2025 Enterprise Agenda

This episode of the “Gen AI Breakthrough” podcast, explores findings from The Hackett Group’s 2025 Enterprise Key Issues Study, with a focus on the rapid adoption of generative AI. Hosted by Andy Warzecha – with insights from Kyle McNabb, Vince Griffin and Harry Osle – the discussion covers AI-driven transformation, workforce skill gaps, and the challenges of scaling AI initiatives while balancing data quality and technology complexity.

Welcome to The Hackett Group’s “Gen AI Breakthrough” podcast, where top experts give actionable artificial intelligence (AI) insights, expert advice and strategies to achieve breakthrough business performance. This episode is hosted by Andy Warzecha, principal and the North American Advisory practice leader at The Hackett Group. He is joined by Kyle McNabb, vice president and principal of Research at The Hackett Group; Vince Griffin, principal and Global Finance and GBS Services Advisory practice leader at The Hackett Group; and Harry Osle, principal and chief human resources officer at The Hackett Group. In today’s episode, they will explore the findings and major challenges from The Hackett Group’s 2025 Enterprise Key Issues study.

To begin, they share the highlights found from the study. In spite of growing concerns of cybersecurity and economic uncertainty, 90% of the enterprises we interviewed have plans to scale their generative AI (Gen AI) efforts with embedded and distributed models. No matter how early or late an enterprise is, it is important to embrace Gen AI. Most enterprises are still fairly early with more centralized models, but 50%-60% of enterprises have plans or are already using AI to support business outcomes and objectives. They are executing and delivering it, and realizing the value and exponential return for Gen AI through transformative improvement or breakthrough.

Next, the top concern from leaders is the complexity of existing processes, which stems from the perceived skill gap that leaders are seeing in their teams. AI can transform work steps and look at specific ones that can be automated. There are concerns from leaders about having the right employees. After we go through the automation process, they worry whether those individuals have the right skill sets. The human aspect would never be eliminated due to AI but due to increased capacity. There is concern surrounding workforce planning and whether individuals have the right skills to do the work moving forward. Leaders need to understand skill sets and upscale or rescale, and how to move A team players up, and develop and train the B team members. HR is the center of everything around AI and it impacts, and helps all the other functions through conducting strategic workforce planning.

In addition, they define the terms “assistance” and “agents,” and the differences. Gen AI is reshaping work in the workforce and how workers will use the capability through assistance and agents. Assistance is the use of AI to help someone in doing a task like summarizing notes from a meeting or Google maps with navigation. Agents are intelligent programs that can do work autonomously on their own without human intervention. Leaders need to look at the work and decide what is right for assistance and agents. Next, they discuss realistic benefits and setting those expectations in the budgetary process. They said enterprises set themselves up for this rocky road by not having the right people, roles, strategy, ideation and overseeing implementation. They need multipoint input from sales, marketing and finance – every area of the company. They need to understand all the variables and the potentiality. It is flawed thinking that this is just an information technology thing. It is truly full company transformative work for all leaders and people in the company. However, finance has to take a core leadership role here because they are uniquely positioned to have visibility from the economics of the company. Their expertise needs to be included with benefits and realistic outcomes on this full company mission.

Lastly, they discuss the challenge of data quality and technology complexity. Existing data is complex and no organization has perfect data. When leaders look at those two challenges on their own, they look huge and immense. They tend to avoid it and focus on the impact to this work and work step. They are finding they can reduce the complexities down to things they can easily fix instead of focusing on the challenge as a whole. For example, they may not need to integrate data, but leverage the models already in place. Individual work needs to be changed and broken down into bite-sized chunks to effectively face these challenges head-on.

Timestamps

• 0:39 – Welcome to this episode hosted by Andy Warzecha.
• 1:23 – What were the highlights found from the study?
• 3:52 – What is behind this concern and what can leaders do to address it?
• 6:47 – Definition of the terms “assistance” and “agents,” and the differences.
• 8:21 – Realistic benefits and setting those expectations in the budgetary process.
• 11:07 – The challenge of data quality and technology complexity.