Despite advances in AI and digital tools human expertise remains crucial for complex and high-stakes purchases, where salespeople help buyers navigate ambiguity and build trust. Because companies continue to produce complex products, the demand for skilled salespeople will continue to grow in many sectors—particularly in tech and advanced manufacturing. To adapt, leaders must ask questions aimed at fully understanding where human involvement is essential and where digital tools can be the primary sales channel.

A company’s success will not rest on AI per se; it rests on what companies do with it. When companies lead with AI or treat it as the answer, they put the cart before the horse and risk compromising their company’s strategy and the value it provides to the marketplace. But when companies take the opposite approach, starting with strategy, identifying how they can offer buyers a leap in value, and then looking to technology as a tool to deliver that leap, AI can be a powerful catalyst to profit, growth, and commercial opportunities.

Cloudflare now beta testing pay-per-crawl feature to stop endless AI scraping.

A comprehensive scorecard can help companies redesign their risk governance frameworks and practices for gen AI and harness the power of this transformative technology.

Mark Cuban has backed Skylight, a TikTok alternative built on Bluesky's underlying technology.

The software company SAP successfully leveraged AI tools to begin selling to the small and medium enterprises (SMEs) market, which had previously been uneconomical for its in-person sales approach. By mapping the customer journey and deploying over 40 AI tools, SAP transformed the way it sells, reducing the sales cycle from 12–18 months to 3–6 months and supporting over 22,000 new customer opportunities in 2024. This case study demonstrates the potential of thoughtful AI scaling to add significant revenue to large businesses.

This Me, Myself, and AI episode features Harvard Business School professor Raffaella Sadun in conversation with hosts Sam Ransbotham and Shervin Khodabandeh.

This Me, Myself, and AI episode features Harvard Business School professor Raffaella Sadun in conversation with hosts Sam Ransbotham and Shervin Khodabandeh.

Lab experiments show that AI can outperform humans, but outside of the lab, when making decisions, executives face shifting challenges, often with insufficient or inaccurate data. A team from Warwick Business School and The BCG Henderson Institute teamed up to understand: What happens if they take AI out of the lab into a real company? Over the past year they joined a series of executive team meetings at Giesswein, an $85 million revenue company based in Austria, that sells organic, eco-friendly wool sneakers. The idea was to experiment with different ways of integrating AI into their executive meetings to understand what works and how.

AI Integration is an evolution. Businesses that fuse AI with human ingenuity will soar, those that resist will stagnate. The key? Build an AI-ready culture.

Organizations that view AI as just another technology project will increasingly find themselves irrelevant. Success will go to those who adopt a balanced approach—being radically optimistic about AI’s potential while remaining cautious about its risks. By integrating structured frameworks like OPEN and CARE, organizations can navigate this challenge, harnessing AI’s transformative power while building the resilience necessary to thrive in an uncertain future.

Gain better strategic foresight and turn future possibilities into actionable insights with horizon scanning.

Gemini Code Assist lets you do 90 times more than competing tools.

Companies often assume that older workers are less adept at adapting to new technologies. As leaders look to bring new AI tools onboard, many believe that younger workers will be better able to integrate these tools into their work. A recent survey suggests this view may be unfounded. When hiring managers—many of whom held this view—were asked to assess the job performance of the midcareer and older workers whom they already employ, 89% reported that these individuals performed as well if not better than younger peers. Midcareer and older workers offer unique strengths when it comes to working with AI. For companies that want to drive AI adoption, these workers are an essential asset that can’t be overlooked.

Gen AI has increased uncertainty about workforce skills and capabilities. Organizations that harness AI-driven innovation find it makes strategic thinking and talent planning easier.

Large Language Models (LLMs) can provide many benefits to security professionals by helping them analyze logs, detect phishing attacks, or offering threat intelligence. Learn from Wazuh how to incorporate an LLM, like ChatGPT, into its open source security platform.

To get the most from employees, organizations need a more strategic, collaborative, fluid, and data-driven people operating system. Here’s how they can build one.

Artificial intelligence is set to revolutionize strategy activities. But as AI adoption spreads, strategists will need proprietary data, creativity, and new skills to develop unique options.

These companies are all in a position to compete both within China and beyond.

Taiwan bans DeepSeek AI citing national security risks; Italy follows suit amid data privacy concerns.

Plus: the US has recorded its first human death from bird flu

Many organizations aren’t sure how to reap business value from GenAI. This series offers expert insights and examples.

Since the 1960s, there has been a quest to find ways to address the tidal wave of paper deluging organizations. The latest technology that is being applied is AI. A case study — an effort at a healthcare insurer — provides five lessons not just applicable in projects aimed at reducing paper but also for other AI initiatives as well: 1) The goal should not be automation; it should be to get the work done in a cost-effective manner. 2) A successful application of AI may not mean lower employment. 3) AI can allow employees to focus on higher-value work. 4) The savings come from increases in scale made possible by higher productivity and quality. 5) Involving employees who have been performing the function in the AI project is essential.

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