Cutting Through the AI Enterprise Buzz: What to Really Expect This Fall

As we head into a new academic year and employees return back to work after summer vacations, the AI landscape looks quite different from the one we left in June. Major players like OpenAI, Meta, and McKinsey unveiled significant new generative AI capabilities over the past two months that have the potential to transform enterprises.

OpenAI launched ChatGPT Enterprise, a more powerful version of its popular chatbot aimed at business users. Meta open-sourced Code Llama, a natural language coding tool leveraging the AI behind Llama2. And consulting giant McKinsey rolled out Lilli, its own proprietary generative AI system designed to synthesize insights for clients. Amidst the buzz over these new tools, one thing is clear - more tangible, practical enterprise applications of AI are coming online to redefine workplaces this fall.

As we move out of the AI hype cycle, we are moving into the real world applications of generative technology.? Six months ago, I was cautioning business to not jump too quickly into solutions so that the market could mature.? I strongly believe this fall is the time to start moving with the wide variety of solutions coming to address the three key issues facing corporate adoption: Privacy and Security, Scaling AI Operations, and Building Your Own Solution.?

Now, I've always believed in cutting through the noise. So, let's do just that.

Privacy and Security

Remember the good old days when locking your diary was all the privacy you needed? Fast forward to today, and 'privacy' is more complex than ever. With AI, the stakes are high. Data isn’t just numbers; it’s personal, it’s sensitive, and it’s power. We're feeding AI systems with vast amounts of information, but here's the million-dollar question: How secure is it?

Novice:

"Understanding Data Contribution"

For small businesses just starting with AI, focus on solutions that allow controlling data sharing. ChatGPT lets users toggle data contribution on/off to decide if they want to train the model. Being informed on how AI systems learn is key.

Intermediate:

"Opting Out of AI's Reach"

As companies expand digitally, leveraging exclusion tools like OpenAI's robots.txt policy becomes critical. This allows webmasters to easily opt-out and prevent their content being used for training. Staying vigilant of privacy risks is essential.

Advanced:

"Building Robust Privacy Protections"

Large enterprises must prioritize strong privacy measures within their AI stack. Microsoft and OpenAI's Private AI uses encryption and federated learning for secure, distributed training. McKinsey's Lilli platform acts as a privacy layer between users and data. Investing in robust controls provides a competitive edge.

Scaling AI Operations

Here's a truth bomb: launching a pilot AI project is one thing; scaling it across an enterprise? That's a whole different ball game. We've seen AI's potential in isolated projects, but the real challenge? Integrating it into the daily grind.

Novice:

"Boost Engagement with AI Assistance"

Early on, leverage ChatGPT for low-cost content generation to boost social media engagement. The AI assistant can help draft posts, respond to comments, and more. Starting small establishes a solid foundation.

Intermediate:

"Optimize Business Processes with AI"

As operations expand, integrate AI to optimize business processes like customer service and HR. Combining NLP with task automation drives productivity and efficiency gains. Focusing on high-impact areas first creates momentum.

Advanced:

"Bake Innovation into Products and Services"

Leading enterprises are building proprietary AI innovations into their offerings, like Canva using Llama2 to generate tailored graphics. Microsoft embeds AI apps and services to uncover data insights. Owned IP unlocks strategic advantages and new revenue streams.

Build Your Own Solution

There's a DIY spirit in all of us, isn't there? That urge to roll up our sleeves and craft something uniquely ours. In the world of AI, this sentiment is catching on. But why rely on off-the-shelf solutions when you can tailor-make AI tools that fit your enterprise like a glove?

Novice:

"Leverage Everyday Tools to Initiate Integration"

Start by using built-in AI in existing work apps. Google Workspace integrates smart features like data analysis in Sheets and writing suggestions in Docs. Low-code options make getting started simple.

Intermediate:

"Elevate Capabilities with Custom Development"

As expertise grows, work with developers to integrate custom AI via APIs and partnerships, unlocking new capabilities tailored to your needs. Investing in technical capabilities creates competitive advantage.

Advanced:

"Build Fully Customized AI Systems"

Enterprises with strong technical teams can build fully customized AI systems like Code Llama and ChatGPT Enterprise. Developing proprietary models trained on your data delivers unique strategic value.

Remember the days when AI was the shiny new toy everyone was raving about? Well, according to Gartner's hype cycle, that glittering 'hype' phase? It's behind us. What lies ahead is the real deal — the tangible, practical applications of AI that will redefine our workplaces this fall.

Building an Enterprise AI Strategy that Delivers

Remember the days when AI was the shiny new toy everyone was raving about? Well, according to Gartner's hype cycle, that glittering 'hype' phase is behind us. What lies ahead this fall is the real deal — tangible, practical enterprise applications of generative AI that will redefine workplaces and industries.

While AI capabilities are advancing rapidly, implementing these technologies successfully involves more than just adopting the latest tools. It requires a strategic approach across the three key areas:

Privacy and Security - Robust governance, ethical practices and technical safeguards are essential as AI is embedded into business processes and offerings. Companies must make privacy and fairness central pillars of their AI programs.

Scaling AI Operations - Moving from isolated pilots to integrated, scalable AI will transform workflows and productivity. Change management, continuous monitoring and iterative deployments will be critical to adoption.

Building Custom Solutions - Enterprises with specialized needs should invest in developing proprietary AI systems tailored to their goals, data and users. However, this requires long-term commitment to R&D and talent acquisition.

The path forward involves choices - build, buy or partner? Move fast or start slow? AI applied judiciously has immense potential, but rushing ahead without strategy increases risks. To cut through the hype and prepare for the AI-powered future, executive education is critical.

That's why I'm excited to announce this Fall’s Generative AI Bootcamp for Executives, an intensive 4-week program running September 6-27. This comprehensive curriculum will equip leaders with the strategic vision, practical knowledge and implementation roadmaps to deploy AI successfully.

Each two-hour session will feature hands-on applications, case studies and interactive discussions focused on:

• Evaluating AI vendors and offerings
• Developing an enterprise AI adoption roadmap
• Assessing legal, ethical and security risks
• Up-skilling teams and planning for job impacts
• Identifying high-value AI applications to pilot
• Maximizing data quality, privacy and controls
• Building vs buying - tradeoffs and approaches

The hype is over and the AI revolution is here. This bootcamp will prepare you to navigate it strategically, avoiding the pitfalls through expert guidance. Enroll now to reserve your spot in this one-of-a-kind program. I look forward to seeing you there!

If you're interested in bringing Generative AI training to your employees, check out my?website ?for course offerings or DM me on LinkedIn for more information.

Registration for our Fall Bootcamp is open. Sessions run Wednesdays 5-7PM EST, September 6-27.

Give yourself the gift of learning this fall.

The adventure awaits.