AI Success Depends on Ditching Perfection for ‘Good Enough”

As companies race to harness AI for employee productivity, a new IDC study reveals it’s just the start— with custom solutions and innovation driving ROI in customer engagement, growth, and beyond. Yet, success hinges on ditching perfectionism, balancing metrics with value creation, and tackling the looming skills gap to future-proof your workforce.

• Prioritize Productivity but Expand Horizons: While 92% of AI users focus on productivity for top ROI, companies should also leverage AI for customer engagement, growth, cost management, and innovation, expecting high impact across these areas in the next two years.
• Embrace Custom AI for Maturity: Building tailored solutions like Siemens’ Industrial Copilot addresses industry challenges such as complexity and labor shortages, boosting efficiency for over 50 customers—signaling a shift from basic to advanced AI applications.
• Measure Smartly and Skill Up: Avoid over-focusing on cost reduction or perfectionism; instead, use ongoing feedback for value creation and strategic advantages, while addressing the critical talent shortage where 30% lack AI skills and leaders worry about filling roles.

Employee productivity is the No. 1 business outcome that companies are trying to achieve with AI, according to an IDC study published last year, commissioned by Microsoft. The study shows that 92% of AI users surveyed are using AI for productivity, and 43% say productivity use cases have provided the greatest ROI. While productivity is a top goal, generative AI use cases that are close behind include customer engagement, topline growth, cost management and product or service innovation — and nearly half of the companies surveyed expect AI to have a high degree of impact across all those areas over the next 24 months.

In the next few months, more companies expect to build custom AI solutions tailored directly to industry needs and business processes, including custom copilots and AI agents. This shows a growing maturity in AI fluency as companies realize the value of out-of-the-box use cases and expand to more advanced scenarios.

AI-Powered Solution A Gamechanger

The study highlights some case studies like Siemens which has developed the Siemens Industrial Copilot, which has eased the challenges caused by increasing complexity and labor shortages for dozens of customers in different industries. The document quotes Boris Scharinger, AI Strategist at Siemens Digital Industries: “In full appreciation of GenAI’s transformational potential, it’s important to remember that production does not have an ‘undo’ button. It takes diligence and effort to mature AI to industrial-grade quality. The Siemens Industrial Copilot for Engineering significantly eases our customers’ workload and addresses the pressing challenges of skill shortages and increasing complexity in industrial automation. This AI-powered solution is a game-changer for our industry with over 50 customers already using it to boost efficiency and tackle labor shortages.”

Experts advise companies to avoid focussing too much on cost reduction as a metric of AI success. Instead they should consider balancing efficiency metrics with new value creation. The attention should be on critical benefits like improved decision quality and customer experience, rather than trying to look for quantifiable outputs. Often the softer benefits are more significant than hard metrics.

When Good Enough is Perfect

Companies using AI are often hobbled by Perfection Analysis. It is like getting stuck trying to make something absolutely perfect, even when “good enough” would do the job just fine. In the world of AI and tech projects, it means wasting time, money, and effort chasing tiny improvements that don’t actually help the business. To maximize AI ROI, organizations should view measurement not as a final evaluation but as an ongoing feedback loop that drives continuous improvement, resource allocation and identification of new opportunities for value creation.

Companies which have been able to achieve RoI from their AI investments are often found to follow a multi-dimensional approach that reveals AI’s complete value story by connecting immediate cost savings to long-term competitive positioning in a way that resonates with both technical teams and executive leadership. Strategic advantages such as Long-term benefits such as competitive differentiation, innovation acceleration, and organizational capability building that position the company for future success.

Looking ahead: Skilling remains a top challenge.

Thirty percent of respondents indicated a lack of specialized AI skills in-house, and 26 percent say they lack employees with the skills needed to learn and work with AI. While AI and job loss are top of mind for many, the data offers a more nuanced view—one with a hidden talent shortage, employees itching for a career change, and massive opportunity for those willing to skill up on AI.  A 2024 Work Trend Index Annual Report from Microsoft and LinkedIn finds that the majority (55%) of leaders say they’re concerned about having enough talent to fill roles in the year ahead. These leaders sit across functions, but the number jumps to 60% or higher for those in cybersecurity, engineering, and creative design.