Moore’s Law says our processing power doubles every two years, without affecting cost. This exponential efficiency gain continues to power digital innovation, and the AI tools at our disposal are expanding in scope and capability at a dizzying pace.
The ability of new generative transformer models to emulate human creativity has our attention as leaders and managers. Harnessing their power in a practical fashion is keeping decision-makers up at night, but we’ve been coexisting with AI for quite some time now. My focus rests squarely on helping organizations solve problems and innovate more effectively. Offloading tasks to AI is important for reducing operating costs, but a more relevant question is, “How can team members partner with AI tools to make even better solutions for our customers?” Empowering team members to maximize their creativity and use resources efficiently is where the rubber meets the road.
Leaders shouldn’t be surprised that teams have been leveraging AI tools for decades. Adaptive algorithms and predictive analytics embedded in structural analysis programs have enabled product shape optimization since the early 2000s. By 2015, growth patterns of organisms had been codified into rules and suggested new directions for topological shape optimization. The algorithms and analytics would probe and test solution areas, and expand around the problem margins that the human engineer had defined.
Today, we are surrounded by generative AI tools trained on massive datasets. New tools are so impressive in emulating human behavior and creativity that we don’t even perceive the old tools as AI. That’s good! When we forget a tool is AI-powered, we’ve probably integrated it correctly into our organization. New technology may require new adaptations, but we’ve leveraged AI for decades, and many of those lessons are durable.
Let’s put the issue of human labor displacement aside. When a tool is powerful enough to mimic certain human tasks and replace labor, it will happen. In my world, this labor displacement allows humans to concentrate more effort on the edge cases of product or service development, the margin solutions.
Four Competencies
I believe this human-AI collaboration is where significant competitive advantage can be gained. Without proper team competency and ongoing stewardship of this collaboration boundary, AI tool adoption will fail to create a long-term advantage. Parity is built into the competitive equation unless the AI technology is a proprietary development or it makes significant use of a company’s proprietary data set.
These competencies help you to leverage your chosen AI “partner.”
Leverage Technology to Bridge Gaps: Understanding the technology behind AI tools goes beyond “Look what this tool is capable of!” Proper technology selection involves bridging an organization’s product or service strategy to a distinct technology value proposition that delivers a competitive advantage. Leaders must have a depth of technical understanding to do this successfully.
Alternatively, they can rely on fluent “translators” who join the tech wizards and the business strategists. Being able to speak both languages requires a distinct talent profile. Without this bridge, an organization won’t be able to differentiate competitively from others using the same tool. These technology translators may or may not be your scanners depending on capability and workload. Your scanners go beyond available technology evaluation. They have a deep grounding in your solution development processes and understand where product or service advantages can be gained through emerging technologies, or the use of AI beyond the scope of the vendor’s imagination. Better yet, several sets of eyeballs should be scanning the environment to help reduce bias risk.
Use Boundary Strategy: Create a fence around the current use of the AI tool. This does not mean it can’t expand in the future. Leadership, the team(s) using the tool, and any liaison managers must be in absolute harmony regarding what the tool does and how it is being applied to increase the value of the customer solution. Remove over-inflated expectations, and agree on how success will be measured. Equally important, ensure everyone agrees on what the tool can’t do. This iterates based on test and implementation.
Test and Implement Tools Based on your Context: Important in finalizing the initial Use Boundary Strategy and step #1 of extracting value with a tool.
- Select functional team members based on their knowledge or ability to be trained in the skills required to leverage the specific technology. AI tools have specific features and tendencies, and use skills aren’t necessarily generic. Every tool has boundary conditions that must be carefully controlled overtly or within prompt engineering to maximize utility. Know these specifications from the tool vendor and train them.
- Train on use cases specific to your organization. Team leaders or functional managers who are most fluent in success drivers related to your past customer solutions should guide this process. They must be activated as liaisons with the technology vendor’s training staff to align with the organization’s actual needs. Can they guide the training conversation rather than passively accepting the training assumed by the vendor?
- Prove your use case with parallel pilot efforts to reduce risk. Who is the arbitrator of validation and quality? Does a team review and judge its own success? Depending on the risk involved in the technology implementation, you’ll rely on the same team members or bring in an outside viewpoint. Chances are, you have this talent on staff. You created the measures in the Use Boundary Strategy. Who judges fairly and can broadcast the results freely?
Track, Evaluate, and Expand: The critical point of extracting long-term value from your AI tools. Reporting and verifying the utility of tool use is straightforward if the success measures were developed correctly, with specificity and fidelity. Choose team leaders or managers with the motivation and technical capability to continuously challenge the use of the boundary strategy. Remember, I’m talking about encouraging human/AI tool pairings to create more competitive solutions than either produce independently.
Provide the functional leader the safety to recommend expansion when a) the tool capability grows, b) the customer needs change, c) internal fluency increases, and/or d) results justify or enable different risk assumptions. They must be comfortable demanding quality and tying performance to measurables, but they must also be seen as champions for creative stretch.
They are constantly judging the AI solution output quality and risk vs. customer requirements but aren’t throttling its use. They are comfortable asking, “Can the tool help you expand your solutions if I relax our boundary case constraints here and here?”. They are also able to judge when dedicating some fraction of resources to unbounded experimentation is warranted.
Strategic deployment methods and team member capability matching demand renewed attention as AI technology evolves.
Focusing on the Human Touchpoint
In the product development world, where humans remain instrumental in pushing creative boundaries, the AI-human pairings have shown success with familiar guidance principles. Paired with AI tools, we’re asking team members for more creative stretch rather than less. Ensuring they’re prepared and appropriately supported is critical.
We’re already debating whether AI is making it more difficult for companies to negotiate a proper balance between incremental and radical innovation. That’s not what I’m interested in constraining. I fall back to the human touchpoint. Give the team members the knowledge required and support environment necessary to make that value decision between creative stretch and practical implementation every day. AI won’t do this for them, but it will help.
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