How to maximize your AI investments now and in the future

Shaky foundations

Yet, encouraging as these findings are, look beneath the surface and there is a warning to be found as well. While many leaders are successfully deploying AI for quick-win efficiency and productivity benefits, they risk overlooking the foundational infrastructure the technology needs to thrive in the long term. Without this strong foundation, enterprise-wide adoption will be difficult, and organizations may even see their investments begin cracking and crumbling beneath them.

One of the key issues here is a lack of data infrastructure. Shockingly, just 36% of senior leaders are investing in the quality, accessibility, and governance of their data at scale, meaning their AI is missing crucial information that would enable it to produce better, more accurate results.

Responsible deployment remains a concern, too. Yes, the vast majority of senior leaders acknowledge the importance of ethical AI usage, but only 34% say their organization is building a governance framework to address fundamental issues such as bias—a clear say-do gap, in other words.

Many firms have yet to figure out how they support the technology with a human workforce capable of maximizing its potential. Despite actively prioritizing the recruitment of AI knowledge workers, only 37% of leaders say they are training and upskilling existing employees on how to use the technology fully and at scale.

Three focus areas for AI centricity

How, then, do organizations overcome these challenges and become truly AI-centric both now and in the future? Here are three key areas for leaders to consider:

1. Don’t play whac-a-mole.

Many firms still tackle data on a project-by-project basis, solving issues of quality, accessibility, and lineage as and when they pop up. Yet, the problem with this “Whac-A-Mole” approach is that it creates an environment where data is everybody’s job—and nobody’s. This, in turn, can lead to inordinate amounts of money being spent on solutions that don’t generate any real, long-term value.

But if you treat data as a functional area with centralized processes, resources, and funding, it becomes far easier to invest in enterprise-wide tools and procedures that deliver a high level of data quality, accessibility, consistency, and governance.

2. Get ahead of AI regulation.

There have been lots of positive commitments made around ethical AI but without much in the way of real progress, because no one is telling organizations they must do it. Ultimately, success will require federal and/or state-level regulation but waiting for the government to act carries its own operational and reputational risks.

Imagine, for example, your company experiences a reputational issue due to a bias in your AI that is disproportionately impacting minorities. Failing to set a solid framework now also means you will have nothing to build on when legislation inevitably arrives. It is far better (and cheaper) to establish clear best practices and guardrails ahead of time.

3. Buy, build, rent.

Every wave of new technology brings a need for new capabilities among human workers, too, and AI is no exception. Once you understand the job roles you need and the AI skills those roles require, it’s a good idea to adopt a buy-build-rent talent strategy. “Buy” means going out to market to hire, “build” means upskilling your existing employees, and “rent” means bringing in third-party providers to augment your workforce.

Ideally, this strategy should be managed centrally to mitigate the risk of having disparate programs being run within the firm. Otherwise, you may end up hiring a bunch of people you should have rented, renting a bunch of people you should have bought, and having a whole population of existing talent who aren’t being leveraged effectively.

Beyond the bottom line

None of the above is to undervalue the importance of continuing to develop and deploy AI solutions that boost efficiencies, increase worker productivity, and enhance customer satisfaction. They are already generating—and will continue to generate—tangible financial, operational, and reputational ROI.

But these solutions are also just the tip of the iceberg. As AI moves out of the hype phase, it offers leaders an opportunity to think beyond the bottom line and develop holistic strategies that positively impact the top line as well. From creating new business units, products, and market channels to finding better ways to interact and engage with customers, AI can help you reimagine your entire enterprise system and gain a competitive edge.

But to do so, it needs the right foundations behind it. Invest in getting your data infrastructure, governance, and talent strategy right today, and you will be well-placed to win tomorrow.