The most daunting aspect of Generative AI isn't the hype around the technology but the business metrics emerging from it. Consulting and services companies are showing or planning the highest revenue gains. Interestingly, these companies were hardly involved in the development of this technology but now promise to scale its execution, while product companies struggle to find the right talent. GenAI strategy differs significantly from standard business strategies. It needs to be execution-focused rather than purely transformational. Sometimes, it's surprising to see the promises made without a deep understanding of the subject matter. One common phrase is: "Create a feedback loop, and the model will learn from it." When the New York Times published their article, "The A.I. Boom Has an Unlikely Early Winner: Wonky Consultants," they missed a crucial point: who will be the loser in this early game? It will be the companies that rely solely on external vendors to build their AI strategy. We saw a similar mistake during the Machine Learning/Deep Learning boom of the 2020s. Rapid, ineffective experimentation in the name of transformation led to technical debt without any real value. Adoption lagged, and many companies were left with a few fancy models they hardly knew how to use. This time, a revenue-sharing model for GenAI might be a better approach based on business gains. Achieving thought leadership in GenAI is challenging. It requires a blend of technical and business acumen. It's essential to understand the background of the team proposing the GenAI strategy. What credibility do they bring? Have they built similar systems in the past? If someone suggests "creating a feedback loop," ask how you will send the feedback, not just in general terms but the specific format of the data used. It's up to you to decide if a polished presentation and sharp vocabulary are enough for GenAI or if you need to set accountability. #ExperienceFromTheField #WrittenByHuman #EditedByAI
Ameya Joshi
4w
Consultants and employees always make money, regardless of the impact or outcome. This shouldn't be a surprise. Founders and investors make money only when they are right. But when they are right, they usually win big.
Curious to know what are these consulting & services companies that are gaining or have gained revenues vs the product companies that are struggling.. Consulting and services companies are showing or planning the highest revenue gains. Interestingly, these companies were hardly involved in the development of this technology but now promise to scale its execution, while product companies struggle to find the right talent.
When I hear “creating a feedback loop” from a client, almost always a vaporvare PowerPoint big consulting company have oversold this concept, and this has been the case for a long time. Clients asking why can’t data points update models as they occur (read consultants telling execs that you gotta have “feedback loop”) - and then explain to the execs that it takes a lot of data points to budge a model.
I completely agree with you, consultancy has always been like this whenever a disruptive technology comes in and change entire landscape. Particularly the scalability part you have highlighted, moreover even the sustainability and maintainability are also not being well thought through in these promises. They miss the deep engineering involved. Sometimes I even more concerned while dealing with GenAI solution, do they even have a model to address the growing concern of security; trust me there's not even well defined guardrails that you could find in these solutions. However, what I could believe an effective model to GenAI solutions, which has huge impact on Security, Privacy, Consumption is, Consulting firms partner with Core Engineering firms who actually have contributed in development of GenAI, additionally these kind of projects need an AI officer as an observer with accountability of overall governance, relevance and progress. Preferably these officer would be a researcher and policy maker in AI paradigm. Please share your thought. Consulting firms may have better reach towards having more use cases due to their access to large clients but engineering knowledge is paramount.
The hype surrounding Generative AI (GenAI) can be overwhelming, but the real challenge lies in translating that excitement into effective strategic planning and execution. Consulting firms might offer attractive scaling solutions, but true success hinges on a well-defined internal GenAI strategy. Don't be swayed by empty promises or vague pronouncements – delve into the specifics. Ask about the team's technical background and their experience building similar systems. Similarly, "feedback loop" references are meaningless without understanding how data will be captured, formatted, and fed back. Building internal expertise alongside strategic partnerships is crucial. While revenue sharing models can incentivize adoption, prioritize long-term business value generation over short-term gains. By prioritizing execution alongside transformation and developing a critical eye for external solutions, businesses can navigate the GenAI landscape strategically and avoid the pitfalls of the past.
Vishal Suri
3w
Nitin Aggarwal What you have mentioned is true for every new technology. You don’t have to be involved in building the technology to become an expert in implementing it. Consulting companies are good in using their technology and domain expertise to learn new technologies quickly, implementing early use cases, and scale as technology maturity and adoption increases.
Following still baffles me around GenAI: 1. Are there SOPs available around ethical use of AI Or is it still at 'We all have responsibilities for its ethical use' stage? 2. What metrics are being chased/defined or is it still FOMO? 3. What happened to Intelligent Automation? Have it achieved all that it promised to achieve? Is GenAI augmenting it or is it going to 'scrap the old'? Augmenting should be more reasonable, isn't it? .. .. .. And many more of such operational questions😊.
Upasana Pati
4w
It’s cart before the horse. Product companies are busy transforming the tech at a blinding pace trying to keep up with competition… yet consulting companies project enormous profits with services. The paradox here is that GenAI is not a volume game where you need a lot of supplemental workforce to implement , rather it will be changing at a faster pace than companies can even keep up with up skilling . This is where consulting companies will have to pivot their traditional ways of functioning and will have to focus hard on where they can really make profits in the long run.
Top CIO & AI Voice | AI & Data Leader | Experienced mid-market CIO & CTO | Fortune 100 C-Suite Advisor | Cloud Strategy | Product & Innovation Management | Responsible AI
4wConsultants in GenAI: 1. Bridge the gap between cutting-edge technology and practical business applications. 2. Enable cross-industry innovation through diverse experience. 3. Provide risk mitigation in ethical AI use, privacy compliance, and bias detection. 4. Address talent gaps by attracting experts and upskilling client teams. 5. Implement agile methodologies for rapid GenAI prototyping and iteration. 6. Focus on measurable outcomes with KPIs tied to business results. 7. Orchestrate complex ecosystems of vendors and resources for coherent AI solutions. That's a lot more than slick PowerPoint presentations.