Generative artificial intelligence (AI) has emerged as a pivotal topic for businesses, with a growing number actively exploring its implementation. Currently, a significant majority of companies are engaged in discussions on how to harness generative AI, with many forming dedicated teams and allocating budgets for its development. Nearly half of these businesses plan to integrate generative AI into their product or service development strategies within the year.
(Source: Capgemini and McKinsey)
Learnings from Generative AI Adoption
While 70% of executives acknowledge the significant potential of generative AI to add value to IT functions and drive innovation across various areas, realizing these benefits remains challenging. According to studies from Stanford and MIT, improvements from AI implementation across industries could range from 7% to 9% over the next three years.
At Multiplica, we’ve collaborated with clients on projects focused on maximizing AI utilization, yielding positive initial results and enhancing our understanding of successful use cases. From this experience, three key learnings have emerged:
1. The hype surrounding AI is diminishing, leading to a lack of clarity about its current commercial value.
2. Early adopters stand to reap the greatest benefits by overcoming the learning curve ahead of others.
3. Initiating awareness and education among employees and stakeholders about AI capabilities and implications is crucial.
Identifying the 8 Common Pains in Implementing Generative AI:
In our experience, we’ve identified eight key pains organizations encounter when implementing generative AI:
1) Shortage of technical expertise: A lack of qualified personnel in AI poses a significant challenge, limiting effective implementation of AI solutions.
2) Outdated technological infrastructure: The use of obsolete hardware or incompatible platforms can severely hinder integration and optimal functioning of AI solutions.
3) Insufficient quality and quantity of data: AI model performance heavily depends on the availability of high-quality data in sufficient volume for training.
4) Unrealistic expectations: There’s a risk of expecting immediate results or overestimating current AI capabilities, leading to disappointment and underutilization of solutions.
5) Organizational change resistance: Employee and managerial reluctance to adopt new technologies may stem from fear of job obsolescence or distrust in AI.
6) Ethical and legal challenges: Concerns about privacy, algorithmic bias, and compliance with regulations present significant ethical and legal hurdles.
7) Cybersecurity vulnerabilities: Safeguarding AI systems against unauthorized access and cyber attacks is essential but poses a complex challenge in protecting these systems.
8) Constant maintenance and updates: Continuous monitoring, periodic adjustments, and updates are necessary to maintain relevance and effectiveness of AI models, requiring dedicated management and resources.
Navigating New Challenges Ahead:
As generative AI continues to mature, organizations will face several emerging challenges alongside existing pains:
1) Talent and Skills Management: The complexity of AI systems will drive a heightened demand for skilled professionals who can build, maintain, and oversee these technologies.
2) Adaptation to Regulation: The increasing adoption of AI is likely to spur global regulatory changes that address ethical, privacy, and algorithmic bias concerns, necessitating adjustments for compliance.
3) Security and Privacy: With cyberattacks growing in sophistication, posing significant risks to AI-based systems, companies must bolster their protection of data and critical infrastructure against these evolving threats.
Steps to understand your company’s maturity and address generative AI implementation:
Navigating this landscape can be daunting, given the multitude of variables and uncertainty about expected outcomes. Therefore, it’s essential to focus on establishing a clear methodology for discovering and prioritizing initiatives. This will enable the development of an implementation plan with well-defined objectives.
Here are seven steps that can serve as a guide:
1) Familiarize yourself with AI: Utilize available online resources and training to understand AI fundamentals and invest in the best training for yourself and your team to ensure you have the necessary talent for AI implementation.
2) Identify problems AI can solve: Reflect on integrating AI capabilities into your current products and services, focusing on specific use cases where AI can solve business problems or provide tangible value.
3) Prioritize concrete value: Evaluate the potential business and financial value of various AI implementations you’ve identified. Instead of getting lost in theoretical discussions, directly link your initiatives to business value.
4) Acknowledge internal limitations: Understand the gap between your aspirations and what your organization can realistically achieve within a set timeframe. This understanding will help establish achievable goals.
5) Implement a pilot project: Start with a small-scale approach and a Minimum Viable Product (MVP). Set clear objectives and consider your knowledge and limitations about AI. Collaborate with partners who can support you in this process.
6) Integrate your data: Before implementing ML in your business, you need to clean and prepare your data, avoiding the “garbage in, garbage out” problem.
7) Start gradually: Begin by applying AI to a small sample of your data. Use AI incrementally to demonstrate its value, gather feedback, and then expand your approach as appropriate.
Unlocking AI’s Full Potential in Your Company Today:
Beyond the hype that surrounded AI implementation in 2023, this year marks a period of maturity where we must consider the most effective way to implement these technologies in our company.
Some consultancies, such as McKinsey, already warn us of the gap that AI can create in performance between leading companies (those that incorporate AI tools into their organization over the next 5/7 years) and passive companies in their adoption. Leaders could potentially double their profits, while non-adopting companies could even decrease them with the same cost and revenue model. For this reason, we cannot waste time in getting to work to unlock the maximum potential of this technology.
(Source: McKinsey)
Conclusion:
At Multiplica, we are ready to support you through our approach focused on assisting in the identification, design, and prioritization of efficient strategies, and supporting the development of MVPs with clear business objectives. Additionally, we train our clients’ teams in AI, ensuring transparency and fostering lasting trust relationships.
Ready to learn more about how we do it? Contact us today.