Consulting Magazine is pleased to deliver the first article of a 3-part series "The Case to Embrace AI." This series examines AI usage among professional services firms, and more specifically, how some consulting firms, as focused as they may be on guiding clients on their AI journeys, could be overlooking important opportunities to put AI to work within their own companies. The series will outline some of the promising use cases for AI in a professional services firm, the benefits these use cases can provide, and the foundational elements firms need to put in place to reap those benefits.
Plotting Your Firm's Internal AI Journey You've been busy introducing your clients to AI. Now it's time to put it to work inside your own firm. Contributed by Carlos Sanchez, SAP
The rise of artificial intelligence has planted a new inflection point on the curve that tracks our technological progress. Soon, it seems, we'll be marking developments according to whether they occurred "BC," before ChatGPT launched publicly in late 2022, or "PC," as in post-ChatGPT, when generative AI opened the world's eyes to the seismic impact intelligent technologies can have on how we conduct our businesses and our lives.
That's certainly true in the world of consulting and across the professional services industries in which I work, where firms of all shapes and sizes are building new services and recurring revenue streams around genAI, helping clients integrate digital assistants and other forms of business AI into their operations to enable them to operate and work more efficiently, and capture new insights to support their decision-making. Among Consulting Magazine's 2024 Leaders in Technology award-winners in AI-related categories are some of the world's biggest-name multidiscipline firms (Deloitte, Infosys) alongside other, younger shops that specialize in AI solutions for clients in specific verticals, such as Codoxo, whose offerings include a service with capabilities that enable employees across a health care company or agency to research issues with self-service reporting and querying of claim, provider, facility and member behavior.
As focused as many consulting firms are on supporting their clients on their AI journeys, and as much as that focus is translating into promising business opportunities, it's important that firm leaders pause for a bit of self-reflection and ask themselves an important question: How much more could and should we be doing to leverage genAI internally to benefit our own business?
As the range of large language model-driven AI solutions for business grows, so does the number of potential use cases for AI inside a consulting firm, and really inside any type of professional services company. Open AI's ChatGPT and other digital assistants like Google's Gemini are doing the work of socializing and demystifying genAI, to the point where many of us are now comfortable using AI co-pilots in our daily lives, thanks to improvements with natural language processing (NLP) and its constituent parts, natural language understanding (NLU) and natural language generation (NLG). It can analyze and glean insight from multiple and sequential inputs, then boil that down into understandable language to suggest, for example, which breed of dog might be most suitable for me given my location, lifestyle and other factors.
Now, as our understanding of and comfort level with genAI grows, and as the models themselves mature, the focus is shifting from the horizontal to the vertical: business applications for genAI within specific industry verticals, from finance to health care to industrial manufacturing and yes, consulting, too. The emergence of digital assistants like Microsoft's Copilot, IBM's Watson, SAP's Joule and others gives consulting firms prebuilt, ready-to-integrate genAI tools they can embed into their own internal processes, just like many consulting firms have with the intelligent services they offer to clients.
The potential use cases for virtual assistants alone look really promising for their ability to elevate client interactions, provide personalized services and accelerate decision-making processes. But where inside your firm to look for opportunities to apply genAI? Those opportunities look especially compelling in three areas:
- Enterprise productivity, including report generation and analysis, intelligent processing of documents — invoices, employee hiring requests, etc.), and document screening (reviewing résumés for contract employees to identify specific skills, for example).
- Client support, including intelligent search, generation of personalized recommendations, case analysis and creation of extracts, automated email responses, and tiered advice with a combination of automated and human support.
- Code generation. A January 2024 report from IDC predicts that by 2025, GenAI will become a go-to tool for software co-development and that by 2028, 80% of software testing will be performed by GenAI. Firms with in-house development teams, take note. With prompts, GenAI can generate code for users who lack coding skills, enabling them to use low-code/no-code tools to build extensions into standard software.
Uses like these suggest there's a strong case for embracing GenAI. But you can't just sprinkle a bit of genAI dust inside your business and expect the insights to magically start flowing. Because AI depends on fresh, trusted and relevant data to learn and produce value, data quality and accessibility are a critical prerequisite to benefiting from any type of AI. With substandard data, the insights your genAI provides might not be trustworthy. And if you can't trust those insights, what's the point?
As with any emerging technology, integrating AI into your business is a journey. To make that journey a fruitful one, you'll need to stay focused on finding AI use cases that align to your business goals and resolve a business issue for you, your clients or your partners. You'll need a plan for strategically piloting genAI within specific areas of your business, then measuring and assessing the results before scaling it more broadly. And perhaps most importantly, you'll need an explorer's mindset, where you push boundaries, build on successes and learn from missteps. Ultimately, what you learn along your AI journey creates a cycle of innovation, where your internal experiences with AI yield lessons and practices that inform your work with clients, and your AI work with clients informs your AI usage internally. This is what a win-win looks like in the Post-ChatGPT Era.
Carlos Sanchez, Platinum Business Principal, SAP Carlos Sanchez has been a business and technical consultant for 25 years with multiple consulting organizations. He is currently a solution expert in the SAP Professional Services Global Industry Business Unit.
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