Interview
Editor's note: This article is part of our series that celebrates the 2024 VKTR Contributors of the Year.
Myles Suer, a research director at Dresner Advisory Services, is a steady voice on AI in the enterprise. As one of the 2024 VKTR Contributors of the Year, Suer regularly covers AI agents, AI from a C-suite perspective, AI trends and more. In this Q&A, Suer discusses how enterprises can effectively approach both AI technology and the AI market.
Table of Contents
- About Myles Suer
- Solving Business Problems With People and AI
- Increasing Productivity and Innovation With AI
- Using AI to Harness Unstructured Data and Automate Tasks
- Planning for AI Risks and Regulation
- Spreading AI Knowledge in the Enterprise
- Mature AI and Getting Left Behind
- Emerging Segments of AI
About Myles Suer
- Research director at Dresner Advisory Services
- Digital and CIO analyst, technology journalist and CIO influencer
- Served as senior manager of IT analytics strategy at HP Software
- Served as chief platform evangelist and principal product marketing manager at Informatica
- Host of the #CIOChat on X
- Holds an M.S.A. in marketing and international business from the UC Irvine Paul Merage School of Business and an M.S.B.A. in strategic planning from the USC Marshall School of Business
Why are you personally interested in AI?
I began my career working on AI for autonomous vehicles and systems, captivated by its potential, though it wasn’t yet practical. With significant coursework in decision science and modeling during my M.B.A., I later product managed an advanced analytics solution at HP Software. This product, developed with data modelers, helped IT organizations optimize staffing investments to minimize the business impact of downtime.
However, the emergence of big data and more accessible modeling approaches transformed AI from aspiration to reality. As Terrence Sejnowski explains in “ChatGPT and the Future of AI,” large data sets and modern computing power unlocked AI’s potential. Today, I’m thrilled to see my long-standing passion for AI finally realized in such a transformative era.
What are your general activities in AI?
While I honed my skills building multi-variate models in graduate school, today I focus on telling the evolving story of AI — its transformative use cases, inherent risks and the path forward. As an author and thought leader, I help organizations navigate this landscape, offering guidance on how to implement AI responsibly and effectively. Reviewing AI books keeps me on the cutting edge of emerging perspectives, and it’s clear that while most organizations are starting with unstructured data, the true revolution lies ahead with the rise of agentic AI.
Solving Business Problems With People and AI
What’s your primary philosophy on AI in the enterprise?
My primary philosophy on AI in the enterprise is simple: start with business problems, not technology. AI isn’t magic — it’s a tool for solving specific challenges and driving measurable outcomes. Success begins with strong business relationships that enable leaders to identify impactful yet realistic problems where AI can deliver quick wins. This requires building cross-functional teams that pair domain experts who understand the problems with data scientists who can apply AI solutions effectively.
To bridge these two worlds, organizations need what Wayne Erickson and Barb Wixom call “purple people” — individuals who combine business acumen with technical AI fluency. Equally critical is fostering what Tom Davenport describes as citizen AI, empowering non-experts across the enterprise to leverage AI tools. Given the scarcity of AI specialists, the real transformation happens when everyone has a role in scaling AI solutions, embedding them into workflows and delivering tangible value across the business.
Increasing Productivity and Innovation With AI
How can AI technology improve enterprises?
AI technology can transform enterprises by driving both productivity and innovation, two pillars of business success. In a world where slowing population growth limits the traditional labor pool, organizations will increasingly rely on gig workers and flexible talent models to fill the gaps. AI becomes critical here: it augments human capabilities, enabling workers to be far more productive, efficient and focused on high-value tasks. By automating repetitive work, AI frees humans to innovate and solve more complex challenges, amplifying the impact of both full-time and gig talent.
Beyond productivity, AI unlocks new possibilities for innovation. For example, it enables deeper levels of segmentation and personalization, allowing businesses to serve markets and customers in ways that were previously unimaginable. More than just a tool, AI to this business strategist is becoming the means to create what Michael Porter labeled "competitive advantage." Enterprises that integrate AI into their core strategies will not only do more with less, but will redefine what is possible, positioning themselves ahead of slower-moving competitors.
Related Article: The GenAI Skills Employees Need to Be Productive
Using AI to Harness Unstructured Data and Automate Tasks
Which enterprise processes and workflows should AI be improving?
Traditional AI focused on structured data and solving broad, well-defined problems, often constrained by the availability and complexity of tools. Generative AI has changed the game, unlocking the value of unstructured data, such as text, images and audio, that was previously cumbersome to process. A prime example is automating data discovery and testing, where GenAI brings speed, accuracy and innovation to tasks that were time-consuming and error-prone.
Agentic AI takes this evolution further, automating workflows originally designed for humans. It doesn’t just augment human effort — it can replace it in areas like first-level support or IT service management, where repetitive, rule-based tasks dominate. The real transformation, however, will be long-term. By automating these processes, agentic AI frees humans to focus on higher-value work, reshaping what we do and how we do it. Rather than displacing people, it redefines their roles and enables a new level of productivity and creativity.
Related Article: How Will AI Agents Redefine the Workplace?
Planning for AI Risks and Regulation
How can AI adversely impact enterprises?
AI brings significant opportunities for enterprises, but it also carries concrete risks that cannot be ignored. Analysts and CIOs have consistently pointed to challenges, such as errors, bias, security vulnerabilities and privacy concerns. Unlike traditional IT systems, where fixes could often be bolted on after implementation, these risks must now be addressed at the very start of AI design and deployment. Failing to do so exposes organizations to personal, legal and reputational risks that can be difficult to recover from.
Regulatory bodies are beginning to demand transparency around AI usage, requiring organizations to disclose their AI strategies and risk management practices. This adds another layer of accountability for enterprises, especially as the consequences of poorly implemented AI become more visible. Brand reputation is fragile — one misstep can erode trust that took years to build. Consider how airlines have faced backlash for flawed implementations of automation and AI in customer service.
These examples underscore the need for thoughtful, ethical and transparent AI deployment. The path forward requires a proactive approach. Organizations must prioritize responsible AI design, embedding safeguards to mitigate bias, ensure accuracy and protect sensitive data. By addressing these risks early, enterprises can avoid costly missteps and maintain trust with their customers, employees and regulators. AI has the power to transform business but only when it is implemented with care, accountability and a clear understanding of its potential downsides.
Related Article: 5 AI Case Studies in Risk Management
Spreading AI Knowledge in the Enterprise
What should an enterprise do to support AI adoption?
To support AI adoption, enterprises must start by educating senior leaders to become AI savvy, as David De Cremer suggests. Leaders need a clear understanding of AI’s capabilities, limitations and ethical implications. This knowledge ensures they approach AI with the mindset that its primary purpose is to augment human efforts, not simply eliminate jobs. With this foundation, organizations can set a clear vision and purpose for their AI initiatives, aligning them with solving real business problems rather than pursuing technology for its own sake.
Enterprises should also foster what some call a citizen AI revolution, empowering employees across all levels to leverage AI tools to address gaps that expert-led efforts can’t cover. By nurturing both citizen developers and technical experts, organizations create an ecosystem where innovation thrives. This means providing the necessary training, tools and support to help teams succeed while ensuring that AI efforts remain aligned with broader business objectives. A thoughtful, inclusive approach to AI adoption not only accelerates innovation, but also builds trust and engagement across the enterprise.
Related Article: A 4-Step Plan to Becoming an AI-First Organization
Mature AI and Getting Left Behind
What are enterprises doing well in AI adoptions?
Enterprises excelling in AI adoption are leveraging it to create transformative business value. Ping An Insurance, identified by MIT-CISR as the most mature in AI adoption, has integrated AI seamlessly into its operations, from customer service to underwriting. Companies like John Deere showcase innovation with tools like See and Spray, revolutionizing precision agriculture. Meanwhile, tech giants like Amazon and Google push AI boundaries internally, setting benchmarks for scalability and innovation. However, these leaders are exceptions. MIT-CISR reports that only 7% of organizations are truly "AI future ready," highlighting how far most enterprises still have to go.
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What are enterprises not doing well in AI adoptions?
Many enterprises struggle with AI adoption in key areas. Some have yet to start their AI journey, missing the opportunity to leverage its potential. Others remain stuck in the experimentation phase, failing to move beyond pilots and scale solutions. Then there are organizations unable to launch AI implementations at all, whether due to technical hurdles, organizational resistance or a lack of clear strategy. These obstacles prevent them from realizing the full value of AI and risk leaving them behind as more agile competitors advance.
Emerging Segments of AI
What do you see as the growth opportunities in AI for enterprises in the next year and beyond?
Enterprises will see significant growth opportunities in AI, especially through the rise of agentic AI, where AI automates human workflows and augments decision-making at scale. The integration of GenAI with traditional AI systems will also be a game-changer, enabling more sophisticated applications that leverage both structured and unstructured data. Additionally, as enterprises refine the enabling technologies, like data infrastructure, AI governance and ethical frameworks, AI adoption will become more seamless and impactful, unlocking new efficiencies and driving innovation across industries. The future is bright for organizations that combine these advances to solve real-world problems and stay ahead of the competition.
Check out some of Suer's articles from 2024:
