Strategic AI Integration and New Business Paradigms


One of the most significant shifts we’re witnessing is corporate organizations grappling with how artificial intelligence (AI) will affect their business and industries in the coming years.

At The Engineered Innovation Group (EIG), we understand the paramount importance of harnessing AI’s potential while preserving the invaluable human element within our organizations. It’s paramount that organizations understand the possibilities and limitations of the technology, the capabilities that are surfacing in the industry, and the new technologies anticipated in the coming months and years. In this article, we’ll delve into how we approach AI, people, and corporate innovation, offering insights gleaned from our experiences and interactions with customers, and from our EIG use cases.

Developing Strategies around AI

Embracing AI isn’t just about adopting the latest technology but fundamentally transforming how we operate and innovate. For innovators and business leaders, it’s crucial to approach AI strategy with a holistic perspective. This entails identifying areas within the organization where AI can enhance processes, drive efficiency, improve productivity, and unlock new growth opportunities. Fostering a culture of curiosity and experimentation, where teams are encouraged to explore AI’s potential and apply it creatively to solve business challenges, helps identify real use cases with real ROI.

In the 2010s, big data was the trend. Businesses were urged to amass extensive data in data lakes for future analysis. Many companies sit on untapped gold mines, with the full extent of potential opportunities often remaining unrecognized.

Jake Miller, CEO, The Engineered Innovation Group

The power of large language models lies in their ability to unveil an array of new use cases. The data that has been dormant in these extensive pools can now be harnessed effectively. Those who learn how to unlock this potential can transform all aspects of their business, from sales to marketing, product development to customer success. New revenue streams will percolate and manifest, helping executives become heroes and drive growth.

In a study commissioned by Salesforce in 2021, over 90% of respondents said they see an increased demand for automation. Unsurprisingly, executives are feeling more pressure to do more with less — and automation is the perfect solution to achieve this.

Hyper-automation and inference-based learning through generative AI represent a new frontier that warrants exploration. This area offers significant opportunities to streamline complex processes and enhance decision-making that can transform your company.

Experimentation and Incremental Progress

It’s easy to get caught up in a grand vision only to encounter obstacles. Unfortunately, all too often champions of innovation often get shut down and discouraged when the typical corporate nonsense foils their plans. Not to mention, the classic innovator’s dilemma is real. We often hear leaders say they think AI is a fad and hype and will soon fade away. This is short-sighted. 

These same leaders are grappling with ever-dwindling budgets to explore opportunities that don’t have a clear path to ROI. The reality is that organizations can conduct experiments cost-effectively by concentrating on one or two specific use cases. The key is to limit the number of cross-team interspecies at all costs. The fewer people that have to be involved, the higher the likelihood an experiment will succeed. Once a concept is validated in bite-sized chunks, the success can be socialized, and folks will be more likely to find paths to adopt the solutions.

Corporate momentum is real, yet it’s often hindered by internal bureaucracy, misalignment of business objectives, and a fear of prematurely adopting technology. However, organizations that adopt a “wait and see” attitude will miss out.

Interestingly, a study by Techpoint for AI in the Indiana Workforce revealed that nearly 50% of organizations are prioritizing their AI investments in employee productivity, whereas 27% are focusing on enhancing their products and services with AI — a reality that may seem counterintuitive.

Engineer Innovation

Innovation can be systematically fostered. This means that company teams can adopt a strategic approach to exploring a harlequin of possibilities. Once a broad spectrum of ideas is generated, they should narrow their focus and design quick, low-cost experiments involving as few parties as possible to avoid inter-team dependencies during building and implementation.

Expect and embrace failure. By conducting small-scale experiments, enterprises can utilize the power law distribution principle from the VC playbook to evaluate the impact of new and emerging technologies on productivity. This approach aims to pinpoint a few high-impact solutions that could significantly boost business efficiency.

There is no replacement for action. While plans on paper are essential, the true insights emerge during the development and piloting of solutions.

We stand on the brink of an unprecedented population explosion, not in human numbers but in the realm of intelligent entities.

Humans and Digital Counterparts

We stand on the brink of an unprecedented population explosion, not in human numbers but in the realm of intelligent entities. Consider the hypothetical scenario where the global count of intelligent beings soars from 7.8 billion to 32 billion within a mere five years. While this is an exaggeration, it serves to underscore the transformative potential of digital agents and colleagues.

For now, it is imperative that leaders empower their teams and adopt both a top-down and bottom-up approach to mastering collaboration with AI counterparts.

These digital entities can serve as co-pilots for software developers, brainstorming allies for marketers, and seemingly omniscient agents — experts on cross-corporate topics that identify and address issues before they escalate into crises.

We must also recalibrate our understanding of computing. For over 40 years, people have been conditioned to view computers as deterministic: input leads to a predictable, structured, and repeatable output. This perception reflects the historical nature of computing — rule-based systems created by “if this, then that” programming.

The rise of non-deterministic, inference-based programs demands a reevaluation of our interaction with technology. Machines are no longer passive tools but active participants, offering unique insights and significantly enhancing human intelligence and productivity.

Corporate Innovation and Change

Corporations are poised to face significant challenges from their more agile startup counterparts. The capacity of startups to explore new territories and outmaneuver their larger competitors is expected to increase. Mastery of programming languages is no longer a prerequisite.

Corporations can mitigate this disadvantage by adopting a new paradigm in human-computer interaction (HCI) to capitalize on technological advancements. We observe a shift in HCI towards more natural interactions, where conversing with a computer supersedes traditional button, grid, and textbox interfaces. This allows for the rapid development of valuable applications through chat interfaces, eliminating the need for screen design and time-consuming UX. Instead, the focus shifts to building datasets, retrieval-argument-generation (RAG) systems, and leveraging large language models (LLMs), enabling teams to concentrate on solving business problems without the necessity of defining and constructing a complex and robust UX.

To conclude, corporate innovation must embrace incremental experimentation. This involves micro-funding projects and formulating precise plans.

To sum it up. Stop talking. Start doing.

Jake Miller is CEO at The Engineered Innovation Group which helps companies of all sizes—from startups to industry giants—build high-functioning software products, engineering processes, and teams.