CEO of Fernweh Group, Chairman of Avail Infrastructure Solutions and Chairman of Ayna.AI.
Today, we stand on the cusp of the generative AI revolution. Throughout my long career in industrials, I have witnessed the profound transformations that new technologies have brought to the industrial sector.
What once was a stodgy and manually intensive industry has undergone an utter transformation—one that focuses on safety and efficiency. However, this transformation looks to pale in comparison to the changes that are set to take place.
While industrial goods and services has undergone a massive transformation, analysts from the World Economic Forum view that the sector lags and will continue to lag behind many other sectors on tech adoption.
Why Industrials Grapple with Technology Integration
In my experience, it’s not surprising that the industry struggles to implement the latest technologies. Industrials are often conglomerates that have grown as a result of acquisitions. As such, many have operations in multiple geographies with heavily decentralized businesses, poorly integrated IT assets and a shortage of processes and controls.
All of this has resulted in situations where firms tend to have poor actionable data, resulting in a lack of business intelligence. I’ve seen certain clients with 30-plus enterprise resource planning (ERP) systems, none of which can be reconciled with each other—certainly a situation set to frighten the bravest tech CEO.
The Promise Of AI In Industrials
Historically, companies have struggled to implement technologies without first overcoming these incredibly basic but challenging problems. This is where a critical opportunity opens up with generative AI. The mundane tasks that prevented tech adoption can be automated more efficiently through AI. From the commercial aspect, companies can massively scale sales and marketing campaigns.
For instance, companies can automate outreach, by creating personalized letters mentioning how their products can solve a particular issue for their prospective clients. On the manufacturing side, generative AI can improve standard operating procedures and ensure people are following the proper steps. Lastly, on the finance side, code generation could enhance performance visibility across business units by allowing disparate data sources to be integrated.
These are just a handful of the opportunities that are available for industrials. However, use cases should only accelerate in the coming months and years.
Five Steps To Implementing AI
While generative AI presents an opportunity for industrials to leap forward, significant risks are present. To succeed, here is a five-step methodology for management teams and investors to employ.
1. Prioritize use cases. For every case, it’s imperative the firm builds a business case, calculates an ROI and sets priorities based on this ranking.
2. Conduct a buy-versus-build analysis for each of the prioritized use cases. For each one, identify the pros and cons of building in-house and buying/outsourcing the infrastructure. I’ve found that a prudent approach is to start small. Leverage the vast start-up ecosystem. Then, as the company and technology scale up, you can bring more of the work in-house.
3. Ensure that your organization is ready. Often, we have found a lack of organizational capabilities and focus has prevented the full success. My experience suggests that success is ultimately highest with a “fail-fast” approach in which your company employs quick sprints, implementing a few use cases and applying lessons learned from the experiences. Additionally, to keep generative AI as an organizational priority, you can set up a dedicated generative AI team that leadership can keep accountable.
4. Adopt an outcome-based approach. In setting up performance measures for the team responsible for prioritized use cases, it’s wise to avoid measuring inputs and instead measure the achievement of the desired outcomes. Ask yourself: Did your team accomplish their required use cases?
5. Invest in the supporting data, analytics and digital infrastructure. Companies that proceed thoughtfully can position themselves to gain a real advantage and leap forward. Consider investing in a one-time effort at data cleansing and structuring. Then, for the use cases, your company can choose from two approaches: application-based investments (involving data lakes on top of existing systems) or systemwide changes.
In an industry that has historically lagged behind other sectors technologically, companies that are able to be fully cogent in their generative AI investments have an opportunity to gain a real advantage and leap forward against their competition.
Past performance is not indicative of future results. This information is not independently verified and should not be relied upon in making investment decisions or interpreted as investment advice.
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