Don't Fall For AI's False Promises

 

We're in the midst of another FOMO frenzy over artificial intelligence (AI),  and especially large language model-based systems like ChatGPT. Seems like millions of companies and dozens of VCs are rushing to invest billions in what they hope is going to be the next best thing.

The term AI has become sloppy shorthand and an overused, misapplied buzzword (like crypto, blockchain and Web 3.0) that's so broad as to cease to be descriptive. AI is so widely misunderstood that the only thing most of these folks actually know about the subject is that they certainly don't want to miss out on it. Even if they don't really know what the "it" is that they'd be missing.

But what's even more discouraging is that this is one of those semantic swamps and media-driven hypes that you wade into and, as you're slowly sinking your precious capital and tech resources into the bog and your team is finally learning something concrete about the relevant applications of these new tools, you sadly discover that the money you've spent and the "tools" you built or licensed have little or nothing to do with the actual operations of your particular company.

Most businesses won't need or find practical, cost-effective uses for actual AI tools for years to come, if ever.  Because their day-to-day information requirements and the characteristics and attributes of their products, services, markets and customers simply don't line up enough with the actual information outputs of these systems.

It's like asking for tax advice from a philosopher rather than an accountant. You might get an answer – and one that might be directionally correct – or, in the current ChatGPT world, one that might be completely made up.  But that advice is nothing that any sane person would rely on.

If you're really trying to solve certain problems in better and faster ways, including scheduling and routing, just-in-term supply chain projections, cost and damage estimates or pricing matrices, AI might be the wrong approach.

You don't need to spend the time and money training your people on new systems to build precise and iterative prompts and inquiries to interrogate huge, generalized data aggregations to get responses based on tons of information that have nothing to do with the specifics of your business; or the marketplaces, geographies and regulatory environments in which you operate.

It's overkill and very much like using a hammer to put out the flames when your hair is on fire. Painful, costly, and not particularly helpful.

On the other hand, spending your time and energies on creating analytical and heuristic systems that are practical, cost-effective, relatively rapid and readily accessible – and built from your own datasets, related third-party and adjacent others, shared and documented experiences and archived knowledge bases – is the smartest way to enter this new generative world.

Let's just say that the simple idea of machine learning is a lot more useful and understandable than all the gobbledygook about AI.

The premise couldn't be simpler: You don't need an oracle to predict next month's likely demand for specific products if you already have a decade of prior sales data, an experienced sales force, good info about your competitors' pricing strategies and a relatively stable marketplace in terms of regulation or other external factors.

Every decent sales organization, every restaurant and any smart entertainment venue has its own version of a "beat yesterday" book, which helps them look backwards and plan ahead. Most businesses have been doing their own variations of these kinds of inquiries for years in some combination of manual and mechanical approaches, so there's virtually no new training involved.

The trick is that any decent machine can manage, absorb, manipulate and display results, variations and projections for thousands of different products and scenarios at multiple price points, in minutes, far more accurately than even your best salespeople.

The critical change is that the speed and abilities of even the base level computing machines have grown exponentially while the costs of accessing and employing the processing power are now close to zero.

The use of machine learning isn't limited to applications like sales projections. Millions of other business interactions occur every day that are subject to known rules, processes, regulation and limitations that are also capable of being accumulated, archived, analyzed and converted to real-time tactical instructions and directions, to be played back to customer-facing team members in hundreds of different roles and positions.

Balto deploys one shining example of such a system, which equips customer service agents and their supervisors and managers with immediate, in-stream, customer history, transaction data, appropriate responses and escalation directions, all drawn from current interactions, company policies and historical experiences in similar cases.

If you have to have a name, I'd suggest that you more properly call this type of assistance "augmented intelligence" as long as you understand that it's the intelligence of the human end user that's being enhanced and extended rather than some novel output being created by a miraculous black box that is answering questions that no one needs to ask.

The improvements in customer engagement and in customer and CSR satisfaction and the gains in time, accuracy and productivity aren't the product of new discoveries. They're simply the result of better and more quickly equipping team members with the data and tools they need to do the best job possible in the shortest amount of time.

Slicing and dicing at scale isn't some new magic – it's a case where we're more focused on the power and value of such analysis and also have the ability to convert massive amounts of data into useful and actionable information.

Snapsheet creates claims management software for the insurance industry; its customers provide access to millions of interactions among claims adjusters, repair shops and consumers who may be insureds or claimants.

As unique as each and every crash may seem to the parties, the nature of the damage to the vehicles, costs of repairs and time required, are remarkably consistent when similar cars and circumstances are present.

In addition, the claim documentation and submission processes have also been streamlined and standardized by major insurers, which means that the vast majority of claims being processed on any given day for comparable vehicles are virtually identical.  Likewise, the entire repair process is fully understood.

And because virtually all the descriptive language regarding damaged parts and systems is also available in commonly employed digital designators, all claim submission material can be captured instantly by the entry systems and flowed directly into the analytical engines.

Here again, as Snapsheet continues to demonstrate, circumstances are ideal for the expanded application of machine learning.  By relying on millions of accumulated prior damage examples, the system can analyze, document and process claims as they are submitted and automatically create initial estimates without any human involvement.

These initial estimates can then be quickly reviewed by adjusters, edited or corrected where necessary and returned in real time to their insureds or claimants, along with directions and authorizations to the body shops to get the damaged vehicles repaired and returned to their owners quickly.

If this process seems remarkably mechanical and straightforward, that's because it is. There's no magic. There's no novel "intelligence" or native thought. There are constantly improving ways to use increasing computer power to manage massive amounts of data.

This is precisely the value and virtue of using computers and other machine learning tools to replace endlessly repetitive human actions, including data entry, with systems that can immediately assemble, process and evaluate materials to increase productivity, avoid entry and calculation errors, assure consistent legal and regulatory compliance, save time and improve everyone's satisfaction.

The most intelligent thing that any business owner can do today is to take stock of their own operations, determine which parts of the workflow have the essential attributes that can be optimized and enhanced through the application of machine learning and get started implementing those kinds of improvements.

There's nothing artificial about the results and savings you'll see in no time at all.