In our latest broadcast of The Digital Download, "Why Most Companies Get AI Wrong," we dove into the transformative world of AI and big data with our special guest, Evangelos Simoudis. Here are some key takeaways from the episode:
Technology First vs. Problem First Approach
Evangelos warned against focusing on AI as a technological solution in search of a problem. Instead, he emphasized a problem-first approach where businesses should clearly define the issue they aim to address with AI, ensuring the right application of technology to solve it.
The Importance of Corporate Culture in AI Implementation
Evangelos highlighted the crucial role of a supportive corporate culture when introducing AI. He discussed the need for a work environment where employees are open to collaborating with AI systems and are trained to critically assess AI output to maintain standards and accuracy.
Data Preparedness for Effective AI
Evangelos noted that many companies struggle with data readiness for AI applications. He explained that data needs to be correctly shaped and labeled specifically for generative AI, which differs from traditional data preparation for business intelligence.
The Evolving Skills and Roles in AI-driven Enterprises
Discussing the impact on talent, Evangelos emphasized the need for skilled professionals who can validate and ensure the quality of AI outputs. He pointed out that while AI can improve productivity, experienced personnel are essential to verify outputs and maintain a pipeline of developing talent.
The Financial Implications of AI Deployment
Evangelos addressed the underestimated costs associated with AI deployment. He stressed the importance of understanding the financial investment required for implementing AI, which involves not just the technology itself but also the necessary organizational adjustments, infrastructure, and ethical considerations.
You can listen to the episode here
You can watch the episode here
Beyond the Show: More Insights from our Guest
The Mistakes Businesses Make When Implementing AI
by Evangelos Simoudis, Managing Director, Synapse Partners, esimoudis@synapsepartners.co
AI's allure is undeniable, and businesses invest heavily in its promise. Bain & Company reports that companies today invest an average of $5 million annually into generative AI initiatives, with some even exceeding $10 million. This pursuit is fueled by AI's potential to improve productivity, cut costs, and unlock new opportunities that may create new revenue streams. However, many companies find the path to successful AI challenging because of fundamental mistakes in their approach.
Klarna, a Swedish fintech company, made headlines in 2024 by replacing a significant portion of its customer support staff with an AI-powered chatbot. This move aimed to reduce costs and enhance the customer experience. Eventually, Klarna rehired human representatives, acknowledging that even though the AI technology was meeting its organizational needs, it couldn’t meet customer expectations. Klarna’s example and our firm's corporate advisory work show that as companies rush into AI, they must consider four dimensions: corporate needs, organizational capabilities, customer expectations, and value realization. Corporations make nine mistakes along these four dimensions.
Corporate Needs
Technology-First Approach: Many companies begin their AI journey by acquiring powerful AI tools without first identifying the strategic problems they must solve. This technology-first mindset often leads to misaligned investments and disappointing outcomes. Bain's research highlights that only 35% of companies have a clearly defined vision for creating business value from AI.
Underestimating Costs: Companies frequently underestimate the true costs associated with AI deployment. According to Deloitte, 78% of surveyed companies expect to increase their AI spending next year, as many realize that scaling AI requires substantial investments in data infrastructure, workforce upskilling, and governance frameworks. Depending on the problems they are trying to solve and the infrastructures they plan to use, such as API calls to frontier models or fine-tuning open-source large language models (LLMs), such increases may still prove inadequate.
Organizational Capabilities
Data Deficiencies: Poor data quality is a pervasive challenge. Companies struggle with inconsistent, inaccurate, and disconnected data, limiting the effectiveness of AI systems. Improved data management is a top priority for successful AI model development and deployment.
Talent Gap: Companies often lack the technical talent required to build, test, manage, and bring to market AI systems effectively. According to BCG, only 4% of companies have the capabilities required to generate substantial AI value at scale. Firms that invest in upskilling employees and hiring AI specialists are more likely to succeed.
Overreliance on Existing Organizational Structures: Traditional corporate hierarchies often struggle to accommodate AI's iterative and experimental nature where person and machine collaborate to solve both hard and mundane problems. Companies that adopt agile methods, and flatter organizations that focus on cross-functional collaboration, achieve better outcomes.
Cultural Resistance: Employee resistance to AI adoption is common, especially when automation disrupts established workflows. Successful companies invest in trust-building, emphasizing AI's role as an augmentation tool rather than a replacement.
Neglecting Change Management: Organizations frequently overlook the effort required to embed AI into daily operations. Without clear change management initiatives, AI projects risk becoming isolated pilot programs that fail to scale effectively.
Customer Expectations
Failure to Align with Customer Preferences: Companies often deploy AI solutions that focus solely on cost reduction or internal productivity improvements without considering how these changes affect customer satisfaction. As seen with Klarna, even technically successful AI systems can fail if they do not meet customer expectations for responsiveness, empathy, or quality of service.
Over-Automation Without Human Support: Companies sometimes replace too many human interactions with AI systems, reducing the quality of customer engagement, and overlooking customer expectations and the quality of the resulting customer experience. Successful AI deployment balances automation with meaningful human interaction, ensuring customer trust and satisfaction are maintained.
Value Realization
Ethical Blind Spots: Companies that ignore AI's ethical risks, such as algorithmic bias and privacy concerns, face reputational and regulatory risks. BCG emphasizes that leaders excel by prioritizing AI transparency, performance monitoring, and explainability to ensure models adapt appropriately to changing conditions.
Data Security and Privacy Concerns: Given AI's reliance on data, businesses must secure sensitive information to maintain customer trust. Data security remains one of the top barriers to scaling AI solutions.
The Path to AI Success
To succeed with AI, businesses must align AI initiatives with strategic goals, invest in high-quality data infrastructure, upskill their workforce, and implement robust governance frameworks. Companies that prioritize these steps will be better positioned to unlock AI's transformative potential while avoiding the common pitfalls that have derailed so many others.
Next time on the Digital Download
Why Kindness is Crucial to Success in Business and Life with Special Guest Sarah Browning
This week on The Digital Download, we're diving deep into the transformative power of kindness in both our professional and personal lives with our special guest, Sarah Browning. As the founder of Time for Kindness and a seasoned communications professional, Sarah brings a wealth of knowledge and experience to the table. Her work with organizations and businesses to foster a culture of kindness has made a significant impact.
Join us as we discuss questions like:
How does kindness impact leadership effectiveness?
Can kindness be taught, or is it an inherent trait?
How do you measure the ROI of kindness initiatives?
What are the common misconceptions about kindness in the workplace?
How can kindness be integrated into company culture?
Sarah's insights into the power of kindness will challenge your assumptions and inspire you to embrace a new approach to success. As she puts it, "When you see the kindness in the world around you, you feel positive and hopeful."
We strive to make The Digital Download an interactive experience. Bring your questions. Bring your insights. Audience participation is highly encouraged!
Join us live! Friday, March 14, 2025, 13:oo GMT / 09:00 ET
#AI #BigData #Innovation #SocialSelling #DigitalSelling #SocialEnablement #LinkedInLive #Podcast