is best tackled with a competent partner.

Here we rely on outside parties to deliver AI innovation and systems — something that is very common in AI. An example might be a third-party AI chatbot vendor that delivers full end-to-end capabilities for designing, building and running cross-channel bots.

When our ability to execute is certain but is paired with uncertain customer demand, we can experiment with low risk. If it works out, great, if it doesn’t, no big deal. This is where fast win/fail innovation projects live.

In AI, this might be a better marketing targeting model based on innovative algorithmic approaches. The model can be readily A/B tested, if it works it delivers a lift in revenue, if it doesn’t, it can be turned off after running a thorough experiment.

And lastly, the area that is most often associated with innovation — truly disruptive innovation. We are not sure we can do it, and we are not sure customers want it, but if the bet pans out, it will deeply transform a process, sector, or a whole industry.

Truly safe and reliable self-driving cars would fall into this category. Similarly, a form of robust general artificial intelligence would certainly be extremely disruptive and transformative.

Step 3: Generate a sequenced roadmap

Now that we have a well-sorted portfolio full of viable ideas, what should we do first? By overlaying time horizons, we can generate a logical project roadmap to give us a good sense of near-term, medium-term and long-term execution priorities.

Note: Allocating ideas to Horizons 1, 2, and 3 as shown below works in many cases, but of course there will be exceptions where it’s perfectly reasonable to tackle a ‘partner’ or ‘disruptive’ project first. Consider the proposed sequence a rule of thumb with lots of wiggle room, not a law set in stone.

Horizon 1: Well-qualified 4D candidates that are incremental in nature can be prioritized into Horizon 1 initiatives. These typically deliver reliable and relatively short-term payoffs with business cases that are more readily explainable and accessible. An excellent way to ‘pay as you go’ in innovation and provide momentum for more aspirational and longer-term initiatives.

Horizon 2: Initiatives with a higher degree of uncertainty often fit into planning Horizon 2 as partner selection and vetting can be time-consuming. Fast win/fail experiments may in turn depend on having built a certain amount of credibility, especially if the experiment turns into a fast fail. This is why having a solid foundation of incremental wins in Horizon 1 and deferring these experiments Horizon 2 can be a more sustainable strategy.

Horizon 3: Lastly, the most uncertain of projects — that often also rely on a top-down business model and aim to unlock whole new segments and markets, are best served with a longer planning horizon. This allows an enterprise to build up experience at executing AI projects…an organizational ‘muscle memory’ so to speak, combined with lots of credibility earned with earlier Horizon 1 & 2 successes.

I hope that you will find the concepts and frameworks in this post useful when launching AI ideas in your own organization. To start, the 4Ds will boost cross-team collaboration, ideation speed, and productivity throughout the innovation journey. Next, the innovation portfolio view provides peace of mind by delivering a holistic perspective of diverse solution candidates and ensuring a balanced approach that fits your organization’s risk appetite. And together with the 4Ds and a solid portfolio view, structuring a long-term transformation journey into logical horizons allows you to achieve incremental goals without losing sight of the bigger picture.

Thank you for reading and I wish you great success, clarity, and ‘AI Zen’ on your own AI innovation journey!

Bjorn Austraat is SVP and Head of AI Acceleration at Truist and specializes in Agile AI at Enterprise Scale and Machine Learning and Deep Learning Innovation.