The webinar featured Tobias Haustein from Amidiro, an expert in AI and digital transformation, along with Dumitru Pascu, CTO at mindit.io. They discussed strategies and approaches for adopting AI in business, including assessing the pace of AI development, evaluating potential productivity gains, and determining appropriate levels of AI adoption.
The speakers outlined an AI strategy framework called the AI Strategy Canvas to guide organizations in crafting effective AI strategies aligned to their business goals. They also covered quick wins with retrieval augmented generation (RAG) systems and gave a demo of an AI assistant built with RAG.
Overall, the webinar provided practical insights into leveraging AI strategically to drive business value. It highlighted key considerations around AI adoption and strategy development for organizations looking to integrate AI into their operations and processes.
AI Adoption Pace
Artificial intelligence is not a new technology, having been invented over 70 years ago. However, progress was slow for many decades, with AI taking 13-17 years to become proficient at tasks like handwriting recognition and speech recognition.
Recently, the pace of AI development has accelerated dramatically with new approaches like machine learning and deep learning. In the past, AI took decades to achieve human-level performance on specialized tasks. But with modern techniques like deep neural networks, AI can now surpass human capabilities in some areas in a matter of months.
For example, AI speech recognition went from below 50% accuracy in 2009 to surpassing human capabilities by 2018. And in image recognition, AI matched humans in 2015 and now far exceeds human performance. The pace of AI progress shows no signs of slowing down.
AI Productivity Gains
Recent advancements in artificial intelligence, especially deep learning, have enabled AI systems to match or surpass human capabilities on many tasks. This is leading to significant productivity gains for companies that implement AI solutions.
According to research from McKinsey, the use of AI in customer service can boost efficiency by up to 15%. In legal services involving document review, AI can improve efficiency by around 25%. Even greater gains are seen in software development, where some companies report cutting their development efforts in half with the help of AI tools.
The key insight is that AI allows automation of tasks that previously required human judgement and discretion. For repetitive and rules-based work, AI systems can take over tasks completely. For more complex work, AI augments human capabilities, allowing people to focus on higher-value activities.
This combination of full automation and augmentation leads to major productivity improvements. While overall EU productivity gains have averaged around 1% per year, adopters of AI are seeing boosts of 15% or more in affected business areas.
The lesson is clear – companies that fail to adopt AI risk falling behind competitors who are realizing major efficiency improvements. To remain competitive, investing in AI automation and augmentation is becoming essential across many industries.
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Reasons to Use AI
Adopting AI can provide numerous benefits for businesses such as gaining a competitive advantage, increasing efficiencies, and enabling innovation.
Competitive Advantage
Using AI can provide a significant competitive edge against companies that don’t utilize it. AI-powered solutions allow businesses to analyze data, automate processes, and make predictions in ways that weren’t possible before. This can lead to better and faster decision making, improved products and services, and the ability to operate more efficiently. Businesses that fail to adopt AI risk falling behind competitors who are leveraging its capabilities.
Efficiencies
AI can help drive major productivity gains and cost savings. According to research, using AI for customer service can boost efficiency by 15%, while it can reduce legal review costs by 25%. In software development, AI tools have been shown to reduce development efforts by up to 50%. By automating repetitive and routine tasks, AI frees up employees to focus on higher value work. It also reduces human error rates.
Innovation
AI empowers businesses to create innovative new products, services, and ways of operating. For example, Spotify utilized AI to power personalized recommendations, which helped it surpass giants like Apple and Amazon in the music streaming industry. Companies can leverage AI to uncover insights in data, predict trends, and generate new ideas that humans alone may have never uncovered. This sparks innovation and disruption.
Levels of AI Adoption
There are five levels of AI adoption that businesses should evaluate to determine their ambition with AI:
AI Non-Users
These businesses do not see the benefits of AI and have no plans to utilize it. They are avoiding investment in AI technologies.
AI Consumers
These businesses use AI tools provided by vendors but do not heavily invest in custom AI development. They utilize ready-made AI as it becomes available.
AI Automators
These businesses embrace AI automation, investing in customized AI tools. They mix self-developed systems with ready-made AI capabilities.
AI Builders
These businesses develop new AI-based solutions, heavily customizing models for their needs. They aim to innovate and transform processes with AI.
AI Researchers
These pioneering businesses invent new types of AI, leading advancements in AI research and development. They focus on pushing the boundaries of what’s possible with AI.
Most businesses today are AI Non-Users or Consumers. However, to fully leverage AI’s benefits, companies should aim to reach Automator or Builder level adoption. This requires strategic planning and investment in developing custom AI capabilities.
Developing an AI Strategy
Developing an effective AI strategy involves establishing strong foundations, enabling key drivers, and focusing on strategic pillars.
Foundations
The foundations of an AI strategy include:
- Awareness – Educating employees about AI capabilities and limitations. Training key staff on AI fundamentals.
- Talent – Hiring data scientists and engineers with AI expertise. Upskilling current staff through training programs.
- Governance – Implementing policies for ethical AI development and use. Ensuring alignment with company values.
Drivers
The drivers that power an AI strategy are:
- Technology – Having the right infrastructure (cloud, on-prem, hybrid) and tools for developing, deploying and managing AI systems.
- Data – Building pipelines for collecting, cleaning, labeling and storing training data. Securing access to relevant external data sources.
Pillars
The core pillars of an AI strategy include:
- Everyday AI – Using AI to enhance existing business processes and workflows. Focus on efficiency and competitive parity.
- Game changing AI – Developing innovative AI solutions to disrupt markets and create competitive advantage.
- Cover Your Back – Preparing for industry disruption from competitors’ AI innovations. Ensuring business resilience.
AI Strategy Canvas
The AI Strategy Canvas is a visual framework that helps businesses develop and communicate their AI strategy across the organization. It was created by the company Amidiro and provides a structured way to define the key elements of an AI strategy.
The canvas helps teams align on the foundations, drivers and strategic pillars when formulating an AI strategy. It provides a shared framework to discuss how AI can create business value. The visual design enables executives and stakeholders to easily understand the key components of an AI strategy at a glance.
Overall, the AI Strategy Canvas gives organizations a structured approach to define their AI vision, assess capabilities, and prioritize investments. It is a useful strategic planning tool to drive AI adoption. The canvas methodology facilitates alignment and brings focus to AI programs.
Business Impact
AI has immense potential to transform businesses and entire industries. Adopting AI can lead to major competitive advantages and disruptive innovations. Here are some of the key business impacts of AI:
Increased productivity and efficiency – By automating manual and repetitive tasks, AI can significantly boost productivity. Studies show efficiency gains of 15-50% in areas like customer service, legal work, and software development.
Higher quality outputs – AI systems can produce work of equal or even higher quality compared to humans for certain tasks. For example, AI-written content can be more engaging and aligned with SEO best practices.
Personalization at scale – With AI, companies can offer hyper-personalized experiences by understanding each customer’s preferences and needs. The music service Spotify used AI to drive recommendations tuned to individual tastes.
New innovative products – AI expands what’s possible, enabling businesses to create new offerings. For example, chatbots and virtual assistants use NLP to deliver helpful automated conversations.
Disruption of incumbents – Small companies can use AI to compete with larger players, as evidenced by Spotify surpassing giants like Apple Music. AI is a potential equalizer.
Transformation of business models – AI doesn’t just improve existing processes, but can facilitate entirely new ways of doing business. Autonomous vehicles could displace individual car ownership.
The transformative potential of AI means companies ignore it at their peril. To remain competitive, businesses should view AI both as a driver of efficiency and an enabler of innovation.
Getting Started with AI
There are two main approaches for getting started with AI – using readymade solutions or building custom solutions.
Readymade AI solutions involve utilizing pre-built models and tools provided by vendors like Google, Microsoft, and Amazon. The main advantage is that they require minimal setup and are quick to implement. Readymade solutions like chatbots, computer vision APIs, and natural language processing tools can be easily integrated into existing applications. The downside is that they offer limited customization and may not fit your specific use case.
Custom AI solutions are tailored to your unique requirements. This involves training and fine-tuning models on your own data. While more complex, custom solutions can provide capabilities beyond what readymade options offer. Key steps for building custom AI include:
- Collecting quality training data specific to your problem
- Selecting the right model architecture and framework like TensorFlow or PyTorch
- Training and iteratively improving the model on your data
- Deploying the model to production and monitoring its performance
- Maintaining and updating the model as new data comes in
The choice between readymade vs custom AI depends on your needs, resources, and capabilities. Readymade solutions are ideal for common use cases or initial experimentation. Custom solutions make sense for advanced requirements where readymade options fall short. Assessing your long-term strategy and data assets can help determine the right approach.
In summary, AI is developing at an unprecedented pace, with capabilities doubling in mere months compared to the 1% annual productivity gains of humans. Businesses should take advantage of AI to boost efficiency, quality, and innovation.
To successfully adopt AI, organizations must set clear strategic goals, build the proper foundations of awareness, talent and governance, and leverage the key drivers of technology and data. The AI Strategy Canvas provides a framework to define ambitions, pillars and enablers.
Starting with ready-made tools and models allows quick implementation for “everyday AI” gains. But customizing AI with proprietary data and models unlocks “gamechanging AI”, with the potential to transform industries. Firms must also “cover their back” against competitors.
With a thoughtful strategy and approach, companies can harness AI as a competitive advantage. The key is matching AI initiatives to strategic business priorities. Done right, AI can drive step-change productivity, delight customers in new ways, and future-proof organizations.
The Definitive Guide to AI Strategy Rollout in Enterprise Whitepaper
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