It’s all over the news – excessive investments in AI and the disappointing return on investment (ROI) in terms of either new revenue uplifts or operating cost reductions.
This bleak reality does not have to be the new normal.
Although Gartner Group and many others are projecting that we are entering the trough of disillusionment, this does not have to be the reality for your organization.
We are where we are at this moment in time because too many humans are not advancing integrated design thinking into their AI solutioning plans.
AI is not a short-term game. It should not be viewed as a plug-and-play technology. It should be viewed as an asset in a nurturing and sustaining life cycle operating model.
Business users need to learn the language of AI, like they had to learn the language of finance to be a leader. AI cannot be relegated to technology leaders to lead. AI is a team sport where everyone needs the foundational muscle to apply with confidence.
Jamie Dimon, Chairman and CEO of JP Morgan Chase, said recently, “We took AI and data out of the technology organization. It’s too important, and while technology remains a deep partner, we put AI at the management table. Are we doing enough? Are we doing it right? There will be no job, no process, no function that won’t be affected by AI — mostly for the positive. It’s about getting all the people who run these businesses to understand the power of it.”
Although AI models can be trained and deployed relatively quickly, the hard work is ensuring the outputs are actionable and adopted, and getting everyone behind the possibility and promise of AI is needed to transform and evolve an organization – especially those rooted in rigid legacy operations.
Without a human-centric design approach, AI solutions risk being underutilized because the context of how they are currently working is not aligned with the new ways of working.
This is the biggest gap we consistently see in organizations. Insufficient change management expertise in design thinking is the #1 leadership gap impacting AI value realization.
As a result of not applying integrated design thinking, there is often tremendous miscommunication, which leads to poor decisions and misalignment with the real jobs to be done.
Most engineering teams developing AI systems are not applying robust design thinking to ensure the AI solutions are built with empathy, explainability, and transparency at all stages of the design process.
My entire career has been advancing complex business changes and aligning new technologies and digital solutions. I have been in the trenches and have learned that behavior change is required, and it is always the most complex to rethink.
AI inevitably changes job roles, workflow, and decision-making processes yet we don’t often dig deep enough to ensure all the communication practices evolve with the toolkits. How does the management review discussions shift from new insights? What are the new questions that are asked? Are these the right questions to ask? How do we ensure all levels of the organization understand the new process and what’s expected of them?
All these changes and integrated design foundations are key for AI execution success.
In our company, we provide sales teams with getting more insights from leveraging AI-generated next-step best action recommendations to increase their odds of success.
Although AI solutioning can be complex at times, it is not the hard part. The hard part is securing adoption to shift how work is really being done.
A CRO that is used to a structured forecasting review process that drills the sales professionals to filter out deals with lower odds of closing relies a great deal on experience and intuition, and these reviews can take a week of productivity out of an organization, as they are all in the forecasting spin/rinse/repeat cycle.
We know from our experience that AI-driven forecasting software solutions, when aligned with behavior changes and new KPIs, offer a very positive ROI. We have seen our customers reach over 95% predictive accuracy — but this only happens with us ensuring data is complete and relevant to the AI model use case. Narrow AI models is an area where agentic AI will advance rapidly – but again, we must ensure data is valid or major risks will be incurred.
The change management foundations are what prevent organizations from realizing the ROI and value from their AI investments. This is often the case with transformative technologies. Organizations need robust change management frameworks to ensure adoption by ensuring value is being communicated, addressing fears or resistance, training employees effectively, and monitoring and iterating on the usage. AI is a journey of course corrections – like tacking on a sailboat – as the model or business context shifts, the change methods either need to scale up or down.
Without strong change management operating practices and processes, ROI will fall far short, even if the AI technically works.
The other major gap is that many organizations do not bridge the gap between technology and business outcomes
Many organizations deploy AI successfully in technical terms but fail to measure business KPIs or align AI with strategic goals
Integrated design thinking ensures: the right problem is solved, the AI solution fits into existing or optimized processes, and that the right key stakeholders are involved from the start. This alignment accelerates measurable value realization.
AI systems also require constant iteration. Design thinking and change management create a feedback culture: users provide insights to refine models, adoption hurdles are identified early and ROI is tracked and improved continuously
To be successful, AI must become a sustaining operating process that needs care and feeding; it must be viewed as an asset with continuity, vs a one-off project.
AI projects are about people, not just algorithms. Without integrated design thinking and change management, adoption stalls, value is unrealized, and the risk of resistance and mistrust increases.
With integrated design thinking, value is realized and adoption is achieved.
Bottom line: Design driven companies that integrate design as a core AI Strategy will outperform their peers. Studies by McKinsey and the Design Management Institute found that top-quartile design performers achieved 32% higher revenue growth and a 56% higher total return to shareholders over a five-year period.
The only pathway forward to accelerate AI value realization is to apply robust design thinking.
Contextual Engineering is an emerging role where humans and machines come together to co-create and collaborate.
We live in an incredibly exciting period, and I am optimistic that more leaders will internalize that their skills mix is wrong and start to fix what is so inherently clear to so many of us, AI Transformation leader.
Simply put, people really matter!
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