Dan Stroot

Artificial Intelligence Is Not a Strategy

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9 min read

How should a senior Technology Leader help their organization adopt and scale AI? Navigating this new landscape takes more than building a few ML models; it calls for strategic and human-centric leadership, clear vision, and the ability to manage teams effectively.

If you read the news covering artificial intelligence (AI) developments on any given day, you may feel pangs of dread. It seems like the technology has evolved so rapidly that it will be difficult to "catch up", let alone "keep up". However, the key is not merely to "keep up," but to strategically integrate AI in ways that enhance efficiency, innovation, and overall competitiveness.


1Contextualizing AI into your business and the environment today
2Finding your highest opportunity AI use cases
3Methods to evaluate and prioritize use cases
4AI examples for enterprise and personal productivity
5Real-world tactics of ML execution, including business trade-offs
6Change management for AI
7Responsible AI deployment and risk mitigation
8How building responsible AI can enhance customer trust and value
9AI tools and resources
1Contextualizing AI
2Navigating the Ecosystem
3Community is Everything
4Tooling Proficiency
5Data Proficiency
6Language Proficiency

Contextualizing AI

If you read the news covering artificial intelligence (AI) developments on any given day, you may feel pangs of dread. It seems like the technology has evolved so rapidly that it will be difficult to "catch up", let alone "keep up". However, the key is not merely to "keep up," but to strategically integrate AI in ways that enhance efficiency, innovation, and overall competitiveness.

While headlines paint a picture of rapid and sometimes daunting advancements, there is a positive shift towards making AI technology accessible and practical for enterprise customers.

Here are some areas where AI adoption has been particularly fast:

  1. Customer Service and Support: Chatbots and virtual assistants powered by AI are increasingly used for handling customer inquiries, providing instant support, and even resolving issues without human intervention.
  2. Marketing and Advertising: AI is being employed for targeted advertising, personalized content recommendations, and marketing analytics. It helps businesses analyze large datasets to understand customer behavior and preferences, optimizing marketing strategies.
  3. Sales and CRM: AI tools are used to analyze customer interactions, predict sales trends, and automate certain aspects of the sales process. This includes lead scoring, customer segmentation, and personalized sales recommendations.
  4. Supply Chain and Logistics: AI is employed for demand forecasting, inventory management, route optimization, and predictive maintenance in logistics and supply chain management. This helps improve efficiency, reduce costs, and enhance overall supply chain visibility.
  5. Human Resources: AI is used for talent acquisition, employee engagement, and workforce management. AI-driven tools can assist in resume screening, identify potential candidates, and even predict employee turnover.
  6. Finance and Accounting: AI is transforming financial processes through automation of repetitive tasks, fraud detection, risk management, and financial analysis. Chatbots are also used for customer support in the financial industry.
  7. Healthcare: AI is making inroads in medical image analysis, drug discovery, personalized medicine, and virtual health assistants. AI technologies are enhancing diagnostics and treatment planning.
  8. Manufacturing and Quality Control: AI-driven technologies are applied in predictive maintenance, quality control, and process optimization in manufacturing. This includes the use of sensors and machine learning algorithms to prevent equipment failures.
  9. Cybersecurity: AI is used for threat detection, anomaly detection, and real-time monitoring of cybersecurity threats. Machine learning algorithms can analyze patterns and identify potential security breaches.
  10. Research and Development: In industries such as pharmaceuticals and materials science, AI is employed in research and development processes to accelerate discovery, optimize experiments, and predict outcomes.

It's essential to note that the adoption of AI varies across industries and businesses, and the landscape may have evolved since my last update. Industries that heavily rely on data and have a willingness to embrace technological advancements are generally at the forefront of AI adoption.

To thrive in this evolving landscape, companies must consider how to contextualize AI within their existing business processes.

One crucial aspect is identifying areas within business processes where AI can have the most significant impact. Whether it's streamlining operations, improving customer experiences, or optimizing decision-making, AI's potential is vast. Companies should conduct a thorough assessment of their workflows and operations to pinpoint where AI technologies can bring tangible benefits.

Moreover, successful AI integration requires a shift in mindset. It's not just about adopting a new technology; it's about fostering a culture of innovation and adaptability. This involves ensuring that employees understand and embrace AI as a tool to augment their capabilities, rather than something that might replace them. Providing training and creating a supportive environment for experimentation can be key in this regard.

Collaboration with AI startups that focus on making the technology accessible can be instrumental for companies looking to embark on this journey. These startups often offer user-friendly solutions and expertise to guide businesses through the integration process. By leveraging the resources provided by these entities, companies can implement AI technologies more efficiently and effectively.

In conclusion, the evolving landscape of AI presents both challenges and opportunities for businesses. Instead of succumbing to fear or feeling overwhelmed, companies can proactively approach AI adoption by strategically identifying areas for implementation, fostering a culture of innovation, and collaborating with startups that specialize in making AI accessible. By doing so, businesses can position themselves to not only keep up with technological advancements but also to thrive in an AI-driven future.

Finding your highest opportunity AI use cases

Unless your business directly revolves around Artificial Intelligence (e.g., OpenAI) AI is not a "strategy". It is much better the consider what your actual strategies are, and then how to apply AI to better achieve those strategies.

AI is a Customer Experience Accelerator

The need to implement digital and AI solutions is now imperative to be competitive. If you read the news covering artificial intelligence (AI) developments on any given day, you may feel pangs of fear and dread. From the recent UN report on AI's potential to harm human rights to the use of AI in spyware to hack into journalists' phones, it can seem as though the developers and creators of AI applications have lost control of its powerful potential.

But these reports lose sight of the more effective and well-governed developments that are supporting and optimizing real work and the interchanges happening every day between humans and AI. When AI is approached comprehensively for how it can optimize an entire system-- including the humans within that system-- it has a higher chance of delivering meaningful impact.

The Global AI Agenda, an MIT report from March 2020, found that customer care was one of the top use cases for AI. 60 percent of executive respondents believe AI will play a role in 11 percent to 30 percent of their processes-- a considerable but not necessarily dominant influence on how most businesses operate. The overall acceleration of digital adoption has likely changed these metrics post-pandemic and the need to implement digital and AI solutions is now imperative to be competitive.

Bots are a good place to start. One of the fastest areas of adoption for AI in the enterprise is chatbot applications. It's often a good place for companies to get started with AI and see quick results. By 2024, Insider Intelligence predicts that consumer retail spend via chatbots worldwide will reach $142 billion-- up from just $2.8 billion in 2019. Back in 2018, pundits were heralding the death of chatbots because as text-based phone trees, they hardly provided a personalized or knowledgeable experience, and their impact was more frustration than a path to cost-savings -- and certainly not a mechanism for building brand loyalty. Today's bots use natural language understanding to translate requests to intent and AI-enabled knowledge to converse more naturally. Beyond enabling better conversations, chatbots are the key to richer conversational intelligence. Sometimes the interactions are very simple at face value but have a cascade effect that profoundly changes a series of customer experiences. Research conducted by my workplace, Genesys, finds that the use of chatbots, social media and mobile apps has more than doubled since 2017. What customers really want is instant access to someone (or something) that understands what they need. Good bots are personalized, they know who you are and understand how to respond accordingly such as leveraging a customer's profile or transferring to the appropriate agent when needed. Self-service customer engagement is trending towards delivering that-- but companies need to work with a partner that can deliver at scale. For example, TechStyle, an online retailer, implemented AI to stand apart from the competition. With 5 million members, 6 million phone calls per year and 3 million chats per year, communication is core to its business. By integrating AI, TechStyle saved $1.1 million in the first year in operations costs and achieved a score of 92 percent in its member satisfaction survey. Supporting a growing AI-Native Workforce These successes are the tip of the iceberg in an accelerating market for AI. Companies also need to expand the aperture in how success is measured against the human-side of the AI equation. Contact center employees are often a customer's primary point of interaction with a business. The volume of customer interactions agents handle has increased by nearly 20 percent on average and spiked 35-40 percent in some cases during the pandemic, according to a poll among Genesys Customer Advisory Board members. This puts tremendous pressure on agents and technology on the front lines of these interactions. In a recent Genesys study, agents identified their strengths. Over half of respondents classified thoroughness and completeness as their top abilities, while less than 10 percent thought empathy and listening were their greatest strengths. When looking at this through the lens of AI implementation there are two critical takeaways. First, employees need systems that support a balance between complex tasks and easy to execute deliverables that satisfy a sense of accomplishment and completion of work at the end of a workday. AI that truly augments and considers human abilities needs to support users holistically and this means balancing high touch, high-level tasks with work that satisfies the need to mark off our list of to-dos at the end of the day. Ultimately the goal is to make the work more rewarding for employees. Having the important infrastructure and insights needed to deliver better customer experiences can achieve this. Developers of AI applications must consider how it impacts not only the end-user speaking to an AI chatbot, but the employee partnering with AI to create a great brand experience and a great work experience. This highlights the second point: the design and implementation of AI must consider empathy and how it augments and supports the self-reported, lagging skill set for listening and understanding. Ethical work environments that offer agents the values they most seek include AI that creates a balance of high- and low-level tasks that support meeting core metrics like average handle time-- but also help agents strive for more empathy and personalization that leads to more brand loyalty and share of wallet. The customer engagement employee is an AI worker, their knowledge and contributions are essential to AI implementation. You cannot separate the two. MIT robotics professor Cynthia Breazeal has said that the next generation will have moved beyond being 'digital natives' they will be 'AI natives.' Contact center employees are at the cutting edge of an AI-literate workforce in our society and we have the opportunity now to provide the technology that will support their work and better serve customers. Is your business ready for the outcomes AI can deliver?

Title: “The Synergy of Human-AI Collaboration: A Formula for Unprecedented Success”

In the rapidly evolving landscape of technology, the collaboration between humans and artificial intelligence (AI) emerges as a key player, poised to outshine individual efforts. As we delve into the era of smart machines, it becomes increasingly evident that the combination of human ingenuity and AI capabilities creates a powerful synergy, surpassing the potential of either entity in isolation.

Studies, such as those conducted by MIT researchers Erik Brynjolfsson and Andrew McAfee in their book “The Second Machine Age,” highlight the transformative impact of AI on various industries. While AI alone can excel in specific tasks, it lacks the creativity, emotional intelligence, and nuanced understanding that humans bring to the table.

On the flip side, humans, unaided by AI, face limitations in processing vast datasets, complex calculations, and executing repetitive tasks with speed and precision. Enter AI, with its ability to analyze massive amounts of data swiftly, identify patterns, and perform calculations beyond human capacity.

The magic happens when these two forces join hands. A landmark paper by Stanford University’s James Landay and Brad Myers, “The Power of Human Energy,” emphasizes the collective strength arising from human-AI collaboration. The authors argue that combining the cognitive abilities of humans with the computational power of AI results in unparalleled productivity and problem-solving prowess.

Real-world examples substantiate this claim. In healthcare, AI algorithms assist doctors in diagnosing diseases by swiftly analyzing medical records and images, while human physicians provide the vital empathetic touch and nuanced understanding of patient narratives. Similarly, in business, AI aids decision-making processes by crunching data, but it’s the human touch that brings creativity, intuition, and a holistic perspective.

In conclusion, the future of excellence lies in the fusion of human intellect and artificial intelligence. The references cited underline the growing consensus among researchers and experts that human-AI collaboration is not just a trend but a paradigm shift, offering unprecedented potential and propelling us into a new era of innovation and achievement.

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